Doctorale Ecole

De Management Panthéon-Sorbonne

Laurent Salé

M Hervé Alexandre

M Alexis Collomb

M Jean

Paul Laurent

Liquidity in the banking sector THESE En vue de l'obtention du DOCTORAT ÈS SCIENCES DE GESTION Par

Keywords: Banks, Cash holding motives, GMM JEL Classifications: C3 -Econometric Methods: Multiple/Simultaneous Equation Models, C53 -Forecasting and Other Model Applications, G21 -Banks, Other Depository Institutions, Mortgages Liquidity creation, sensitivity analysis, GMM, Cox regression JEL Classifications: C3 -Econometric Methods: Multiple/Simultaneous Equation Models, C53 -Forecasting and Other Model Applications, G21 -Banks, Other Depository Institutions, Mortgages

n'entend donner aucune approbation ou improbation aux opinions émises dans les thèses. Ces opinions doivent être considérées comme propres à leurs

Finally, we have learned that for regulators to make accurate predictions requires a comprehensive picture of capital flows, liquidity and risks throughout the system. But coordination among regulators, which is so important, is enormously difficult in the current Préambule sur l'origine de la thèse : Un étonnement, une interrogation La présente thèse a pour origine l'étonnement de la crise financière de 2007/2008 et de son ampleur. En tant que cadre au sein d'une grande banque Française, j'ai en effet été témoin de la suite d'évènements qui a abouti à ce qui est reconnu comme la plus grave crise financière depuis 1929. Cette crise du système financier a largement remis en cause la vision selon laquelle les banques étaient solides et résilientes mais aussi l'hypothèse implicite que la liquidité est disponible à tout moment sur les marchés dans les économies modernes. Il faut rappeler que cette crise trouve ses racines dans la rupture de la traçabilité des risques des actifs, en particulier via les mécanismes de titrisation [START_REF] Gorton | Securitized banking and the run on repo[END_REF]). Les premières banqueroutes pour cause de manque de liquidité, en particulier celles de Northern Rock en 2007, de Bear Sterns en 2008 ainsi que des organismes américains de refinancement du crédit immobilier Fannie Mae et Freddie Mac, des prêteurs Countrywide et Indyman et de l'assureur AIG, ont contribué à montrer l'ampleur de cette opacification. L'arrêt brutal de la liquidité disponible sur les marchés et qui permet en temps normal le refinancement des banques à court terme et d'ajuster ainsi leur risque idiosyncratique a conduit à un scénario dont l'amplitude est comparable à celle de 1929. L'exemple le plus saillant est celui de la faillite de la banque Lehmann Brothers en 2008 qui détenait une dette de 619 milliards de dollar et employait 25 000 personnes, alors qu'en février 2007 cette banque avait un cours historique de 85,80$ et une capitalisation d'environ 60 milliards de dollars. L'impact systémique de ces faillites et sa contamination auprès des marchés a causé leurs blocages [START_REF] Iyer | Interbank liquidity crunch and the firm credit crunch: Evidence from the 2007-2009 crisis[END_REF] ; [START_REF] Acharya | Precautionary hoarding of liquidity and interbank markets: Evidence from the subprime crisis[END_REF]).

Introduction

As one determinant of a bank's survival during the financial crisis of 2007-2008, liquidity in the banking sector presents a challenge for the financial and academic communities and has recently become a central point of interest. The three articles presented in this thesis focus on the two main facets of liquidity in the banking sector: the holding of liquid assets (i.e., cash and assimilated resources) and the process of liquidity-creation in banks used to fund loans. As will be discussed in the articles, these two aspects of liquidity can be viewed as two sides of the same coin. I acknowledge that liquidity in banking is linked to the creation of money; however, this thesis focuses on the aforementioned two aspects of liquidity.

First, this section presents how ideas about liquidity in the banking sector have evolved in mainstream economic thought. Second, it considers the revival of cash-holding that has been observed since the financial crisis of 2007-2008. Third, it discusses the properties of liquidity. Fourth, it explores what we do not know about liquidity. Fifth, it identifies the fundamental issues analyzed in the three articles. Finally, it presents the methodology used in the articles to address these issues.

An overview of the evolution of concepts of liquidity

This section provides an overview of the evolution of mainstream economic thought regarding the status of liquidity. It first discusses the status of cash holdings and finishes with a consideration of the latest concepts of liquidity in the banking sector.

a) Prior to the neoclassical tradition

Neither mercantilism nor the classical economic thought of such theorists as Adam Smith (1723-1790), Thomas Malthus (1766-1831), David Ricardo (1772-1823) and Jean-Baptiste Say (1762Say ( -1832) ) attended to agents' motives for cash holding: money was primarily used in physical form, composed of different valuable metals. One of the important features of precious commodities in this era was that they served as a standard of value that was immune to various risks, such as inflation. The first attempt to conceptualize money dates to the classical period, during which money was considered to be a "simple vehicle of transaction" [START_REF] Say | traité d'économie politique: Ou simple exposition de la manière dont se forment, se distribuent et se consomment les richesses[END_REF]). In this view, cash-holding was driven mainly by transactional motives, intended to further an individual's objective of maximizing his own satisfaction while minimizing his effort. At the beginning of the 19 th century, the Quantity Theory of Money of [START_REF] Say | traité d'économie politique: Ou simple exposition de la manière dont se forment, se distribuent et se consomment les richesses[END_REF] was augmented by [START_REF] Fisher | The purchasing power of money[END_REF] famous equation. 2The neoclassical tradition, which emerged in the second half of the 19 th century with the works of Léon Walras (1834-1910), Wilfredo Pareto (1848-1923) and Alfred Marshall (1842Marshall ( -1924)), completed the classical view and provided a framework to explain economic phenomena at the individual level. As an alternative approach to the classical Quantity Theory of Money, the well-known Cambridge 3 equation shed new light on cash-holding, particularly through its conceptualization of the factor k, representing the amount of money that agents wish to hold. None of these equations, however, provided a comprehensive framework of the factors causing agents to hold cash.

b) Keynesianism and motives for holding cash

In response to comments on The General Theory of Employment, Interest and Money [START_REF] Keynes | The general theory of employment, interest, and money[END_REF]), John Maynard Keynes, in The general theory of employment [START_REF] Keynes | The general theory of employment[END_REF]), completed his hypothesis regarding the preference for liquidity, positing four motives for holding cash: transaction, precaution, speculation and financing. To these four motives, the author added a degree of agents' preference for liquidity, corresponding to the degree of agents' confidence in the development of the economy.

c) Monetarists and the reduction of banking regulation

The Chicago school of economics, and particularly the Monetarists led by M. Friedman (1912Friedman ( -2006)), observed inefficiencies in public policies, and in response, questioned the effects of government intervention [START_REF] Friedman | Free to choose: A personal statement[END_REF]). This school of economic thought demonstrated the complexity of implementing policies to stabilize the economy, leading them to argue for minimal governmental intervention [START_REF] Friedman | A monetary history of the united states, 1867-1960[END_REF]). Regarding financial stability, this view supported the deregulation of banking activity and was largely supported by the Federal Reserve until the financial crisis of 2007-2008. In his 2008 testimony before the House of Representatives (110 th Congress), Alan Greenspan 4 admitted to having put too much faith in the self-correcting power of the free market. In essence, his testimony argued for a unified framework of banking regulations needed to reduce market friction and minimize the biases generated by existing heterogeneous regulations. It also stressed the importance of better understanding liquidity in the banking sector, among other things. The Basel III regulatory framework has since been approved by the G20 countries. d) A revival of the concept of liquidity: The relationship between liquidity created by banks and their structural fragility

The idea of liquidity in the banking sector was revived with the works of [START_REF] Bryant | A model of reserves, bank runs, and deposit insurance[END_REF] and [START_REF] Diamond | Bank runs, deposit insurance, and liquidity[END_REF], which provided a framework for establishing the relationship between liquidity and the intrinsic fragility of banks. Liquidity is created when liquid liabilities (i.e., deposits) are transformed into illiquid assets (i.e., loans). Once this transformation occurs, however, a disconnection of maturities is also created: the expected inflows from loan reimbursementswhich are, by nature, scheduled -may be desynchronized with potential depositor outflows, which can occur at any time. Under stressed conditions, depositors may "run" to the banks and withdraw their deposits, generating massive bank runs.

This shortage of liquidity becomes the main trigger forcing banks into bankruptcy. Despite this issue, the creation of liquidity is the cornerstone of banking activity; however, it also accounts for their structural fragility (Diamond and Rajan (2001); [START_REF] Kashyap | Banks as liquidity providers: An explanation for the coexistence of lending and deposit-taking[END_REF]). The financial crisis of 2007-2008, which entailed serial bankruptcies of major banks, 4 Alan Greenspan, October 23rd 2008, testimony before the U.S. House of Representatives, 110 th Congress.

has validated this theoretical framework (Shin (2008); Goldsmith-Pinkham and Yorulmazer (2010)).

A change in the status of liquidity in the literature This section analyzes the evolution of the status of liquidity in the literature over the last decades, considering liquidity first in non-financial firms and second in banks.

a) Corporations

During the 1990s, a sharp increase in corporate cash-holding was observed. This unexpected increase challenged the academic community to account for the causes of this excessive stockpiling of cash [START_REF] Ferreira | Why do firms hold cash? Evidence from emu countries[END_REF]; [START_REF] Saddour | Pourquoi les entreprises françaises détiennent-elles de la trésorerie?[END_REF]; [START_REF] Foley | Why do firms hold so much cash? A tax-based explanation[END_REF]; [START_REF] Bates | Why do u.S. Firms hold so much more cash than they used to?[END_REF]); [START_REF] Frésard | The value of excess cash and corporate governance: Evidence from us cross-listings[END_REF]). In addition to these studies, which mainly focused on the motives for cash-holding, other authors examined whether cash-holding is adjusted over time and hence whether it is subject to optimality [START_REF] Venkiteshwaran | Partial adjustment toward optimal cash holding levels[END_REF]; [START_REF] Akbari | Deviation from optimal level of cash holdings and cumulative abnormal returns[END_REF]; [START_REF] Martínez-Sola | Corporate cash holding and firm value[END_REF]; [START_REF] Subramaniam | Firm structure and corporate cash holdings[END_REF]; [START_REF] Ozkan | Corporate cash holdings: An empirical investigation of uk companies[END_REF]), beyond which agency effects could be detected.

b) Banks

Following the work of [START_REF] Bernanke | Inside the black box: The credit channel of monetary policy transmission[END_REF], who established the importance of interest rate structure on loan creation, other authors have analyzed liquidity created by banks, proposing their own metrics. For example, [START_REF] Deep | Are banks liquidity transformers?[END_REF] propose the "LT Gap." [START_REF] Berger | Bank liquidity creation[END_REF] offer a three-step procedure for constructing measurements based on the category of product (CAT) or on their maturities (MAT), with offbalance-sheet information (FAT) or without (NONFAT). [START_REF] Fungáčová | Bank capital, liquidity creation and deposit insurance[END_REF] offer a measurement derived from the latter, and Brunnermeier, [START_REF] Brunnermeier | Liquidity mismatch measurement[END_REF] offer a method to measure the risks of illiquidity by taking into account its endogenous nature and the stochastic movements of the market.

Since the introduction of these metrics, other studies have been produced that measure liquidity-creation in different countries and periods or estimate the effects of certain factors, such as a new regulatory framework, on liquidity-creation. For instance, [START_REF] Berger | Bank liquidity creation[END_REF] studied the creation of liquidity in the US between 1993 and2003;[START_REF] Hackethal | Determinants of bank liquidity creation[END_REF] in Germany between 1997 and2006;[START_REF] Lakštutienė | Lithuanian banks liquidity creation in 2004[END_REF] in Lithuania from 2004Lithuania from to 2008;;[START_REF] Berger | Bank risk taking and liquidity creation following regulatory interventions and capital support[END_REF] in Germany between 1999 and2009;[START_REF] Fungáčová | Bank liquidity creation in russia[END_REF] in Russia between 1999 and[START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF][START_REF] Al-Khouri | Bank characteristics and liquidity transformation: The case of gcc banks[END_REF]) in GCC countries between 1998and 2008;[START_REF] Horváth | Bank capital and liquidity creation: Granger-causality evidence[END_REF] in the Czech Republic between 2000 and 2010; and [START_REF] Lei | Liquidity creation and bank capital structure in china[END_REF] in China between 1988 and2009. All of these studies, conducted in various countries at different periods, provide a mosaic of views on banking liquidity-creation, sometimes offering contradictory results [START_REF] Bancel | Focus on basel 3 and bank liquidity creation[END_REF]).

Fundamental properties of liquidity and definitions

Liquidity is an ambiguous concept that holds two fundamental properties: immediacy and transferability [START_REF] Diamond | Liquidity shortages and banking crises[END_REF]). From these properties, it is possible to define liquidity for each type of intermediation.

a) Financial intermediation

The liquidity of financial intermediation refers to the speed at which securities can be traded [START_REF] Myers | The paradox of liquidity[END_REF]). This is traditionally conducted through investment banking.

The speed of exchange ensures the fluidity of the market, enabling an optimal balance between the supply of capital and the demand for it. Moreover, this form of liquidity has an endogenous property caused by the behavior of agents: under stressed conditions, loss aversion tends to lead agents to strongly prefer avoiding losses over pursuing gains. Given the interactions between funding and market liquidity [START_REF] Brunnermeier | Market liquidity and funding liquidity[END_REF]) and also given capital and liquidity problems [START_REF] Allen | Financial intermediaries and markets[END_REF]), this aversion to the prospect of loss is multiplied; it can lead to a loop effect, in which agents sell their positions because they witness everyone else doing the same. The level of liquidity is measured by the spread between selling and buying prices as well as by the volume of transactions. A high spread indicates that the security is not liquid, which also contributes to reducing the volume of transactions.

b) Balance-sheet intermediation

The liquidity of balance-sheet intermediation refers to the transformation effected by the traditional banking model. Deposits (liquid liabilities) are transformed into loans (illiquid assets), the maturation of which varies from middle-to long-term. Although different measures have been created -as detailed in the second article presented in this thesisthe liquidity created can be (roughly) summarized as the difference between liquid liabilities and illiquid assets.

It should be noted that the functional separation of investment banking from traditional banking is, in the context of liquidity, porous. Traditionally, money is created when a bank grants a loan, under the condition that compulsory reserves are set aside in the central bank [START_REF] Bourva | Création de la monnaie et multiplicateur du crédit[END_REF]). In addition to this classical scheme, banks can sell or pledge securities that they hold to obtain cash to fund loans. Consequently, investment banking provides additional services to customers, such as guarantees and currency coverage, and directly supports the traditional banking business. As a result of the complexity of the relationship between investment banking and traditional banking businesses and to mitigate market friction, banking groups tend to develop their own internal capital markets [START_REF] Houston | Capital market frictions and the role of internal capital markets in banking[END_REF]; [START_REF] Campello | Internal capital markets in financial conglomerates: Evidence from small bank responses to monetary policy[END_REF]).

These different concepts of liquidity are related, however: as examined in the second article, liquid assets reduce the quantity of liquidity-creation performed in traditional banking.

While holding cash and assimilated resources improves the liquidity ratio, it also reduces resources that could be allocated to illiquid assets (i.e., loans), hence reducing liquidity creation. Overall, liquidity in the banking sector can take various forms that interact, ultimately requiring the bank manager to make complex trade-offs. The complexity of these interactions and constraints, added to the fact that illiquidity risk may contribute to the creation of an intense financial crisis, has attracted the scrutiny of the academic community.

What we do not know about liquidity

Although cash can be considered as a trivial question for practitioners, there remain theoretical aspects that are not fully understood. This section presents what we do not really know about liquidity. Because the list of what we do not know about liquidity could be neverending, I briefly discuss the most important issues that relate to liquidity in banking sector.

There are several facets of the liquidity that are not yet fully understood.

The first is how to value the liquidity service of cash. For instance, unlike holding Tbills, holding cash does not provide interest. Cash, however, is more liquid than T-bills.

Hence, it can be assumed that holding cash provides an additional service that offsets the loss of interest that the T-bills provide. Said differently and in a state of equilibrium, the marginal value of additional liquidity equals the interests of the T-bills. Theoretically, there are two possibilities for banks to hold cash and cover against the lack of synchronicity between inflow and outflows. The first is to finance this shortfall on the market. This "finance as you go" strategy assumes that cash is available at any point of time and thus implies that the markets are perfect or "agency-cost-free." The other way around is to hoard cash to reducing the moral hazard of market imperfections. For the latter case, it implies that holding cash beyond an optimal level is triggered by two potential causes. The first is strategic. Agents may anticipate a sudden recession and a dramatic fall of the capital markets, making it difficult to find cash.

By holding cash, non-distressed banks effectively force their competitors to sell assets at fire prices. Banks that anticipate a financial crisis and decide to accumulate cash may buy assets at fire prices and then hold a dominant position [START_REF] Acharya | Crisis resolution and bank liquidity[END_REF]). Thus, by building such a dominant position, cash holding can create value to a bank. In that sense, the additional value of liquidity is composed of a predatory motive that can be assimilated to an option or more precisely a call on the assets of competitors underlain by the freeze of the capital market. The second is agency. [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF] argues that in the context of poor investment opportunities, anchored managers are likely to keep cash instead of paying dividends out to shareholders. [START_REF] Harford | Corporate governance and firm cash holdings in the us[END_REF] document that managers tend to stockpile excess cash and spend it quickly. To sum up this aspect: holding cash beyond the minimum required to hedge against the idiosyncratic risk can be caused by predatory or agency motives. In both cases, the additional liquidity is valued more than the interest that could be provided if this cash were invested in T-bills. Holding liquidity for a strategic (or predatory) motive can finally be assimilated to an option. It poses the question of how to value this option: does it mean that the value of liquidity service of cash is countercyclical?

The second facet is a matter of degree that is composed of two aspects. The first is the degree of liquidity of the assets. Even if the T-bill is less liquid than cash, it is still more liquid than corporate bonds, which are more liquid than other types of assets, such as specialized machinery. In this view, an asset is liquid if the transaction cost for buying the asset is low. The second is the degree of market liquidity, which is commonly measured by the difference between the bid and ask, the market depth that corresponds to the volume of the transactions and the market resilience that indicates the speed at which the prices revert to their equilibrium level [START_REF] Bervas | Market liquidity and its incorporation into risk management[END_REF]). In this view, the market is liquid because three indicators indicate there is a quasi-absence of transaction costs on the capital market. To summarize this dimension, the transaction cost lies at the heart of liquidity. Even very illiquid assets, however, can be converted into cash if the holder accepts the need to keep this asset a certain amount of time and pay for the transaction cost that the selling of such illiquid asset implies. In this vein, holding an excess of cash can be viewed as a way to hedge against transactions costs. This poses the question of whether the excess of liquidity held corresponds to the sum of the transaction costs. Further, if there is an excess of cash, what is the optimal amount of cash for banks to hold?

The third dimension is the close relationship between liquidity and the value of an asset. In the liquidity-asset-pricing-model (LAPM), [START_REF] Holmström | Lapm: A liquidity-based asset pricing model[END_REF] have documented that the value of the assets is not only triggered by the consumer sector but also by the corporate demand for stores of values. As a driver of asset valuation, the liquidity constraint measures the level of confidence of the agents in the future and in market uncertainties. This liquidity-risk dimension poses the question of how close the relationship is between liquidity and confidence of the economy. This interrogation is particularly echoed by the endogenous property of liquidity. Under stressed conditions, liquidity shortfall acts as a loop that amplifies the negative effects of market falls. The prospect of loss generates the freeze of the liquidity, which contributes to the panic. Is there any limit to this endogenous effect beyond which the confidence of the market is reestablished? It finally poses the question of how the governments can mitigate the effect of the erosion of liquidity to stabilize the financial system.

The fourth dimension concerns the effect of public policy on liquidity. The close relationship between market liquidity and financial stability has obliged governments to impose various regulations on the financial sector. The first issue with regulation is to define a proper objective, and the second issue is whether adequacy between the goal and the measures is reached. There is a large strand of literature on the effect of regulation on liquidity, but fundamentally there are three views. The first contends that the regulation has a negative effect, or can create more harm than good. For example, and under this view, the lender-oflast-resort mechanism reduces the incentive for banks to self-insure against payment shocks, which weakens overall financial stability. The second view contends that the regulation has a positive effect and is thus necessary to stabilize the market and reduce the social cost that such instability generates. The third view contends that regulation is not a matter of thresholds that enforces minima or maxima or mechanisms that imply a governmental intervention.

Under this view, regulations and supervisory practices that force accurate information disclosure, empower private-sector corporate control of banks and foster incentives for private agents to exert corporate control work best to promote bank development, performance and stability [START_REF] Sharma | Evaluation of basel iii revision of quantitative standards for implementation of internal models for market risk[END_REF]). The blurred distinction between liquidity and solvability complicates the story. An insolvent bank can quickly become illiquid and an illiquid bank insolvent. Is the solvability the cause of the liquidity issue or is it the opposite? How exactly do they interact? The fact that the Basel III regulatory framework has enforced liquidity ratios within the banking sector tends to demonstrate that the solvability ratios enforced with the previous versions of Basel were not sufficient to stabilize the financial sector. It finally poses the question of what are the effects of the Basel III regulatory framework on the liquidity created by banks. Indeed, the fifth and final dimension is the liquidity created by banks. The central role of banks is to create liquidity [START_REF] Diamond | Bank runs, deposit insurance, and liquidity[END_REF]). Despite the existence of empirical studies on banking liquidity creation, there is no homogeneous data on banking liquidity creation over a long period of time and across several regions. The lack of such comprehensive observations reduces our vision on liquidity created by banks. How much has been created over a long period of time? Which regions have created most? Do European banks perform better than their peers from Asia or North America?

From these different facets, different definitions of liquidity can be established.

Liquidity can first be viewed as a measure at which an asset can be sold without significant transaction costs. Liquidity can also be regarded as a measure of the health of the financial sector. Additionally, the value of the liquidity of cash has a value can be assimilated to an option that is countercyclical. Liquidity creation as the central role of banking activity is also its structural weakness that obliges governments to regulate banks and thereby stabilize the financial sector. Consequently, liquidity in the banking sector is at the core of the complexity of the financial system where conflicting objectives cohabitate. As a central point of financial crisis, is the liquidity the cause, the effect or the symptom of the financial crisis? Finally, all of these interrogations are linked to one crucial point: the absence of a comprehensive theory on liquidity.

The next sections explain which issues this thesis explores and what methodologies have been used.

Liquidity in the banking sector: What is at stake? This section considers the most important questions at the center of the financial and academic communities' discussions of liquidity in the banking sector. I present first how the agency effects could potentially hamper the efficient transmission of monetary policy and then discuss the effects of regulation on liquidity. a) Determining the existence of agency effects that may hamper the efficient transmission of monetary policy.

Agents in the banking sector are bounded by a number of constraints, making it difficult to identify their preferences and therefore to optimize their utility. Additional effects, such as game theory [START_REF] Thakor | Game theory in finance[END_REF]) and market signaling theory [START_REF] Ross | The determination of financial structure: The incentive-signalling approach[END_REF]; [START_REF] Campbel | Information production, market signalling, and the theory of financial intermediation[END_REF]), complicate the story: the open market acts as a peer-to-peer surveillance system for competitors [START_REF] Mosebach | Market response to banks granting lines of credit[END_REF]), informing banks of the financial situations of their peers. Under conditions of stress, the banks may eventually use that information to force their competitors to sell assets at fire-sale prices, thus acquiring a dominant position [START_REF] Acharya | Crisis resolution and bank liquidity[END_REF]). The paradox of liquidity results from this complexity; deviations from value-maximizing are difficult to detect because of potential agency effects [START_REF] Myers | The paradox of liquidity[END_REF]). Overall, the financial crisis of 2007-2008 has shed light on the risks of illiquidity, with the systemic risk it implies. The crisis finally forced the academic community to question the nature of liquidity: are the fundamental properties of liquidity (transferability and immediacy) [START_REF] Diamond | Liquidity shortages and banking crises[END_REF]) completely understood? Given the bounded rationality of cash-holding, is the utility function of cash subject to optimality? If that is the case, are there agency effects [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF])? b) What are the effects of the regulatory framework on banking liquidity-creation?

The financial crisis has also challenged policymakers in addressing liquidity created by banks, which exists for the purpose of financing the economy. The inherent fragility of banks (Diamond and Rajan (2001)) forces authorities to impose a regulatory framework to reduce it. The ultimate objective of the regulatory framework is to mitigate systemic risk, which represents a social cost that governments -and by extension taxpayers -do not wish to face. Regulatory constraints may also generate negative effects, however, or even increase the probability of bank failures [START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF]. Because there is no consensus on whether regulation has positive (or negative) effects on the banking sector [START_REF] Bancel | Focus on basel 3 and bank liquidity creation[END_REF]), it is thus important to determine the effects of increasing levels of capital and banking regulations on liquidity created by banks. If liquidity creation is reduced by constraints (such as an increase of capital or liquidity ratios) that mobilize resources instead of using them to fund projects [START_REF] Alexandre | Impact of the 2008 crisis on credit rationing for french smes[END_REF], it may potentially obstruct the path to economic recovery and ultimately contribute to greater financial fragility. Consequently, is increasing capital the appropriate response? How does the new regulatory framework affect banking liquidity-creation? All of these questions place banking liquidity creation at the center of the debate on financial stability, and ultimately raise questions about the most appropriate method of regulation.

The three issues studied and the methodology used in this thesis

This thesis, composed of three articles, 5 uses an empirical approach to address some of the issues discussed above. In the article "Why do banks hold cash?", I examine bank cashholding over a long period (35 years) to determine if the liquidity held by banks is subject to agency issues [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF]). To achieve this aim, I use advanced econometric models to determine whether cash optimality exists in the banking sector. This question is of concrete importance: in the case that banks hold cash beyond this optimality, this idle money would not only represent an opportunity cost for the bank [START_REF] Baumol | The transactions demand for cash: An inventory theoretic approach[END_REF]; [START_REF] Whalen | A rationalization of the precautionary demand for cash[END_REF]) but also would not be used to fund the economy, hence representing an overall cost to society.

The second article, "Does an increase in capital negatively impact banking liquiditycreation?", explores liquidity created by banks in several countries over a period of 35 years for the purpose of determining which hypothesis prevails, the "crowding out" or the "risk absorption" hypothesis. It is the first study offering a unified vision of banking liquiditycreation over a long period of time and encompassing Asian & Pacific, European and North American banks. It uses advanced econometric models that enable us to identify the types of banks that create the most liquidity versus those that create less. Furthermore, these models allow us to estimate the effect of increasing levels of capital on liquidity created by banks. 5 Although unpublished, none of the articles have been desk rejected. They all have been subject to blind review and have received comments of reviewers from the following journals, respectively: the JFQA (Journal of Financial and Qualitative Analysis), the JBF (Journal of Banking and Finance) and the JOEF (Journal of Empirical Finance). In addition, the second article has been presented to the 33rd International Symposium on Money, Banking and Finance on 7-8 July 2016 at Clermont-Ferrand and the third article to the Labex Refi doctoral college on June 22 nd 2016. All the articles integrate the comments of the reviewers of the aforementioned journals or public presentations.

The new Basel III regulatory framework, however, is not only about the increase of capital. It is composed of several components, some of which aim to reduce risk in the banking sector. Considering all of these components, what effects will Basel III have on banking liquidity-creation? To address this issue, the third article, "Positive effects of Basel III on banking liquidity creation", uses advanced econometric models, such as the "D-in-D,"6 life table and semi-parametric regressions, to estimate these effects.

The three articles presented in this thesis will contribute to increasing our understanding of how specific banking factors affect the transmission of monetary policy and the efficiency of regulatory constraints.

Overall, this thesis is composed of the following three axes of research:

 The first axis of my research examines the two main facets of liquidity. The first is cash and assimilated, the main purpose of which is to hedge against the risk of illiquidity. This aspect is explored in article 1, "Why do banks hold cash?". The second aspect relates to liquidity creation, which is the primary purpose of banks. This aspect of liquidity is studied in article 2, "Does an increase in capital negatively impact banking liquidity creation?", which studies banking liquidity creation over a long period of 35 years and across several countries.

 The second axis of my research explores the effects of regulation in the banking sector. For instance, the third article estimates the effects of Basel III on banking liquidity creation.

 The third axis of my research is based on empirical observations that may challenge existing theories. Each article in this thesis uses datasets and advanced econometric tools to elaborate robust estimates and provide solid evidence, i.e., GMM, semi-parametric regressions, survival functions, sensitivity analysis and D-in-D.

Introduction

Cash has a value that is more complex than what is commonly accepted: as part of its liquidity, cash has properties of transferability and immediacy [START_REF] Diamond | Liquidity shortages and banking crises[END_REF] ) that can not only generate wealth in specific situations but also annihilate value because of the related agency costs or tendency of banks to stockpile cash during stressful market conditions. This dilemma has attracted the scrutiny of nonbanking firm researchers and, more recently, those studying banks. Moreover, the recent global financial crisis has demonstrated that cash holding is a major determinant in banks' chances of survival; for example, the Bear Stearns collapse in 2008 publicly revealed that banks at the time were unable to fulfill liquidity stress test requirements and that both secured and unsecured funding could vanish very quickly.

The classical theoretical view is that an optimal point exists beyond which holding cash generates an opportunity cost [START_REF] Baumol | The transactions demand for cash: An inventory theoretic approach[END_REF]; [START_REF] Whalen | A rationalization of the precautionary demand for cash[END_REF]). Hence, if agents act rationally, they should not stockpile cash, meaning that cash should be available at any point in time in the market. However, this paper documents a secular increase in bank cash holding over a 35-year period and examines the reasons why banks have to hold cash if they can meet the financial demands thrown at them by whatever economic conditions they might face.

Overall, why is such an examination important? First, it examines whether an optimal level of cash holding financing structure exists. If the existence (or otherwise) of cash optimality can be determined, any excess of cash can be established, facilitating an assessment of the existence of agency problems [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF]). Second, it is important to understand the factors that influence banks' cash holding decisions. Indeed, current studies document that precautionary holdings of cash substantially increased during the recent financial crises. Thus, banks' lending to households and corporations exhibited substantial stagnation. During times in which banks show extreme precautionary hoarding of liquidity, central banks' transmission mechanisms of monetary policy could ultimately diminish their effectiveness. This paper thus aims to improve our understanding of how banks decide about their cash holdings in an international context and thus to contribute to a better transmission mechanism of monetary policy.

With regards to banking sector, causes of cash holding have recently drawn the attention of the academic community. For instance, [START_REF] Acharya | A model of liquidity hoarding and term premia in inter-bank markets[END_REF] explore the relation between cash holding and counterparty risk by modelling three states ranging from "normal" to "market with liquidity hoarding" and by addressing causes of cash holding in the context of financial crises. Moreover, [START_REF] Acharya | Precautionary hoarding of liquidity and interbank markets: Evidence from the subprime crisis[END_REF] show that a regime shift occurred in 2007 that forced banks to hold cash mainly for precautionary measures, which in turn generated spillover effects. These studies show that an increase in counterparty risk was the main reason explaining banks' cash holding and that stressful conditions create a favorable environment that encourages banks to hold important buffers [START_REF] Ashcraft | Precautionary reserves and the interbank market[END_REF]). This significant increase in aggregate liquidity aims to ensure that banks have the ability to face unpredictable payment shocks; however, the authors' data requirements restrict their sample size to limited periods (2007)(2008)(2009), rendering generalization of their results difficult. In addition, [START_REF] Ashcraft | Precautionary reserves and the interbank market[END_REF] acknowledge that reasons other than precautionary motives could play a role in cash holding.

Hence, what motivates banks to hold cash, and how persistent are these motives over a long period of time?

To fill this research gap, the present article first explores the determinants of bank cash holding, and following [START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF], I present variables in this respect. The results reveal that the regulatory and institutional framework, bank size, government monetary policy and the macroeconomic environment exert a significant influence over cash holding by banks. Additionally, for specific cases, the spread that measures the difference between interbank loans over 3 months and T-bills over 3 months and that proxies for interbank liquidity shocks also plays a role in banks' cash holding. The findings also do not support the substitutability hypothesis regarding the substitutive relation between cash holding and leverage ratios. Second, I test for the existence of cash optimality. For robustness considerations, I follow [START_REF] Ozkan | Corporate cash holdings: An empirical investigation of uk companies[END_REF] and adopt the generalized moment of method (GMM) approach, which accounts for the dynamic behavior of cash optimality. I find evidence that the dynamic nature of cash optimality is rejected in the banking industry. These findings contrast with nonbanking sector where the dynamic nature of cash optimality is observed [START_REF] Ozkan | Corporate cash holdings: An empirical investigation of uk companies[END_REF]; Venkiteshwaran (2011); [START_REF] Subramaniam | Firm structure and corporate cash holdings[END_REF].

By defining variables tailored to the banking industry and by using data from a large panel of 943 listed banks, this paper documents the evolution of cash holding in the banking sector over a 35-year period. This cross-country research helps to identify the main driving forces that induce each type/category of bank to hold cash. Considering the particular risk that major banks created during the liquidity crisis, I separate banks based on their asset size. The paper is organized as follows. The first part of the paper analyses the related literature on the causes and implications of cash holding in the banking sector by focusing on bank cash holding as part of banking liquidity. The second part describes the methodology and data used in this research and presents the evolution of cash holding by Asian and Pacific, European and North American listed banks over a 35-year period. The third part presents the main results of the regression and GMM models, and the conclusion sums up the findings.

Theoretical considerations and econometrical proxies

This section reviews the literature on bank cash holding and its determinants.

Why do banks hold cash? Although a large strand of empirical research on corporate cash holding [START_REF] Kim | The determinants of corporate liquidity: Theory and evidence[END_REF][START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF]; [START_REF] Bates | Why do u.S. Firms hold so much more cash than they used to?[END_REF]) that validates trade-off theory exists, research on the determinants of cash holding in the banking sector is relatively recent, and studies in this area have focused ostensibly on specific periods, such as the financial crisis [START_REF] Acharya | A model of liquidity hoarding and term premia in inter-bank markets[END_REF]; [START_REF] Acharya | Precautionary hoarding of liquidity and interbank markets: Evidence from the subprime crisis[END_REF]; [START_REF] Ashcraft | Precautionary reserves and the interbank market[END_REF]) and cash flow risks. On the one hand, trade-off theory argues for debt optimality. This is mainly supported by the trade-off between benefits and costs of debt [START_REF] Scott | A theory of optimal capital structure[END_REF]) and by trade-off between tax benefits with expected distress costs or personal tax costs [START_REF] Miller | Debt and taxes[END_REF]). Similarly to debt, cash holding, produces costs and benefits. The two main costs associated with cash holding depend on whether managers' interests are in line with shareholders' interests: if managers maximize shareholder profits, the cost is a lower return relative to another investment with a similar risk profile; if, however, managers do not maximize shareholder profits, they tend to increase the size of assets under their control to broaden their managerial discretion [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF]. The only benefit to holding cash is therefore to obtain a safety buffer, which helps to finance growth opportunities while avoiding increases in external funds or the liquidation of existing assets. Given the trade-off between costs and benefits, there is no rationale for holding cash beyond this optimal cash point, apart from rationale related to agency. On the other hand, the POH of [START_REF] Myers | Corporate financing and investment decisions when firms have information that investors do not have[END_REF] posits that no optimal debt level exists. Because increases in financing costs are positively correlated with information asymmetry, issuing new equities is costly; hence, firms finance their projects by following a preference hierarchy that is (1) using internal funds, (2) indebting and (3) issuing new equity. Under this view, cash holding results from financing and investment decisions, and it is utilized as a buffer between retained earnings and investment needs.

But including capital markets frictions and financial constraints complicates the story. The market frictions that are caused by taxes, transaction costs and information asymmetries (between lenders and borrowers) can make external funding sources more expensive than internal funding source. From these market imperfections external funding sources is not equally available or in favorable terms to all. This shortage of internal sources of funding causes constraints on investments and oblige banking group to develop internal capital market [START_REF] Houston | Capital market frictions and the role of internal capital markets in banking[END_REF]). If a bank identifies activity sector with growth opportunities that would rise its value and finds itself being short of resources to finance these investments, it may loses some of these investment opportunities. Consequently, the level of internal funding is potentially an important determinant of growth opportunities.

What are the possible motives for banks to hold cash?

The first motive is precautionary, whereby a bank aim to ensure that its level of cash holding complies with the level of risk that it assumes. As part of this motive, the transactional motive is included, where the high volume of transactions within payment systems and cash management complexity are also sources of additional uncertainty [START_REF] Armakola | Ccp resilience and clearing membership[END_REF]. Both transactional and precautionary motives concern the cost of illiquidity; thus, banks are likely to retain extra cash as a guarantee to ensure smooth payment capabilities against cash inand outflow shock waves, also known as "idiosyncratic risk". In this vein, banking groups establish internal capital markets. As shown by [START_REF] Cetorelli | Liquidity management of us global banks: Internal capital markets in the great recession[END_REF], the internal capital market within a banking group is a function of bank size and the distance between the headquarters and its subsidiaries and branches. By establishing an internal capital market, banks aim to reduce the cost of external financing and of idiosyncratic risk within their banking group, especially during periods of interbank liquidity shocks. What proxy can best measure a bank's cash flow risk? In this paper, bank risk is measured by using the approach of [START_REF] Laeven | Bank governance, regulation and risk taking[END_REF], who use the ROA through the z-score (as originally proposed by [START_REF] Roy | Safety first and the holding of assets[END_REF]). One disadvantage in using equity volatility as a measure of risk reduces the sample of 50 banks in average per year. To account for that risk and for robustness reason, I also included the non-performing loans banking variable that indicates an increase of the credit risk and accumulation of the bank's reserves.

The second motive is regulatory compliance, as banking regulatory frameworks oblige banks to hold cash for liquidity ratio requirements. As [START_REF] Berger | Bank risk taking and liquidity creation following regulatory interventions and capital support[END_REF] argues, risk taking is affected by the applicable supervisory regime.

Moreover, as highlighted by [START_REF] Acharya | Imperfect competition in the interbank market for liquidity as a rationale for central banking[END_REF], differences in the institutional framework between central banks may have implications for bank reserve balances because such institutional differences affect the interbank market competition and, by extension, banks' cash holdings. To control for the differences, I have built a supervisory index similar to that used by [START_REF] Shehzad | The impact of bank ownership concentration on impaired loans and capital adequacy[END_REF], which is composed as follows: Before the deployment of the Basel regulatory framework (1997), I use the data from [START_REF] Porta | Legal determinants of external finance[END_REF], who find a positive relationship between capital market development and legal enforcement. A grade is provided for the level of property rights protection from 0 to 4 corresponding to not protective to most protective, respectively. After the deployment of the Basel regulatory framework (1998), I use the data of [START_REF] Barth | The regulation and supervision of banks around the world[END_REF]; [START_REF] Barth | Bank regulation and supervision: What works best?[END_REF]; [START_REF] Barth | Bank regulations are changing: For better or worse ?[END_REF]; [START_REF] Barth | Bank regulation and supervision in 180 countries from 1999 to 2011[END_REF], which made it possible to measure the effect of the legal environment and institutional differences on liquidity requirements per period. I have computed these data and averaged the capital stringency as well as capital activity and restrictiveness per period and per country. Tight regulations and scrutiny from regulatory agencies oblige banks to reduce their risk exposure and to thus manage their liquidity needs more efficiently. Because a decrease in risk implies a reduction in the liquidity ratio, the relation between cash holding and the legal environment is expected to be negative.

The third motive is signaling. In the context of non-cooperative players, such as those modelled by [START_REF] Thakor | Game theory in finance[END_REF], cash holding can be viewed as a signal. During downturn periods, banks in severe need of cash submit unusually high refinancing rates to the central bank, thus revealing the need to fund their liquidity risk. The interbank market, which plays a role in liquidity insurance during normal periods, is then totally frozen because no bank is willing to lend to other banks. As a result, an open market with a central bank is the only place to find cash, and this market adjudication procedure provides information on counterparty risk [START_REF] Acharya | Precautionary hoarding of liquidity and interbank markets: Evidence from the subprime crisis[END_REF]). The open market also acts as a peer scrutinizer for competitors [START_REF] Mosebach | Market response to banks granting lines of credit[END_REF]), whereby non-cooperative players are informed of the financial situation of their competitors. This observation is reinforced by market signaling theory [START_REF] Campbel | Information production, market signalling, and the theory of financial intermediation[END_REF]), according to which insiders in a bank have information to which the market does not have access. What metric could proxy this motive? [START_REF] Ohlson | Earnings, book values, and dividends in equity valuation[END_REF] conclude that dividends paid out provide information to the market that is then reflected in the market price of stocks. If, by generating income that serves to establish the dividend paid to shareholders, a bank gives a positive signal to the market about its financial health, the bank is thus viewed as less risky and is more likely to have better access to external finance. In this paper, the net income (before extraordinary items) that is used to calculate earnings per share is used as a proxy to measure the signaling motive. The measure is scaled over assets, and the relationship between this variable and cash holding is expected to be negative.

The fourth motive is strategic, and it has two dimensions. The first dimension involves cash holding to avoid value destruction. Tensions in financial markets can be amplified and can lead to a major financial crisis when liquidity fades away. Diamond and Rajan (2001) establish that liquidity is a major element of value destruction, while [START_REF] Carlson | Arresting banking panics: Federal reserve liquidity provision and the forgotten panic of 1929[END_REF] demonstrate that the liquidity problem exacerbates solvency issues, thereby generating massive bank runs. This shortage of liquidity was the main trigger that forced the British bank Northern Rock into bankruptcy and that led to widespread banking panic. Such a shortage was repeated on a larger scale in 2008, during the collapse of Lehman Brothers, AIG, Freddie Mac and Fannie Mae. The second dimension is a predatory motive. By holding cash, nondistressed banks effectively force their competitors to sell assets at fire prices. Banks that anticipate a banking crisis and that decide to accumulate cash may buy assets at fire prices and then hold a dominant position [START_REF] Acharya | Crisis resolution and bank liquidity[END_REF]). Thus, by building a dominant position, cash holding can create value for a bank. Two hypotheses model the funding deficit and substitutability between debt, equity and internal funding sources. The first hypothesis is the POH from [START_REF] Myers | Corporate financing and investment decisions when firms have information that investors do not have[END_REF], which models the funding deficit and substitutability between debt, equity and internal funding sources. The second hypothesis is the trade-off theory, which implies that an optimal debt ratio exists. In both cases, the leverage ratio is the metric used to determine this substitutability effect. Following [START_REF] Bates | Why do u.S. Firms hold so much more cash than they used to?[END_REF] and [START_REF] Graham | The theory and practice of corporate finance: Evidence from the field[END_REF], this paper uses the commonly accepted leverage ratio, that is, the debt over equity ratio, to measure a bank's preference for indebtedness and its strategic motive. In trade-off theory, the substitutability between cash and debt implies a negative relationship. However, if the relationship is positive or if the sign is not constant over time, trade-off theory must be rejected. Because banks are financially constrained, the substitutability between various sources of financing is more sensitive when growth opportunities arise. These growth opportunities are proxied by two metrics: first, the loan growth that represents a bank's ability to invest, in line with [START_REF] Caprio | Governance and bank valuation[END_REF], and second, the market-to-book ratio, whereby a low ratio indicates weak growth opportunities [START_REF] Tobin | A general equilibrium approach to monetary theory[END_REF]).

The fifth motive is agency. [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF] argues that if poor investment opportunities exist, anchored managers are likely to keep cash instead of paying dividends out to shareholders. [START_REF] Harford | Corporate governance and firm cash holdings in the us[END_REF], for instance, document that managers tend to stockpile excess cash and then spend it quickly. Because entrenched managers tend to avoid paying dividends and to spend cash quickly, the dividend paid to shareholders over the shareholders' equity ratio is the proxy that measures this motive in this paper. This research follows [START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF], who document that the expected relations between dividends paid and cash holding is expected to be positive. A negative and significant coefficient can reveal that banks engage in profitable investment opportunities, which in turn diminishes cash holding and thereby engender a negative relationship.

What other factors might influence cash holding behavior?

First, the size of the bank may influence cash holding behavior because, depending on their size, banks behave differently; for instance, larger banks are more likely to have a high volume of transactions and thus a higher risk of in-and outflow cash mismatches.

Hence, a bank's size affects its precautionary motive. Additionally, as documented by [START_REF] Houston | Capital market frictions and the role of internal capital markets in banking[END_REF], larger banks are more likely than smaller banks to develop internal capital markets. Finally, as argued by [START_REF] Myers | The paradox of liquidity[END_REF], the more liquid assets a bank hold, the more likely it will face agency costs. Demirgüç-Kunt and Huizinga (2011) consider the log of total assets to be an acceptable proxy for bank size. In this paper, the relationship between bank size and cash holding is expected to be positive, in that larger banks are expected to be more likely to hold cash than smaller banks. Furthermore, three types of banks are defined: small banks (when an asset size lower than $US 50 billion), medium banks (with an asset size between $US 50 billion and $US 250 billion), and large banks (with an asset size higher than $US 250 billion).

Second, government monetary policy may influence cash holding behavior.

The close relationship between liquidity creation and monetary policy has been established by [START_REF] Bernanke | Inside the black box: The credit channel of monetary policy transmission[END_REF], in that a relatively low government-set interest rate encourages credit supply, which ultimately positively increases bank liquidity capabilities. A positive relationship between government monetary policy and cash holding is expected whereby banks will tend to hold more government bonds when government bond rates are high. These bonds are liquid and are hence equated to cash.

Finally, the macroeconomic environment may influence cash holding behavior, as cash holding may also depend on where the bank is located and its economic perspectives. GDP growth measures the economic growth rate of a bank's locale. In addition, access to the interbank market might be subject to liquidity shocks, which could play an important role in banks' cash holding decisions. For instance, during the recent financial crisis, interbank markets froze, and banks increased their precautionary cash holdings once they were cut from interbank markets. To control for the "interbank liquidity shock effect", I follow [START_REF] Cornett | Liquidity risk management and credit supply in the financial crisis[END_REF] and use the TED spread indicator. A positive relation between this indicator and cash holding is expected whereby a higher spread indicates a greater cash stockpile.

Data and descriptive statistics

This section begins with a short description of the data sources, and the next section undertakes a descriptive analysis of bank cash holding. Because the panel data set is composed uniquely of listed banks, it is dominated by US banks that represent 60% of the number of banks toward the end of the sample period. However, 95 % of the US banks have a small size of less than $10 bln and represent around 15% of the sum of assets of all US banks (figures not displayed). Besides, the coverage of the initial year is rather limited to 60 banks in 1981. This coverage is much improved in 2000 with 687 banks. As a result, the results are broken down per geographical zones (Asia & Pacific, Europe and North America) per period and types bank (small, medium and large).

Following [START_REF] Berger | Financial crises and bank liquidity creation[END_REF], I include data from consolidated financial statement of mother companies, which includes the financial statement of branches and subsidiaries but eliminates intragroup activities. By excluding subsidiaries and branches, I avoid double counting the cash hold by banks that belong to the same group, which would have inflated the results otherwise. But this exclusion does not obstruct the results because the panel is sufficiently representative. As can be observed in the table 2 below, the panel dataset indeed covers a substantial fraction of Asia & Pacific, European and US market.

Table 1 shows a breakdown of the number of banks by geographical zone, country and year.

Insert Table 1.

To provide a better view on the fraction of the market covered by the sample used in this study, table 2 compares the total amount of deposits of loans of the database vs. that of each country for the year 2011 (except for Canada, for which the year is 2013). Data were extracted from local or regional bankers associations, when available. Overall, the sample in this study covers 55% and 54% of the total deposits and loans, respectively, of the banks in Asian, European and North American countries. 2.

Insert Table

Evolution of cash holding per geographical zone and type of bank

This section documents the evolution of first the cash ratio and second of the aggregated cash holding in amount in the banking sector.

Cash ratio

This section shows the evolution of cash holding in the banking sector, documenting the evolution of the cash to net asset ratio and of the aggregated cash holding of banks per geographical zone. This dissociation per geographical zone is justified by the potential effect of differences in accounting rules and bank size against the cash ratio. As shown by a study initiated by the ISDA in 20127 on height major banks, the magnitude of the difference between US GAAP and IFRS accounting netting rules substantially affects the amount of total assets, thereby generating bias in comparisons of these institutions. Banks using US GAAP net more than their peers using IFRS. Because of this netting effect, the cash ratio is mechanically higher for banks using IFRS than for those using US GAAP.

The table below compares the evolution of cash and assimilates the evolution of cash over assets, cash paid out over equity and leverage (total debts over common equities) ratios per geographical zone, bank type and period. The type of bank corresponds to banks' asset size, whereby small banks are those with total assets below US $50bln, medium-sized banks are those with total assets between US $50bln and US $250bln and large banks are those with total assets equating to greater than US $250bln. The four periods correspond to the Basel regulatory framework deployment: before Basel I (from 1981to 1988), Basel I (between 1989and 2007), Basel II (between 2008and 2010) and Basel III (from 2011 to 2014). In addition to the Basel periods, I have established ten-year periods (1981-1990, 1991-2000, 2001-2010, and 2010-2014). Because the banks are financially constrained and heavily regulated, it intuitively justifies a breakdown of the Basel periods. To control for the potential implications of regulatory changes on banks' cash holding decisions, OLS and GMM models are computed on both the Basel and the ten-year periods.

Insert Tables 3 and4.

Between regions and periods, the cash and leverage ratios show different trends that do not provide strong evidence supporting the substitutability between cash holding and leverage. The medians follow the same trends as the average terms presented below, but they are not reported here for brevity. For small banks (columns 2-13), between 1981 and 2013, the cash ratio increased from 30% to 39% for Asian and Pacific banks, whereas it decreased from 35% to 26% and from 41% to 19% for European and North American banks, respectively. Moreover, the leverage ratio decreased from 115% to 89%, from 1,761% to 343% and from 168% to 85% for Asian and Pacific, European and North American banks, respectively. For medium-sized banks (columns 14-25), the cash ratio remained stable at approximately 38% and 25% for Asian and Pacific and North American banks, respectively, whereas it increased from 26% to 34% for European banks. In addition, the leverage ratio decreased from 1,559% to 160% and from 1,680% to 431% for Asian and Pacific and European banks, while it varied between 119% and 256% for North American banks. For large banks (columns 26-37), the cash ratio remained relatively stable at approximately 39% for Asian and Pacific banks, whereas it varied from 41% to 46% and from 35% to 40% for European and North American banks, respectively. Further, the leverage ratio decreased from 562% to 368%, from 1,093% to 725% and from 327% to 187% for Asian and Pacific, European and North American banks, respectively. The paid out shareholders' equity ratio varied between +0% and +6% for all types of banks, with higher ratios for North American banks.

Aggregated cash hol ding s

This section documents the evolution of cash holding in the amount counter valued in US$ billions based on the IMF foreign exchange rate at the end of each year. Given that the bank size effect relates to several motives, the banks are divided into three types: small, medium and large banks. The type of bank corresponds to the asset size, where small banks are those with total assets below US $50bln, medium banks are those with total assets between US $50bln and US $250bln and large banks are those with total assets greater than US $250blblnn.

Insert Table 5.

The observations demonstrate that the evolution of cash holding is heterogeneous and that European banks tend to stockpile more cash than their peers, confirming the necessity to better understand the motives for banks to hold cash. However, two types of causes for cash holding explain the observed differences: structural and conjectural causes. The conjectural causes can be linked to the conjectural circumstances that oblige banks to increase their cash holdings to cover against the cash flow risk. For instance, in 2005, the European Constitutional Treaty was rejected in France after a referendum. In addition, the oil commodity price rose by 40% for the first nine months of 2015. This generated significant inflation, which in turn obliged the FED to increase its funding rate.

Consequently, the foreign exchange rate of €/US$ in Europe changed from 0.73 to 0.84 at the end of 2014 and 2015, respectively. Coupled with a significant variation in the rate €/US$, this referendum rejection obliged French banks to increase substantially their cash holdings from US $0.7bln to US $1.9bn between 2004 and 2005. The structural causes of this heterogeneity are further discussed in the regression section, which shows the significance of cash holding motives.

For all regions aggregated (columns 26-27), cash holdings increased steadily from US $104bln in 1981 to US $3 304bln in 2002. The amount of cash holdings then increased from US $4 399bln to US $14 071bln between 2003 and 2012, and in 2013, this figure decreased to US $11 255bln. For Asian and Pacific (columns 2-9) small banks, the amount of cash holdings between 1981 and 1998 increased progressively from US $17bln to US $146bln. Then, in 1999, it decreased sharply to US $79bln. Between 2000 and 2013, cash holdings increased steadily from US $191bln to US $442bln, and finally, in 2014, small banks aggregated US $284bln. For medium banks, the amount of cash holdings between 1982 and 1990 increased from US $6bln to US $162bln, and between 1991 and 1993, it decreased from US $135bln to US $68bln. In 1994, the amount of increased to US $229bln, and from 1995 to 2000, it varied between US $158bln and US $ 107bln on the cusp of the Asian crisis. Between 2001 and 2011, the amount of then increased from US $123bln to US $715bln, and it started to decrease sharply between 2012 and 2014 from US $675bln to US $375bln. For large banks, the amount of cash holdings between 1991 and 1993 increased from US $32bln to US $123bln, but it decreased sharply to US $53bln in 1994. Between 1995 and 1998, the amount of cash holdings varied hieratically between US $99bln and US $172bln. In 1999, at the edge of the Asian financial crisis, it fell to less than US $0.1bln. Between 2000 and 2012, the amount of cash holdings then increased from US $113bln to US $2,996bln, and between 2012 and 2013, it decreased from US $2,996bln to US $2,315bln. For all European banks (columns 16-17), between 1981 and 2007, the amount of cash holdings increased from US $59bln to US $9,597bln, andbetween 2008 and2014, it decreased from US $8,874bln to US $5,131bln.

For all North American banks (columns 24-25), between 1981 and 2012, the amount of cash holdings increased from US $28bln to US $2,046bln, and between 2012 and 2013, it decreased from US $1,979bln to US $1,901bln. In the next section, which analyses the motives for cash holding by banks, I analyze the structural causes of these differences.

Methodology and empirical results

This section presents the model specification, the results of the regression model that computes the determinants of bank cash holding and of the GMM estimate that analyses cash holding from a dynamic perspective.

Variable definition and model specification

From the literature review, an exhaustive list of explicative variables was established, in which the cash-to-assets ratio is the variable explained in detail. Different alternatives to the cash ratio, such as cash to assets, cash to net assets [START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF]) or the log of cash to net assets [START_REF] Foley | Why do firms hold so much cash? A tax-based explanation[END_REF]), are proposed by the literature. In this sample, one independent variable is used: cash and the assimilated ratio over assets, which comprises treasury securities, trading account securities and cash due from banks. This definition is conceptually similar to that of liquid assets (see [START_REF] Berger | Bank liquidity creation[END_REF]).

The model is constructed as follows. I use the dependent variable cash holding assets, and the explanatory variables are (1) the standard deviation of ROA, a proxy for the bank's precautionary motive [START_REF] Laeven | Bank governance, regulation and risk taking[END_REF]); (2) the loan growth and market-to-book ratios, proxies for the bank's growth perspectives [START_REF] Caprio | Governance and bank valuation[END_REF] and [START_REF] Tobin | A general equilibrium approach to monetary theory[END_REF]) ; (3) the legal and institutional environment in which the bank is located, a proxy for regulatory compliance and the capital/liquidity stringency motive [START_REF] Porta | Legal determinants of external finance[END_REF] and [START_REF] Barth | Bank regulation and supervision: What works best?[END_REF]; (4) net income over assets, a proxy for the bank's signaling motive [START_REF] Ohlson | Earnings, book values, and dividends in equity valuation[END_REF]); (5) the leverage ratio, a proxy for the bank's strategic motive [START_REF] Bates | Why do u.S. Firms hold so much more cash than they used to?[END_REF]); and (6) the dividend paid out over the equity ratio, a proxy for the bank's agency motive [START_REF] Jensen | Theory of the firm: Managerial behavior, agency costs and ownership structure[END_REF]). Moreover, five additional variables suggested by the literature review are added to the model: The first three are considered independent, as they may substantially affect the dependent variable, while the last three variables are control variables: (i) bank size, as small banks may behave differently from large banks; (ii) the 10-year government bond per country in which the bank is located, a proxy for government monetary policy; (iii) GDP growth in the country in which the bank is located, a proxy for the economic environment [START_REF] Beck | Bank concentration, competition, and crises: First results[END_REF]); (iv) the TED in which the bank is located, a proxy for interbank liquidity shocks [START_REF] Cornett | Liquidity risk management and credit supply in the financial crisis[END_REF]); (v) Jensen (1968) excess returns, a proxy for the risk profile of the bank; and (vi) nonperforming loans over the total loans ratio, a proxy for the bank's risk management efficiency [START_REF] Acharya | Should banks be diversified? Evidence from individual bank loan portfolios[END_REF]). Consequently, the model can be notated as follows: Equation 1:

Cash Holding i = β0 + β1 Standard deviation of ROA i + β2 Legal environment j + β3
Net income over asset ratio i + β4 Leverage ratio i + β5 Loan growth i + β6 Market-to-book ratio i + β7 Dividend paid out over equity i + β8 Bank size i + β9 Government 10-year bond rate j + β10 GDP Growth j + β11 Control1_TED j + β12 Control2_Excess return i,j + β13 Control3_Nonperforming Loans over total loans ratio i + µ i Tables 6 and 7 below list the explained explicative and control variables that are used in the regression model.

Insert Tables 6 and7 This chapter examines banks' cash holding motives based on robust [START_REF] Massart | Least median of squares: A robust method for outlier and model error detection in regression and calibration[END_REF]) regression models that control for heteroscedasticity. Based on observations noted in the descriptive statistics, the following combination of test elements were established systematically for each model presented in order to ensure the solidity of the regression models: endogeneity, omitted variables, colinearity and size effect. For endogeneity, the control variables test the nonperforming loans ratio, the excess returns and the TED. By including [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF] excess returns in the control variables, I avoid endogenous biases linked to strategic effects or unobserved factors. For omitted variables, a time-fixed effect test is computed to check average variances in cash holding across years that would not be taken into account by the former exogenous variables and to lessen threats related to serial correlation. They results show p-values below 0.001, with Eta² or Omega² below 0.3, while the results of Student t-tests show p-values below 0.001.

Regression results

The table below displays the results of the regression model for all banks per geographical zone and period (Basel and ten-year) to control for the effect of the deployment of the Basel regulatory framework on banks' cash holding motives.

Insert Tables 8 to 15.

The findings reveal significant heterogeneity in relation to banks' cash holding motives. This disparate nature of motives suggests that cash holding is sensitive to various parameters that depend on the environment in which a bank is located. Below, the standardized coefficients are presented to scale the cash holding motives and thus enable a comparison between heterogeneous regions.

The following observations can be drawn. Mainly composed of developed countries with lower starting point, on economic point of view, the banks in APAC have enjoyed a stronger economic growth than in Europe or North America over the last decades. Also, the regulatory framework in APAC region has been less constraining as shown by [START_REF] Barth | The regulation and supervision of banks around the world[END_REF]. Over the period studied, the banks in US have taken advantage of a common currency and more homogenous fiscal and regulatory framework than their European peers. In addition, the legal environment in the US favors the business risk takers.

Consequently the US banks have benefited of a stable regulatory environment, even if the US has been hurt by different economic downturns. In contrast, the European banks had to face a lower economic growth, fiscal instability, heterogeneous regulatory frameworks and monetary policies. These differences in Europe have started to be reduced, with the implementation of the common euro currency. The necessity for a greater convergence has been largely discussed, in particular since the last 5 years and after the budget crisis of 2010 that was mainly triggered by Greece. The recent vote for Brexit in UK may accelerate this global convergence, enabling a more stable environment for the banking system in Europe. This difference of environments explains why the motives for holding cash and assimilated liquid assets differ between regions.

For Asian and Pacific banks (table 10, column 4), the GDP growth, interbank liquidity shock, precautionary and signaling motives explain Asian and Pacific banks' cash holding behavior, with standardized coefficients of 0.25***, 0.21***, 0.18*** and 0.16***, respectively. These motives vary over time, as can be observed in columns 5 to 7, which show the unstandardized coefficient per Basel period, or in columns 8 to 11 (table 10), which show the unstandardized coefficient per ten-year period. The leverage ratio, which tests the substitutability between cash holding and external funding, is relatively low and nonsignificant, and the sign changes over the different periods, suggesting that the trade-off theory does not apply for these types of banks.

For European banks (table 12, column 4), the bank size, precautionary motive, interbank liquidity shock and regulatory compliance motives explain European banks' cash holding behavior, with standardized coefficients of 0.40***, 0.27***, 0.16***, and -0.11***, respectively. The negative sign of the regulatory motive is expected. The significance of bank size and interbank liquidity shocks remain relatively stable over the various periods presented (table 12, columns 5 to 11); however, the regulatory compliant motive is less significant after the Basel I period, suggesting that the regulatory and institutional changes marginally affected the cash holding decisions of the banks after the deployment of Basel I.

For North American banks (table 14, column 4), the 10-year bond yield, bank size, strategic and regulatory compliance motives explain North American banks' cash holding behavior, with standardized coefficients of 0.32***, 0.17***, -0.12***, and -0.10***, respectively. The findings indicate that the leverage ratio has a significant predictive value but that this predictive standardized coefficient (not reported in the tables for brevity) tends to decrease over time, with values of -0.18***, -0.09** and -0.07*** for the Basel I, Basel II, and Basel III periods, respectively. The negative sign in table 5 "Summary of variables -part 1" is expected.

Overall, the findings confirm that the leverage ratio does not have a significant predictive value, has a varying sign, or has a predictive value that decreases over time, which does not lend support to the substitutability hypothesis regarding the substitutive relation between cash and debt. Thus, the trade-off hypothesis may not apply in the banking sector. To determine precisely whether the trade-off theory has to be rejected, I have established a dynamic and optimal cash holding model.

Dynamic model estimates with GMM

This section analyses cash holding from a dynamic perspective. If an optimal level of cash holding indeed exists, this level could differ over time and across banks.

Therefore, an appropriate estimation technique will enable lags in adjustments, as well as systematic changes in the determinants of optimal cash levels. The GMM 8 estimation, developed by [START_REF] Arellano | Some tests of specification for panel data: Monte carlo evidence and an application to employment equations[END_REF], helps to estimate unbiased partial adjustments and makes it possible to control not only for endogeneity in bank-specific variables but also for the dynamic nature of cash holding. To build a model consistent with the trade-off hypothesis, which implies that banks have an optimal level of cash holding, I follow the protocols 8 Usually attributed to [START_REF] Hansen | Large sample properties of generalized method of moments estimators[END_REF], Large sample properties of generalized method of moments estimators, Econometrica 1029-1054., the GMM is the most appropriate tool for controlling unobserved effects. First, the cash holding at time t-1 may have an autoregressive effect. Second, Hansen showed that every instrumental variables estimator, in nonlinear or linear models, with cross-section, panel data or time series, could be conceived as a GMM estimator. Considered as a unifying framework for inference in econometrics, this estimation has the advantage of being robust to the distribution of errors, and it is considered more efficient than 2SLS [START_REF] Hall | Generalized method of moments[END_REF]. Generalized method of moments (Oxford University Press Oxford).. Most commonly tool used when there is endogeneity threat between the dependent variables, the GMM instruments them with a lagged dependent variable. In this article, the independent variables act as control variables, with regards to liquidity created at t+1. Also, at lag 2, the GMM coefficient measures the degree of mean reversion and thus (1-λ) is interpreted as the coefficient of persistence of the explicative variable Yt-1 for each bank. Refer to proof of evidence in art. 1 "Why do banks hold cash?", equation 4 on how to interpret the lag

2 coefficient 1-λ.
elaborated by [START_REF] Ozkan | Corporate cash holdings: An empirical investigation of uk companies[END_REF] and [START_REF] Venkiteshwaran | Partial adjustment toward optimal cash holding levels[END_REF]. Because this level might vary over time and between banks, the optimal cash ratio obtained is: Equation 2Suppose the unobserved target cash ratio: Cash* i,t Cash* i,t = Σ k βX i,t + µ i,t Where  Cash is the cash ratio (cash over total assets)  Banks = i:from 1 to n  Time = t, from 1 to t  Xi,t is the vector of bank-specific determinants.

 β is the estimated coefficient vector, and it corresponds to the propensity to revert to cash holding. Banks adjust their current cash ratio to reach the target ratio. It ultimately forces the banks to establish a partial adjustment mechanism. This standard partial adjustment model for cash holding can be written, where cash optimal* -cash (actual) = excess of cash.

Equation 3

Cash* i,t -Cash i,t-1 = λ (Cash* i,t -Cash i,t-1 )
where coefficient λ varies between 0 and 1 and captures the fraction of the gap between the target and the actual level of cash, said differently the ability for banks to adjust to their target ratio.



If λ is close to 1: It implies that the banks are able to adjust immediately and thus there is no adjustment costs, i.e., Cash* i,t = Cash i,t;  If λ is close to 0: It means that banks are not able to adjust immediately thei cash structure because the adjustment costs are too large, i.e., Cash i,t = Cash i,t-1 If Equation 2 is substituted into equation 1 and the terms are rearranged, the following equation is obtained:

Equation 4 Cash* i,t = (λβ) X i,t + (1-λ) Cash i,t-1 + δ i,t
where  (λβ) Xi,t is the target cash ratio toward which the bank's cash ratio converges.

 1-λ is interpreted as the mean reverting property of cash holding for a first order autoregressive model of change in cash position for each bank.

It results that the model that tests for dynamic cash holding becomes:

Equation 5 Cash ratio i,t = β13 Cash ratio i,t-1 + β0 t + β1 Standard deviation of ROA i,t + β2 Legal environment j,t + β3 Net income over asset ratio i,t + β4 Leverage ratio i,t + β5 Loan growth i,t
+ β6 Market-to-book ratio i,t + β7 Dividend paid out over equity i,t + β8 Bank size i,t + β9 Government 10-year bond rate j,t + β10 GDP Growth j,t + β11 Control1_TED t j + + β12 Control2_Nonperforming Loans over total loans ratio i,t + µ i,t

The tables below report the results of the estimation. The results of Sargan & Hansen's J tests indicate that the instruments utilized in the GMM estimation do not correlate with the error terms and that there are no overidentifying restrictions.

Insert Table 16.

Table 16, which reports the coefficient of the propensity to revert to cash holding (1-λ) per geographical zone and bank size, confirms that the dynamic nature of the cash holding model in the banking sector can be rejected. For the Asian and Pacific, European and North American zones, the coefficients are 0.07 (1-0.93***), 0.28 (1-0.716***) and 0.13

(1-0.871***), respectively. For small, medium and large banks, the coefficients are 0.37 (1-0.631***), 0.03 (1-0.968***) and 0.02 (1-0.978***), respectively.

Overall, these findings provide strong evidence that the dynamic nature of the optimal cash model in the banking sector is rejected.

Conclusions

In this paper, I examined the hypothesis that cash optimality exists in the banking sector. To determine the excess of the cash coefficient, I first identified the determinants of cash holding. In contrast to previous studies, this paper explored the empirical determinants of cash holding for a large sample of listed banks and over a long period of 35 years. I did not find any evidence that might support a substitutive relationship between cash holding and the leverage ratio. Second, I integrated the dynamic nature of cash holding over time to test whether banks adjust their level of cash to an optimal level. By using the GMM protocol, I could establish this optimal cash level per bank while controlling for endogeneity and measurement errors. Finally, the findings provide evidence that the dynamic nature of the optimal cash model in the banking sector is rejected, in contrast to the nonbanking sector [START_REF] Ozkan | Corporate cash holdings: An empirical investigation of uk companies[END_REF] ; Venkiteshwaran (2011) Akbari, Rahmani, Ahmadi andShababi (2014)); Subramaniam, Tang, Yue andZhou (2011)). This paper yields several important features that provide additional insights into not only what causes a bank to hold significant amounts of cash but also how banks differ from corporations in regard to cash management.

Additionally, the Basel III regulatory framework-particularly with respect to liquidity ratios-may oblige banks to adapt their financing models and, by extension, may change the relationship between cash holding and debt levels. Further research on the impact of Basel III on liquidity creation would be useful in this respect. Table 1: Breakdown of the number of banks per geographical zone and country

Tables

Table 1 shows the number of banks included in this research, per main geographical zone and per country. The sample includes Thomson Reuter's bank year observations from 1981 to 2013. Banks in Thomson Reuters correspond to listed companies, and they are classified as such by the ICB. The first column indicates the region and then the country, while the second column shows the number of banks per year that belong to these countries. The years 1981The years , 1985The years , 1990The years , 1995The years and 2000 and then and then 2 compares the total deposits and loans of the sample of banks used in this study vs. the total deposits and loans reported by local regional banker associations and summed per geographical zones. The first column indicates the region. The first and second columns display the number of banks used in this study for 2011 and 2013, respectively. The second and third columns sum the deposits and loans of the banks from the current bank sample. The fifth and sixth columns display the total deposits and loans of all banking sectors. These data were extracted from local or banker associations for the year 2011, except for Canada (year 2013). The last two columns show the percentage of the sample used in this study over the total of deposits and loans aggregated per geographical zone, respectively. Table 3 : Compared evolution of the cash and paid out over equity and leverage ratiosper geographical zone, type of bank and period -Small and Medium banks

Region

This table shows the evolution of the cash and assimilated, paid out over the equity and leverage (total debt over equity) ratios in average terms. The medians follow the same trend, but they are not reported for the sake of brevity. The first column shows the periods corresponding to the Basel regulatory framework deployment: before Basel I (from 1981to 1988), Basel I (between 1989and 2007), Basel II (between 2008and 2010) and Basel III (from 2011 to date). Columns 2 to 13 display for small banks and per geographical zone (Asia and Pacific, Europe and North America) the following information: the number of observations, the cash ratio, the paid out ratio and the leverage ratio. This order is duplicated for medium banks (columns 14 to 25) and large banks (columns 26 to 37). The type of bank corresponds to the asset size, where small banks are those with total assets below US $50bln, medium banks are those with total assets between US $50bln and US $250bln and large banks are those with total assets greater than US $250bln. Table 4 : Compared evolution of the cash and paid out over equity and leverage ratiosper geographical zone, type of bank and period -Large banks

This table shows the evolution of the cash and assimilated, paid out over the equity and leverage (total debt over equity) ratios in average terms. The medians follow the same trend, but they are not reported for the sake of brevity. The first column shows the periods corresponding to the Basel regulatory framework deployment: before Basel I (from 1981to 1988), Basel I (between 1989and 2007), Basel II (between 2008and 2010) and Basel III (from 2011 to date). Columns 2 to 13 display for small banks and per geographical zone (Asia and Pacific, Europe and North America) the following information: the number of observations, the cash ratio, the paid out ratio and the leverage ratio. This order is duplicated for medium banks (columns 14 to 25) and large banks (columns 26 to 37). The type of bank corresponds to the asset size, where small banks are those with total assets below US $50bln, medium banks are those with total assets between US $50bln and US $250bln and large banks are those with total assets greater than US $250bln. This table shows the evolution of aggregated cash holding. The first column shows the reporting year, the second the number of banks, and the third the sum of cash and assimilated. This order is replicated per geographical zone (i.e., Asia and Pacific, Europe and North America) and type of bank (i.e., small, medium and large bank). The type of bank corresponds to the asset size, where small banks are those with total assets below US $50bln, medium banks are those with total assets between US $50bln and US $250bln and large banks are those with total assets greater than US $250bln. Cash and assimilated are defined in Table 4's "Summary of variables". Amounts are expressed in billions of USD, based on the IMF foreign exchange rate of each end of year. This table sums up the discussion above and shows the exhaustive list of variables used in this research. The first column indicates the motive, the second the variable name, the third the corresponding code used in the regression models, the fourth whether the variable has been scaled under a Siegel-Tukey test, the fifth the predictive sign and the sixth how the variable has been computed (Thomson Reuters' code). To measure the effect of the legal environment and institutional differences on liquidity creation, an index has been established as follow :

N

N $ Bln N $ Bln N $ Bln N $ Bln N $ Bln N $ Bln N $ Bln N $ Bln N $ Bln N $ Bln N $ Bln N $ Bln N $
(1) before Basel I : I start first from La [START_REF] Porta | Legal determinants of external finance[END_REF] who established legal zones to measure the effect of the legal environment on the development of the capital market. To measure the effect of the legal environment on liquidity creation, a similar mapping is utilized in this paper, where a grade is provided to the level of property right protection from 0 to 4 corresponding from not protective to most protective, respectively.

(2) from Basel I to II, (3) from Basel II to III, and (4) since Basel III : The regulatory environment mapping of banks is elaborated from [START_REF] Barth | Bank regulations are changing: For better or worse ?[END_REF]; [START_REF] Barth | The regulation and supervision of banks around the world[END_REF] for the three last periods corresponding respectively to Basel regulatory frameworks deployments. It is a summary that averages the capital stringency as well as capital activity and restrictiveness per periods.

Signaling

(α) = R(a) -R(f) -Beta(a,m) [R(m) -R(f)] ; beta is the commonly indicator [Cov (a, m) / Variance (m)],
where R(a) is the return of market share, R(f) is the return of benchmark government bonds, R(m) is the return of market index m where is listed the bank. Var(x) and Cov (x, y) require at least three observations. Covariances and variances are rolling and computed every year (step of maximum 5 years). Market price = Market value per share (item WC05001).

Control variable 3 NPLoLoans X13NploL NA -Non Performing loans over loans is the control variable that proxies the risk management. NPLoLoans = Non performing loans (item WC02285) / Loans total (item WC02271)

Omitted variables Set of dummies for all but one year. Per bank and country in which the bank is located. NA + / -Fixed effects : Bank i and country j , where the bank is located 1998[START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF], Basel II = 2008-2010, and Basel III = 2011-2014). Columns 7-10 show the results per ten-year period (1981-1990, 1991-2000, 2001-2010, and 2011-2014).

Columns 11 and 12 show the fixed-effects results, where bank and country are the variables that control for unobserved heterogeneity. Robustness tests include endogeneity with instrumented variables and omitted variables with fixed effects. All the results show F values greater than 10, with P(F) less than 0.0001 and P(t) less than 0.0001. (1981-1990, 1991-2000, 2001-2010, and 2011-2014). Columns 12 and 13 show the fixed-effects results, where bank and country are the variables that control for unobserved heterogeneity. Robustness tests include endogeneity with instrumented variables and omitted variables with fixed effects. They all show F values greater than 10, with P(F) less than 0.0001 and P(t) less than 0.0001. (1981-1990, 1991-2000, 2001-2010, and 2011-2014). Columns 12 and 13 show the fixed-effects results, where bank and country are the variables that control for unobserved heterogeneity. Robustness tests include endogeneity with instrumented variables and omitted variables with fixed effects. They all show F values greater than 10, with P(F) less than 0.0001 and P(t) less than 0.0001. This table presents the results of the regression models for North American banks per Basel and calendar period. Column 1 shows the cash holding motives, and column 2 the corresponding independent variables. The next columns show the estimated coefficients with their level of confidence. T-tests are reported in parentheses. The symbols ***, ** and * indicate significance levels at 0.1%, 1% and 5%, respectively. Columns 8 to 11 show the results per ten-year period (1981-1990, 1991-2000, 2001-2010, and 2011-2014). Columns 12 and 13 show the fixed-effects results, where bank and country are the variables that control for unobserved heterogeneity. Robustness tests include endogeneity with instrumented variables and omitted variables with fixed effects. They all show F values greater than 10, with P(F) less than 0.0001 and P(t) less than 0.0001. Table 16: GMM estimation of dynamic cash holdingpropensity to revert to optimal cash compared per region and bank size

This table reports the results of the GMM estimation [START_REF] Arellano | Some tests of specification for panel data: Monte carlo evidence and an application to employment equations[END_REF]) computed to estimate the coefficients for optimal cash and the assimilated over an asset ratio of t. The dependent variables correspond to those in table 4. The z statistics are presented in parentheses below the coefficient estimates. Sargan J-stats [P(Chi²)] appear in the last lines per model. The symbols ***, ** and * indicate significance levels at 0.1%, 1%, and 5%, respectively. Column 1 displays the motives, column 2 the corresponding variables and column 3 the coefficient name. Column 4 shows the coefficient estimates for all banks. Columns 5 to 6 show the coefficient estimates per geographical zones that correspond to Asia and Pacific, Europe and North America. Columns 8 to 9 show the coefficient estimates for bank size, which corresponds to banks' asset size, where small banks are those with total assets below US $50bln, small banks are those with total assets between US $50bln and US $250bln and large banks are those with total assets greater than US $250bln.

Introduction

Throughout modern finance theory, and according to [START_REF] Diamond | Bank runs, deposit insurance, and liquidity[END_REF] in particular, there has been a consensus that the primary purpose of banks is to create liquidity.

Two main facets of banks represent their inherent characteristics, but these two elements can also be a source of weakness: By transforming liquid liabilities into illiquid assets, banks expose themselves to liquidity mismatching, which in turn results in structural fragility.

Nonetheless, do banks create liquidity and if so, what types of banks achieve this goal? In addition, how has this pattern of liquidity creation evolved over time? Finally, do any increases in equity have a positive effect overall? Although liquidity creation is acknowledged as the primary goal of banks, paradoxically, only a few empirical studies have attempted to address such questions, and these have only focused on limited periods and countries. The first attempt in this respect, by [START_REF] Deep | Are banks liquidity transformers?[END_REF], is based on a panel of 200 US commercial banks over a period of four years (Q2 1997 to Q2 2001). Their work presents clues to the determinants of liquidity creation and concludes that credit risk inhibits the liquidity creation process. The second study, by [START_REF] Berger | Bank liquidity creation[END_REF], which examines a panel of 9,095 US banks over 11 years (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003), shows that bank size plays a positive role in the process. A third work focuses on German banks over a period of 10 years (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) and attempts to determine the impact of banking regulation on liquidity creation [START_REF] Berger | Bank risk taking and liquidity creation following regulatory interventions and capital support[END_REF]).

Several studies have examined banking liquidity creation in other countries, such as [START_REF] Lei | Liquidity creation and bank capital structure in china[END_REF] While these works shed valuable light on banking liquidity creation, they still perform analyses of specific countries and for limited periods (typically over ten years).

However, a review of the literature review indicates that no empirical research has undertaken a unified and large cross-country analysis of liquidity creation over a period of more than 10 years. Given the potential effects of exogenous variablessuch as economic recession and monetary policyon bank liquidity creation, an examination of a shorter period of less than 10 years and of only one country may influence a study's determinants substantially.

To address this gap, I examine liquidity creation over a period of 35 years and across the following geographic zones: Asia-Pacific, Europe, and North America. Additionally, I attempt to determine whether capital increases positively affect liquidity creation.

To address this question, this paper is organized as follows. The first section reviews the literature on liquidity creation, and the second section shows how the various methodologies employed to compute liquidity creation levels have been adapted to this dataset. A dataset of 940 listed banks extracted from Thomson Reuters has been established

that gathers financial and market data from 29 countries over more than three decades. These data have been supplemented with macroeconomic data from the Bank of International Settlement and from the International Monetary Fund.

The results demonstrate that the evolution of liquidity creation is somehow heterogeneous across the examined regions. The last section of this paper is composed of three parts as follows. First, a model for identifying determinants and liquidity creation processes is presented. To address issues of endogeneity, I employ a multivariate dynamic panel model using a GMM (Generalized Method of Moment) estimator as established by [START_REF] Arellano | Another look at the instrumental variable estimation of error-components models[END_REF]. In addition, OLS with a fixed effects estimator is presented to ensure that it is not affected by omitted variables or other biases that would otherwise weaken it. Second, a Cox regression [START_REF] Cox | Regression models and life-tables[END_REF]) is used to establish a hazard model, which makes it possible to determine a bank's propensity to create liquidity. This makes it possible to construct a profile of banks that create more liquidity than others based on inherent characteristics such as size, risk level and equity level. Finally, a sensitivity analysis is conducted to extend the core results and to estimate the marginal effect of an equity increase on liquidity creation and risk management performance. General implications are presented for policymakers in the conclusion.

Literature review on liquidity creation

This section reviews the literature on bank liquidity creation and its determinants.

Litterature review

Although [START_REF] Smith | An inquiry into the nature and causes of the wealth of nations[END_REF] was the first to emphasize the role of banks in financing the economy, the mainstream literature on bank liquidity creation dates back to the 1980s with the work of [START_REF] Diamond | Bank runs, deposit insurance, and liquidity[END_REF]. Indeed, the model developed by the authors provides the most comprehensive theoretical explanation regarding why it is important for banks to hold a mix of illiquid loans and liquid assets. In addition, it helps practitioners to understand that banks create liquidity by funding illiquid assets, such as loans with liquid liabilities, such as deposits. It also shows that the fragile capital structures of banks are caused precisely by this liquidity mismatch. Moreover, this liquidity creation process cannot be disassociated from the risk transformation process, whereby banks transform risk by issuing riskless deposits to fund risky loans [START_REF] Ramakrishnan | Information reliability and a theory of financial intermediation[END_REF]; [START_REF] Boyd | Financial intermediary-coalitions[END_REF]).

By issuing short-term deposits while lending against a medium-or long-term horizon, banks expose themselves to interest rate fluctuations and counterparty defaults on payments. This transformation, coupled with risk management (Diamond and Rajan (2001 )), is thus the defining characteristic of banks.

Thus, do equity level increases contribute to the creation or destruction of liquidity?

Two opposing views address this subject; the first, "risk absorption hypothesis" [START_REF] Berger | Bank liquidity creation[END_REF]), contends that equity levels have a moderator or buffer effect on risk. With increases in the probability of defaults arising as loans increase [START_REF] Allen | The theory of financial intermediation[END_REF]), increases in equity levels enable banks to absorb risks [START_REF] Repullo | Liquidity, risk-taking and the lender of last resort[END_REF]; [START_REF] Coval | Financial intermediation as a beliefs-bridge between optimists and pessimists[END_REF]). In this respect, this risk-buffer effect favors liquidity creation. In other words, an increase in equity levels reduces risk, making it possible to increase bank loan capacities and thus liquidity creation. The second view, "financial fragility-crowding out hypothesis" [START_REF] Berger | Bank liquidity creation[END_REF]), contends that a bank's level of equity contributes to the destruction of liquidity, i.e., as deposits are immediately available, they offer more insurance for short-term investors than illiquid equities [START_REF] Gorton | Liquidity provision, bank capital, and the macroeconomy[END_REF]). As a result, and because equities are illiquid, capital providers are more reluctant to fund banks (Diamond and Rajan (2001); [START_REF] Diamond | A theory of bank capital[END_REF]); thus, this view assumes that liquidity is a form of indirect insurance for depositors and that equilibrium is to be found by investors in short-term (deposits) and medium-to long-term (equity) investments. If equities are preferred, it is to the detriment of deposits, which mechanically decreases the capacity of banks to create liquidity.

In essence, there is an arbitrage effect between the preference for illiquid liabilities (equities)

and liquid liabilities (deposits) within a context of limited resources.

Other factors may contribute to liquidity creation. According to [START_REF] Kashyap | Banks as liquidity providers: An explanation for the coexistence of lending and deposit-taking[END_REF], both sides of a bank's assets cannot be dissociated due to their mutual synergies.

These synergies between deposits and loan commitments create a form of liquidity provision that contributes to liquidity creation [START_REF] Kashyap | Banks as liquidity providers: An explanation for the coexistence of lending and deposit-taking[END_REF]. In addition, supervisory regimes and regulatory stringency may also affect whether banks decide to consider risk [START_REF] Berger | Bank risk taking and liquidity creation following regulatory interventions and capital support[END_REF].

Two approaches to measuring liquidity creation have been identified. The first, proposed by [START_REF] Deep | Are banks liquidity transformers?[END_REF], defines liquidity as a gap between liquid liabilities and liquid assets, i.e., it shows the amount of transformed liquidity over total assets. The second approach, created by [START_REF] Berger | Bank liquidity creation[END_REF], proposes a protocol that makes it possible to construct a comprehensive measure of liquidity. It essentially dissociates liquid assets/liabilities from illiquid assets/liabilities and establishes a three-step protocol for computing liquidity creation, which can be summarized as follows: (1) classify asset liabilities and off-balance sheet items with a certain degree of liquidity based on two types of criteria (category or maturity), (2) assign to them three types of weight (+0.5; 0 or -0.5) and

(3) combine these two steps. The use of weights is justified by the "dollar-for-dollar adding up constraints" argument, which is as follows. Assume that a bank holds $1 in liquid liabilities, such as deposit claims, and that it uses this $1 to finance a loan, which is an illiquid asset. The weighted protocol ensures that liquidity creation is indeed $1, where half of the $1 of liquid liability + half of the $1 of illiquid asset = $1 of created liquidity. In effect, $1 is created when the bank transforms $1 of a deposit into a $1 loan. Similar reasoning is applied to cases of liquidity destruction.

An additional measure presented by Fungáčová, Weill and Zhou ( 2010) is a gross measure that has the advantage of providing an overall understanding of liquidity creation. By applying assumptions that are less strict than other measures, it does not use detailed categories or maturity classifications of different balance sheets items. On the assets side, total loans are assumed to be illiquid, and current account and securities investments are assumed to be liquid. On the liabilities side, total deposits are treated as liquid liabilities and equity is treated as illiquid liabilities.

Another measure of liquidity exists, that is the Liquidity Mismatch Index [START_REF] Brunnermeier | Liquidity mismatch measurement[END_REF]. Because this index measures the difference between the "asset price in times of crisis relative to the maturity structure of the liabilities", it takes into account the fact that liquidity of assets is endogenous: there is indeed an interaction between funding and market liquidity [START_REF] Brunnermeier | Market liquidity and funding liquidity[END_REF]) and also between capital and liquidity problems [START_REF] Allen | Financial intermediaries and markets[END_REF]. This interaction can generate a loop effect when the financial sector runs into liquidity problem, leading to further asset price reduction, which in turns worsens the liquidity of the markets. This index thus measures the risk for banks to be short of liquidity in adverse scenario, whereas the liquidity measures of [START_REF] Deep | Are banks liquidity transformers?[END_REF], [START_REF] Berger | Financial crises and bank liquidity creation[END_REF] and Fungáčová, Weill and Zhou (2010) measure the liquidity created by banks.

Determinants of liquidity creation

Which elements determine liquidity creation? Different variables may interact within liquidity creation processes. Based on the literature review, the effect of bank size and the relationship between a bank's role as a risk transformer and liquidity creator were identified as the elements that are most likely to substantially interfere with liquidity creation.

Additional parameters, such as the level of equity, monetary policy and the legal environment, are also explored. The determinants can be categorized into two sets of indicators: specific and environmental bank factors.

Specific bank factors include the follow ing.

Four bank specific factors have been identified: (1) bank size, (2) level of risk, (3) level of equity and (4) bank performance.

Bank size: The relation between bank size and liquidity creation is due to several factors. For instance, Houston, James and Marcus (1997) demonstrate that capital market imperfections oblige banking group to put in place internal capital markets. Consequently, bigger banks reduce their transaction cost, which enable them to optimize the allocation of funding sources among their branches and subsidiaries. Given the empirical evidence presented by [START_REF] Kashyap | Banks as liquidity providers: An explanation for the coexistence of lending and deposit-taking[END_REF] that highlighted the synergy between deposit taking and lending, as well as the relationship between bank size and transaction deposits, the size of a bank may have a significant effect on liquidity creation. Following Demirgüç-Kunt and Huizinga (2011), the log of total assets is a proxy for the size of a bank.

Level of equity: The role of equity levels in liquidity creation is documented by [START_REF] Gorton | Liquidity provision, bank capital, and the macroeconomy[END_REF] and Diamond and Rajan (2001), who establish that higher capital levels diminish liquidity creation as they negate deposit collection. [START_REF] Berger | Bank liquidity creation[END_REF] elaborate two hypotheses to offer explanation for the substitution relationship between liquidity creation and the level of capital. The first posits that given the limited access to resources, if level of equity increases it is at the detriment of liquid assets and thus reduces the liquidity creation. The second posits that increase of capital, reduces the risk, which provides more resources allocated to loans, thus augments the liquidity creation. In this paper, the equity variable is scaled over the assets. I acknowledge that the bank capital structure plays an important role in the risk management [START_REF] Laeven | Bank governance, regulation and risk taking[END_REF]). Also, the bank capital structure reflects not only its own characteristics, but also the environment in which it operates (De Jonghe and Öztekin ( 2015)). Because bank capital structure is not available in Thomson Reuters database, the level of risk is used to take into consideration the effect of bank capital structure on risk management (see below). In addition, I have also accounted for the exogenous factors that may influence the capital of banks in the section "Environmental factors".

Level of risk: The role of banks as risk transformers is examined widely in the literature. According to [START_REF] Diamond | Financial intermediation and delegated monitoring[END_REF] and [START_REF] Ramakrishnan | Information reliability and a theory of financial intermediation[END_REF], banks finance risky loans by issuing riskless deposits. However, the strength of the relationship between liquidity creation and risk transformation has not been explored fully, and there is no evidence that they vary in perfect parallel to one another. Reliance on performance indicators such as RoE are a key incentive to excessive risk-taking in banks, as shown by [START_REF] Moussu | Roe in banks: Myth and reality[END_REF]. In this paper, bank risk is measured using approach, namely, the standard deviation of RoE. For robustness and following [START_REF] Imbierowicz | The relationship between liquidity risk and credit risk in banks[END_REF], I add the credit risk level typically calculated by non-performing loans over loans and bank stability risk or z-scores (as originally proposed by [START_REF] Roy | Safety first and the holding of assets[END_REF]).

Bank performance: Following Hackethal, Rauch, Steffen and Tyrell (2010), I include a set of similar indicators, such as operating income (before taxes and exceptional profits or losses), but also commonly used indicators to proxy bank performance, such as dividends paid over equity, loan growth, Jensen's excess return (as originally proposed by Jensen (1968)) and the market price of the bank shares. property rights protection (0 to 4, corresponding to "not protective" to "most protective").

Environmental

Second, the regulatory environment mapping of banks is adapted from Barth, Caprio and Levine ( 2008)); [START_REF] Barth | The regulation and supervision of banks around the world[END_REF] for the last three periods corresponding to the Basel regulatory framework deployment [START_REF] Barth | Bank regulations are changing: For better or worse ?[END_REF]. This summary averages out capital stringency and capital activity and restrictions for each period. When banks are subjected to high levels of scrutiny from regulatory agencies, they tend to lower their risk exposure while managing their liquidity needs more efficiently. The relationship between tight supervisory control and bank risk-taking behavior is expected to be negative. A decrease in risk implies that less liquidity destruction is present, and this in turn contributes to liquidity creation. Thus, a positive regulatory framework coefficient is predicted with regard to liquidity creation. The tables below summarize the determinants for estimating liquidity creation and how they have been computed.

Insert Tables 17 and18.

Methodology used to estimate liquidity creation

Based on three approaches, this section describes the proxies to estimate liquidity creation and compares the liquidity measures with those utilized by their authors, to check for their correctness.

Note that the dataset is composed of variables extracted from a Thomson Reuters database and they do not correspond fully with those used by the authors. Because Thomson Reuters defines assets and liabilities according to their accounting classification, information on customer types and off balance sheets are non-existent. Thus, I adopted the following three measures:

 Liquidity transformation (LT) gap as established by [START_REF] Deep | Are banks liquidity transformers?[END_REF], calculated as (liquid liabilities minus liquid assets) / total assets. In this article, this measure is abbreviated as "LTGap".

 Gross liquidity creation measure (LGM) as established by [START_REF] Fungáčová | Bank capital, liquidity creation and deposit insurance[END_REF]. A simple measure that classifies all loans as illiquid and all deposits as liquid. In this article, this measure is abbreviated as "LGM".

 BB-MatNonFat as established by [START_REF] Berger | Bank liquidity creation[END_REF]. This measure calculates liquidity creation using a 3-step procedure: it first classifies bank activities as liquid / semi-liquid / illiquid using information on both product category and maturity of each activity, except that for loans only maturity information is used (due to data limitations); it then applies a weighting scheme to these activities; finally, it calculates the dollar amount of liquidity created by an individual bank by multiplying the dollar amounts in the different buckets by the weights and summing the weighted amounts. In this article, this measure is abbreviated as "BB-MatNonFat", where Mat means that the balance sheet items have categorized by maturity and NonFat means that the off balance sheet is excluded because of Thomson Reuter's limitations that do not have these data.

Because of Thomson Reuters' limitations on maturity, I followed Distinguin, Roulet and Tarazi (2013) to compute the liquidity measures. As a result, for US banks, the deposits were weighted at 33%, whereas for other banks, they were weighted at 80%. The net loans are all weighted at 85%. All amounts were deflated by the IMF inflation index (base 2005). The table below shows how these three measures were proxied and presents the Thomson Reuters items that were used to compute them. Notice that the time-series data are inflation adjusted (base 2005) from the IMF inflation index for each country and are counter evaluated in US$ at the IMF foreign exchange rate for each end of calendar year.

Insert Table 19.

Overall, these liquidity creation measures are consistent with those utilized by the works described above, although the dataset established herein helps to construct metrics for a larger range of countries and for a longer period of time.

Insert Table 20.

For a similar period (1997)(1998)(1999)(2000)(2001) and US banks, the LT gap computed with the protocol aforementioned has a mean of 0.17, a median of 0.19 and a standard deviation of 0.08, whereas the same metric computed by the authors [START_REF] Deep | Are banks liquidity transformers?[END_REF] For a similar period (1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) and for German banks, the MatNonFat and the LT Gap record a mean of 0.29 and 0.14 respectively, whereas CatNonFat and the LTGap computed by the authors Hackethal, Rauch, Steffen and Tyrell (2010) record 0.18 and 0.14 respectively. Median and standard were not indicated in their article.

For a similar period and for Chinese banks, the MatNonFat measure computed with the aforementioned protocol has a mean of 0.38, whereas the same metric computed by the authors Lei and Song (2013) record a mean of 0.28. Median and standard

were not indicated in their article.

The liquidity creation over assets is then computed as follow:

Equation 6

/ = [0.5 * illiquid assets + 0 * semi liquid assets -0.5 * liquid assets + 0.5 * liquid liabilities + 0 * semi liquid liabilities -0.5 * illiquid liabilities ] Total assets

Evolution of liquidity creation over a 34-year period

This section analyses liquidity creation. I first briefly describe the bank sample and the database; the second part analyses bank liquidity creation. To explore differences between the two metrics, I begin by comparing the three liquidity creation measures reported over assets. The second section presents liquidity creation levels valued in billions of US dollars per year and per geographic zone, and the final section compares the evolution of liquidity creation and risk to the equity level for each period. The following four periods are examined:

(1) before Basel I (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988), (2) from Basel I to II (1989II ( -2007)), (3) from Basel II to III (2008)(2009)(2010) and ( 4) since Basel III (2011 to date). My use of these periods is intuitive and is justified by the effect of the institutional and regulatory framework that surrounds banking activity, thus legitimizing my examination of the evolution of banking liquidity creation based on this timeframe.

Bank sample

This section begins with a short description of the data sources and of the panel representativeness.

Thomson Reuters referential data have the advantage of providing a somehow homogeneous set of information, making it possible to create a database of banks with common items and for a long period of time. To avoid double counting various banks that fall under the same group, I have carefully selected unique bank values and excluded their subsidiaries or branches. As shown by [START_REF] Cetorelli | Liquidity management of us global banks: Internal capital markets in the great recession[END_REF], banking groups tend to establish internal capital markets, and this may skew overall measures of liquidity creation. Table 22 shows a breakdown of the number of banks by geographical zone, country and year.

Banks selected in the

Insert Table 21.

Accounting and market data were extracted from the Thomson Reuters data source on an annual basis for the last 35 years. Foreign exchange rates, GDP growth and the inflation index have been provided by the IMF at closing end of year since 1981.

Although I acknowledge that this dataset is composed of 940 10 listed banks, excluding small banks, the panel is representative enough as shown in the table below, which presents the fraction of the market covered per geographical zone by this panel dataset.

Insert Table 22.

10 The number of banks varies per year. This variation is attributable to the fact that newly listed banks appear in the Thomson Reuter's database, while merged, delisted or bankrupt banks are omitted.

2.5.2 Descriptive statistics

The first table shows the evolution of the three liquidity creation metrics scaled to the total asset level for each period and geographic zone, thereby making a comparison between them possible.

Insert Table 23.

Table 23 illustrates the evolution of each period and geographic zone for the three liquidity creation measures "LTGap," "LGM" and "BB-MatNonFat" -such as defined in the section "2.4 Methodology used to estimate liquidity creation" -and scaled over assets. Major disparities between the geographic zones can be observed. The cause of such disparities is also due to different factors that compose the structural environment of the banks. In terms of liquidity creation, the APAC region has created most. Triggered by a strong GDP growth, which itself is linked to the low economic starting point of the different countries in this region, such increase is not surprising. This region has also benefited of less compelling regulatory framework, enabling the banks in this region to allocate their resource on illiquid assets, and hence finance the economic growth. Developed countries have a higher starting point, in terms of economic development. Therefore, the liquidity created in North America and Europe is lower than in APAC region. But US banks have enjoyed a common currency on their market, whereas European banks had to face different monetary policies until the euro currency was implemented in 2000. Additionally, the heterogeneous regulatory framework in Europe has complicated the story for the banks, until the implementation of Basel III. The budget crisis mainly triggered by Greece in 2010 has brought to light the necessity for the European countries to converge their budget, legal and fiscal frameworks.

The recent vote for Brexit in UK may accelerate this convergence. The sections below show these differences.

For LTGap (column 4). In the Asia-Pacific region, liquidity creation ratios decreased from 48% to 44% between 1981 and 2014, with a peak of 50% occurring during the period 1991-2000, which corresponds to the economic boom of the APAC zone. For Europe and North America, liquidity creation ratios increased steadily from 16% to 26% and from 14% to 22%, respectively, between 1981 and 2014. Median values follow the same trend.

For LGM (column 7). In the Asia-Pacific region, this measure decreased from 57% to 50%, with a peak of 59% occurring during the period 1991-2000. For Europe, this measure decreased from 45% to 41%, with a peak of 45% occurring during the period 1991-2000. For North America, the measure remained relatively stable at 36%, with a peak of 39% occurring during the period 2001-2010. The median follows the same trend.

For BB-MatNonFat (column 11). In the Asia-Pacific region, this measure decreased from 51% to 46%, with a peak of 53% occurring during the period 1991-2000. In Europe, the measure increased from 33% to 37% between 1981-1990 and 1991-2000 and then steadily decreased to 34% until the period 2011-2014. For North America, the measure remained relatively stable at approximately 29%.

These findings show that liquidity creation varies significantly across countries and Insert Figure 1.

For the Asia-Pacific region (data not reported but indicated in Figure 1), liquidity creation steadily increased from $153bln to $1 873bln and from $116 to $1 570bln between 1981 and 1999 for the LGM and BB-MatNonFat measures, respectively. In 2000, with the onset of the Asian Financial crisis, liquidity creation declined and fell to $1 542bln and $1 293bln for the LGM and BB-MatNonFat measures, respectively. Between 2001 and 2012, liquidity creation dramatically increased from $2 345bln and $2 030bln to $7 364bln and $6 411bln for the LGM and BB-MatNonFat measures, respectively. These values started to decline in 2013 and 2014, falling to $6 780bln and $5 728bln for the LGM and BB-MatNonFat measures, respectively. Over this period, liquidity creation levels increased 44and 49-fold for the LGM and BB-MatNonFat measures, respectively.

In Europe (data not reported but indicated in Figure 1), liquidity creation between In North America (data not reported but indicated in Figure 1), between 1981 and 1997, liquidity creation steadily increased from $485bln and $436bln to $1 051bln and $788bln for the LGM and BB-MatNonFat measures, respectively. Between 1998 and 2013, this steady increase accelerated from $1 341bln and $1 010bln to $3 324bln and $2 394bln for the LGM and BB-MatNonFat measures, respectively. In 2014, liquidity creation declined to $3 250bln and $2 329.bln for the LGM and BB-MatNonFat measures, respectively. Over this period, liquidity creation levels multiplied seven-and five-fold for the LGM and BB-MatNonFat measures, respectively.

The next table compares the evolution of liquidity creation to the level of capital and risk over four periods: (1) before Basel I (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988), (2) from Basel I to II (1989II ( -2007), (3) ), (3) from Basel II to III (2008III ( -2010) ) and ( 4) since Basel III (2011-present).

Insert Table 24.

While an increase in equity levels is observable across all regions, this was not followed by a significant decrease in the standard deviation of RoE. In the Asia-Pacific region, from 1981-1988to 1989[START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF], the std. dev. of RoE increased from 2% to 24%. From 2008-2010[START_REF] Venkiteshwaran | Partial adjustment toward optimal cash holding levels[END_REF]-2014, this measure sharply decreased from 8% to 6%. In Europe, from 1981-1988[START_REF] Venkiteshwaran | Partial adjustment toward optimal cash holding levels[END_REF]-2014, the std. dev. of RoE steadily increased from 7% to 14%. In North America, between 1981-1988and 1989[START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF], the std. dev. of RoE remained stable at 5%. From 2008-2010[START_REF] Venkiteshwaran | Partial adjustment toward optimal cash holding levels[END_REF]-2014, this variable increased from 11 to 13%.

The median values follow the same trends, though these are again not reported for the sake of brevity. It can be concluded that an increase in equity may reduce liquidity creation, but this is not necessarily accompanied by better risk performance.

The following section examines the determinants of liquidity creation and presents a sensitivity analysis of the effect of marginal increases in equity levels on liquidity creation and risk performance.

Econometric model

This section presents the results of the regression model that computes the determinants of liquidity creation. To establish a profile of banks that generate liquidity following [START_REF] Pagano | The geography of equity listing: Why do companies list abroad?[END_REF] approach for predicting the probability of a European company listing abroad, a Cox regression is also used. This section concludes with a sensitivity analysis that compares the marginal effect of an increase in capital levels on both liquidity creation and risk diminution measures. 94

Determinants of liquidity creation

Following [START_REF] Berger | Bank liquidity creation[END_REF] and [START_REF] Imbierowicz | The relationship between liquidity risk and credit risk in banks[END_REF], to address endogeneity issues, I have constructed a model for estimating a multivariate dynamic panel model using a GMM 11 estimator ( [START_REF] Arellano | Another look at the instrumental variable estimation of error-components models[END_REF]; [START_REF] Berger | Bank liquidity creation[END_REF] and [START_REF] Imbierowicz | The relationship between liquidity risk and credit risk in banks[END_REF]; [START_REF] Distinguin | Bank regulatory capital and liquidity: Evidence from us and european publicly traded banks[END_REF]).

This model can be notated as follows:

Equation 7

Eq1: Liquidity created over assets i,t = β0 + β18 Liquidity created over assets i,t-1 +

11 Usually attributed to [START_REF] Hansen | Large sample properties of generalized method of moments estimators[END_REF], Large sample properties of generalized method of moments estimators, Econometrica 1029-1054., the GMM is the most appropriate tool for controlling unobserved effects, with regards to liquidity created by banks. First, the liquidity created at time t-1 may have an autoregressive effect. The autoregressive effect can be intuitively attributed to traditional banking model contributions to the stable structure of commercial banks: When a loan is granted to a customer, it is typically allocated for a number of years. The customer may repatriate extra resources that are deposited and may open a current account, which may in turn be used to finance new loans for the same customer or for other customers.

Second, Hansen showed that every instrumental variables estimator, in nonlinear or linear models, with crosssection, panel data or time series, could be conceived as a GMM estimator. Considered as a unifying framework for inference in econometrics, this estimation has the advantage of being robust to the distribution of errors, and it is considered more efficient than 2SLS [START_REF] Hall | Generalized method of moments[END_REF]. Generalized method of moments (Oxford University Press Oxford). Most commonly tool used when there is endogeneity threat between the dependent variables, the GMM instruments them with a lagged dependent variable. In this article, the independent variables act as control variables, with regards to liquidity created at t+1. Also, at lag 2, the GMM coefficient measures the degree of mean reversion and thus (1-λ) is interpreted as the coefficient of persistence of the explicative variable Yt-1 for each bank. Refer to proof of evidence in article 1 "Why do banks hold cash?", equation 4 on how to interpret the lag 2 coefficient 1-λ. where the variables are those discussed in the first (literature review) section and listed in Table 1.

In addition to the GMM models, OLS regressions were conducted using the standard robust approach and by clustering banks to control for heteroskedasticity. For the omitted variables, a time-fixed effect test was conducted to check for average variances in liquidity creation across years that would not be accounted for by the former exogenous variables and to diminish serial correlation threats. In addition, the regression models were tested to ensure that no collinearity or size effects were present. Insert Table 25.

The results of the GMM and OLS regressions are shown in the tables below. Given the correlations with the z-score and the std. dev. of RoE or operating income, the results are first presented without the z-scores but with the std. dev. of RoE or operating income and second with the z-scores but without the std. dev. of RoE or operating income.

Insert Tables 26, 27 and 28.

The results of the GMM estimators for the European banks are remarkably similar to those presented by [START_REF] Hackethal | Determinants of bank liquidity creation[END_REF] and8). The unemployment rate, always significant, also affects negatively the liquidity creation. This confirms that an increase in economic activity results in investment growth, which in turn triggers loan demand. Bank size and equity levels both play a negative and significant role.

These results confirm the views of [START_REF] Gorton | Liquidity provision, bank capital, and the macroeconomy[END_REF] and Diamond and Rajan (2001), according to which higher capital diminishes liquidity creation, i.e., the larger the size of the bank, the lower the probability of liquidity creation. As larger banks enjoy broader access to national and international markets, they may finance their loans through indebtedness rather than by collecting deposits. Conversely, small banks may tend to prefer collecting deposits in order to fund loans. Finally, the results reported in both the OLS and fixed effects estimator, which tests for variance between countries, show that bank size and equity level coefficients are not affected by fixed effects biases, confirming that the econometric model presented generates accurate estimates. The descriptive statistics and econometric model provide some evidence of a relationship between equity levels and banking liquidity creation. However, to compare the explanatory power of the competing hypotheses and eliminate spurious correlations, I employ a duration analysis to determine which specific bank characteristics predict the variation in liquidity creation.

Bank t ypologies that generate more liquidity per geographic zone

This section describes the bank profiles that generate more liquidity than others based on the Cox regression. Though typically used for survival analysis, I use the Cox proportional hazard model because it is suited to the prediction of events in a panel setting, hence to the estimation of distances to liquidity creation. This model reports the hazard rate h(t) (that is, the probability of creating liquidity at time t conditional on not creating liquidity)

for a set of explicative variables:

Equation 8

h(t) = h 0 (t) exp (X'β)
where h 0 (t) is the baseline hazard rate at time t for the covariate vector set at 0 and β is a vector coefficient. The Cox hazard regression is a semi-parametric model, which does not require assumptions about the baseline hazard. The table below presents the exponentiated coefficients ((exp(β1), exp (β2),…) rather than the coefficients (β1, β2,…). Exponentiated coefficients can be interpreted as the effect of a unit change in the explanatory variable on the hazard ratio h(t) = h 0 (t). For instance, a coefficient of 1.098 implies that a unit change in the dependent variable increases the relative hazard by 9.8 percent.

To achieve this goal, I created a survivor function that corresponds with the derivative of the three following liquidity creation measures: LTGap, LGM and BB-MatNonFat. Note that the measures are deflated values drawn from the IMF inflation index (base 2005) for each country. If the function is negative (i.e., if liquidity creation over assets decreases over two reporting years), it is considered a failure. If the function is positive (i.e., if liquidity creation over assets increases over two reporting years), it is considered a success.

The table below shows the results and the hazard ratio for specific bank characteristics, bank sizes, risk levels and equity levels. These explicative variables have been layered to satisfy the proportional-hazards assumption. For risk levels, the stratification corresponds to the quintile, where IStratSDRoE_2 corresponds to q1 (less than or equal to 25%); IStratSDRoE_3 corresponds to q2 (less than or equal to 50%) and IStratSDRoE_4 corresponds to q3 (less than or equal to 75%). In regards to bank size, small banks are those with total assets of less than $US 50bln; medium banks are those with total assets of more than or equal to $US 50bln and of less than $US 250bln; and large banks are those with total assets of more than or equal to $US 250bln. Note that the total asset values have been deflated based on the IMF index inflation rate for each country (base 2005) and then counter revaluated at each end-of-year US$ IMF foreign exchange rate. For level of equity, stratification values vary from 0%

("_IStratEqoA_0") to 17.5% ("_IStratEqoA_5") with a regular increase of 2.5bp with each step. Overall, the Wilcoxon tests for the equality of survivor functions for the three liquidity measures generate a p(χ²) of less than 0.000, demonstrating that the hypothesis of survivor function equality is not rejected. As the Wilcoxon test gives more weight to tables at earlier failure times, this test is preferred to the log-rank test when hazard functions are believed to vary in ways other than proportionally; this is a reasonable assumption given that this paper focuses on liquidity creation over a period of 35 years.

Insert Table 29.

Although level of risk, bank size and level of equity have a large impact on banking liquidity creation, the effects vary among the three factors. The results indicate that for the transformation from liquid liabilities to illiquid assets, the liquidity that generates liquidity creation is closely linked to risk. The hazard ratio estimated shows a regular increase when the std. dev. of RoE moves to a higher quintile.

For instance, the estimate decreases but with negative coefficients from -0.249***, -0.406***, .0568*** for the LTGap and LGM, respectively, in the Asia-Pacific region (column 4) when the std. dev. of RoE changes from q1 to q3. However, for Europe, the coefficients are not significant to extrapolate the effects of increase of risk on banking liquidity creation with regard to the semi parametric regression method. However, in North America, an increase in risk level is followed by a decrease in the estimate. The coefficients steadily decreases with negative coefficients from -0.176 *** to -0.578*** for the LTGap when the std. dev. of RoE changes from q1 to q3. Only the LTGap shows significant coefficients for all regions and for the risk level criteria. The results suggest that Asia-Pacific and North American banks tend to generate liquidity for less risky projects than those of their European counterparts.

Bank size plays a role in the Asia-Pacific region and in Europe but does not significantly affect the liquidity creation process in North America. For the Asia-Pacific region, when the bank size increases, the hazard ratio decreases as well. The estimate decreases from 0.556*** to 0.464** between small and medium banks for the LTGap (column 4). For Europe, the opposite is observed. The estimate increases from 0.313*** to 0.575*** for the LTGap (column 7). A similar comparison for North America is not possible given the absence of highly significant coefficients, and thus, over a long period of time, there is no clear evidence for the real effect of bank size on liquidity creation.

The effect of equity level increases also exhibits heterogeneous evolutions between regions and across liquidity creation measures. For the Asia-Pacific region, between Equity level 1 (5-7.5%) and Equity level 3 (10%-12.5%), the estimates decrease with negative coefficients from -1.357*** to -1.496*** for the LTGap (column 4). The estimates decrease from -1.786*** to -1.818*** between Equity level 1 (5-7.5%) and Equity level 2 (7.5-10%) and increase to -1.416*** at Equity level 3 (10-12.5%) for the BB-MatNonFat (column 6).

There is strong evidence that for this region, regular equity increases are accompanied by liquidity creation decline.

For Europe, the LGM (column 8) and BB-MatNonFat (column 9) provide significant coefficients, enabling comparisons with different equity levels. For LGM (column 8), between Equity level 1 (5-7.5%) and Equity level 2 (7.5-10%), estimates decrease from -0.838*** to -0.871*** but increase to -0.592* at Equity level 3 (10-12.5%). For BB-MatNonFat (column 9) between Equity level 1 (5-7.5%) and Equity level 2 (7.5-10%), estimates decrease from -0.566*** to -0.613** but increase to 0.450** at Equity level 3 (10-12.5%).

For North America, the estimates follow very different patterns depending on the liquidity creation measure. Between Equity level 1 (5-7.5%) and Equity level 3 ( 10 These results may also leave the real effects of equity level increases on liquidity creation measures unclear. However, it can be observed that between Equity level 1 (5-7.5%)

and Equity level 2 (7.5-10%), the hazard ratio tends to decrease for all liquidity creation measures and regions, indicating that liquidity creation risks tend to increase in parallel with equity level increases. However, the results strongly suggest that optimality effects are important: Beyond a certain point, the variation in liquidity creation may change, suggesting that liquidity creation is subject to marginal effects. To further this analysis, I have conducted a sensitivity analysis of the effects of a marginal increase in equity levels on liquidity creation and risk performance for each region.

Sensitivit y anal ysis

This section presents the results of the marginal effect that a change on a regressor has on those quantities computed and after estimation. As a convenient way used in understanding the response of supply and demand in a market, the elasticity analysis enables to observe the effect of changing level of equity on level of risk vs. liquidity creation, hence to determine if the "risk absorption hypothesis prevails to the "crowding out hypothesis". This model reports the marginal effect of an increase of level of equity over the risk (i.e. Std Dev of RoE, z-score) and liquidity creation (i.e. LTGap, LGM, BB-MatNOnFat) measures, hence to compare these effects.

The figure below shows the sensitivity analysis for the LTGap, LGM and BB-MatNonFat measures and for the std dev. of RoE and z-score risk performance measures for a regular increase in equity level from 4% to 16%. The data are presented within the figures.

All of the data have p(t) values of less than 0.000 (not reported for purposes of brevity).

Insert Figure 2.

The sensitivity analysis shows that over the study period, an increase in equity levels is accompanied by a decrease in liquidity creation levels. For the Asia-Pacific region and North America, an increase in equity levels is regularly accompanied by higher declines in liquidity creation measures than declines in risk. For Europe, only the LGM is less affected and shows lower declines than risk. The other liquidity creation measures follow an identical trend to those of the Asia-Pacific region and North America.

Each 2bp increase in the level of equity is followed by a regular decrease in liquidity creation and by a slower decrease in the std dev. of the RoE, z-score and non-performing loan ratio. The diminution of non-performance is not reported, as it has a P(t) of more than 5% and can thus be ignored. Nevertheless, these findings demonstrate that a marginal increase in the level of equity has at best a relatively neutral impact on both liquidity creation and risk reduction. At worst, it reduces liquidity creation more than it reduces risk performance.

Strikingly, the decrease of liquidity creation is a curve for Asia & Pacific region, whereas it has a form of a slope or Europe and North America, suggesting that this marginal effect is higher for Asia & Pacific banks. In addition, I have compared the trends of the slopes of liquidity vs. risk. The findings show that for increase of equity over assets from 4% to 16%

 For Asian & Pacific banks, the slopes of LTGap, LGM and BB-MatNonFat measures are of-0.89, -0.49 and -0.63, respectively, whereas the slope of the risk (RoE) is of -0.02 (not reported for purposes of brevity).

 For European banks the slopes of LTGap, LGM and BB-MatNonFat measures are of -0.0003, -0.0414 and -0.0097, respectively, whereas the slope of the risk (RoE) is of -0.0020 (not reported for purposes of brevity).

 NAR the slopes of LTGap, LGM and BB-MatNonFat measures are of -0.06, -0.10 and -0.15, respectively, whereas the slope of the risk (RoE) is of -0.05

(not reported for purposes of brevity).

This sensitivity analysis demonstrates that the liquidity creation decreases more that the risks for Asian & Pacific and North American banks suggesting that over a long period of time, the "financial fragility-crowding out hypothesis" prevails for these banks. However, the liquidity tends to decrease less that the risk for European banks, suggesting that over a long period of time, the "risk absorption" hypothesis prevails for these banks.

To summarize the findings that can be extrapolated from the standard and Cox regressions and from the sensitivity analysis carried out in this section, banks with lower equity and that take more risks, create more liquidity. These outcomes confirm that over a long period of time, the negative effect of high levels of equity that [START_REF] Gorton | Liquidity provision, bank capital, and the macroeconomy[END_REF] and Diamond and Rajan (2001) The finding that bank size plays a significant role in liquidity creation processes also echoes the results of previous studies. Two main factors may oblige larger banks to hold more liquid assets than their smaller counterparts: first, as documented by [START_REF] Allen | Financial contagion[END_REF], idiosyncratic and systemic risk are closely related. Given the high volumetric nature of payments, the larger the bank, the more likely it is to hold cash to avoid internal cash-in and outflow shocks. Second, larger banks hold cash not only for idiosyncratic constraint reasons but also to establish an internal market (see [START_REF] Houston | Capital market frictions and the role of internal capital markets in banking[END_REF]). Banks may also hold cash for other reasons, as explored in the first article. However, the effect is not necessarily negative; it may also play a positive role, in particular for European banks.

Conclusions

This study yields results with respect to three factors. First, it is the only crossnational, empirical study that documents bank liquidity creation over a period of 35 years.

Second, over this period, bank liquidity creation has increased from $1 413bln and $1 185bln to $15 194bln and $11 234bln for the LGM and BB-MatNonFat measures, respectively. This pattern may vary across regions; for example, liquidity creation increases in Asia and Europe have multiplied 44-and 49-fold for the LGM and BB-MatNonFat measures, respectively, although those measures for Europe and North America have increased seven-and five-fold for the LGM and BB-MatNonFat measures, respectively. Third, this study shows that liquidity creation is not only determined by monetary policy, macro-economic environment, but also by endogenous factors of banks such as bank size, equity levels and risk performances.

The results may have implications in regards to policy issues, particularly pertaining to banking regulations and transmission of monetary policy. The results of this study highlight that an increase in equity levels has two consequences. The first is positive, i.e., risk reduction, and the second is negative and involves a decline in liquidity creation. The results indicate that at best, the positive effects are equal to the negative effects; at worst, risk reduction is lower than the decline in liquidity creation. This overall negative effect of an increase in the level of equity may present medium-and long-term consequences for banking Table 19: Items for computing the three liquidity creation measures

The table below identifies the Thomson Reuters items used to proxy the three liquidity creation measures: The liquidity creat ion measures are the "LTGap" as established by [START_REF] Deep | Are banks liquidity transformers?[END_REF]; the liquidity gross creation measure -"LGM" as established by [START_REF] Fungáčová | Bank capital, liquidity creation and deposit insurance[END_REF] and the "B-B MatNonFat" measure as established by [START_REF] Berger | Bank liquidity creation[END_REF]. The first column displays the liquidity level: liquid, semi-liquid and illiquid. Column 2 displays the asset items, column 3 presents the asset weights, column 4 presents the liability items, and column 5 presents the liability weights for the LTGap. This order is used for the LGM and BB MatNonFat measures. Note that for US banks, deposits were weighted at 33%, whereas for other banks, they were weighted at 80%. The loan net values are weighted at 85%. All amounts were deflated from the IMF inflation index (base 2005). Table 23: Liquidity created over asset measures per period and geographic zone.

The table below displays the three measures "LTGap," "LGM" and "BB-MatNonFat" of liquidity creation per year by geographic zone. Column 1 displays the region (Asia-Pacific region, Europe and North America, respectively), column 2 lists the ten-year periods, and column 3 lists the number of observations reported. Columns 4 to 6 display the average, median and standard deviation of the LTGap, respectively. This order is used for the LGM (columns 7 to 9) and BB MatNonFat (columns 11 to 12) liquidity creation measures.

Av

Table 24: Comparative evolution of equity level, risk performance, and liquidity creation measures

The table below displays per Basel periods for the evolution of equity over assets; the standard deviation of RoE; and LTGap, LGM and BB-MatNonFat liquidity creation measures per geographic zone (Asia-Pacific region, Europe, North America). Column 1 shows the four periods: (1) before Basel I (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988), (2) from Basel I to II (1989II ( -2007)), (3) from Basel II to III (2008III ( -2010) ) and (4) since Basel III (2011-present). Columns 2 to 7 show the number of observations; the average equity over assets ratio; the average standard deviation of ROA and the LTGap, LGM and BB-MatNonFat liquidity creation measures scaled over assets for Asia-Pacific banks, respectively. This order is used for the European (columns 8-13) and North American banks (columns 14-19). Though not displayed for purposes of brevity, median values follow the same trends. The table below shows the results of the GMM estimators with heteroskedasticity robust standard errors. The explained variables are the LTGap, LGM and BB-MatNonFat measures scaled over assets, respectively. Columns 1 to 3 show the typologies, the comprehensive names and the names of the explicative variables, respectively. Columns 5 to 8 show the results of the LTGap measure for all regions, for the Asia-Pacific region, for Europe and for North America, respectively. This order is used for the LGM (columns 9-12) and BB-MatNonFat (columns 13-16) measures. T-statistics are reported in brackets below the estimated coefficients, and the number of asterisks denotes the level of significance, where * denotes p<0.05, ** denotes p<0.01 and *** denotes p<0.001. The Sargan J-stat [p(chi²)] is reported at the bottom. The figure below shows the marginal effect of a regular increase in equity levels from 4% to 16% on LTGap, LGM and BBMatNonFat measures. Values are below the graphs as % values, and they all show p(t) less than 0.000, although they are not reported in the interest of brevity. 4% 6% 8% 10% 12% 14% 16% Δ % LTGap -3.02% -3.11% -3.21% -3.32% -3.43% -3.55% -3.69% Δ % LGM -3.92% -4.08% -4.25% -4.44% -4.64% -4.87% -5.12% Δ % MatNonFat -4.61% -4.83% -5.08% -5.35% -5.65% -5.99% -6.37%

Δ % SDRoE 3.66% 3.53% 3.41% 3.30% 3.19% 3.09% 3.00% Δ % Z-score 2.97% 2.89% 2.81% 2.73% 2.66% 2.59% 2.52% -8% -6% -4% -2% 0% 2% 4% 6%
North America Sensitivity analysis on level of equity over asset (from 4% to 16% )

4% 6% 8% 10% 12% 14% 16% Δ % LTGap -0.22% -0.23% -0.23% -0.23% -0.23% -0.23% -0.23% Δ % LGM -2.64% -2.71% -2.79% -2.87% -2.95% -3.04% -3.14% Δ % MatNonFat -1.34% -1.36% -1.38% -1.40% -1.41% -1.44% -1.46% Δ % SDRoE 0.65% 0.65% 0.64% 0.64% 0.63% 0.63% 0.63% Δ % Z-score 0.13% 0.13% 0.13% 0.13% 0.13% 0.13% 0.13% -4% -3% -3% -2% -2% -1% -1% 0% 1% 1%
Europe Sensitivity analysis on level of equity over asset (from 4% to 16% ) 4% 6% 8% 10% 12% 14% 16% Δ % LTGap -9.02% -9.92% -11.01% -12.37% -14.12% -16.44% -19.67% Δ % LGM -7.36% -7.95% -8.63% -9.45% -10.44% -11.65% -13.19% Δ % MatNonFat -8.07% -8.77% -9.62% -10.64% -11.91% -13.52% -15.63%

Δ % SDRoE 2.16% 2.11% 2.07% 2.03% 1.99% 1.95% 1.91% Δ % Z-score 1.11% 1.10% 1.09% 1.08% 1.07% 1.06% 1.04% -25% -20% -15% -10% -5% 0% 5%
Asia & Pacific Sensitivity analysis on level of equity over asset (from 4% to 16% ) Chapter 3. Positive effects of Basel III on banking liquidity creation

Abstract, keywords and JEL classification

This paper estimates the effect of the Basel III regulatory framework on banking liquidity creation. The results are based on a panel data set of U.S. banks that represent approximately 60% of U.S. loans and deposits over a 7-year period (from 2009 to 2015) in addition to difference-in-difference and standard survival methods. All components of Basel III taken together, there is empirical evidence that Basel III has a positive effect on banking liquidity creation in the US market in particular for major banks. These findings have broad implications for policy makers.

Introduction

This paper estimates the effect of the new banking regulatory frameworkcommonly referred to as Basel III -on banks' liquidity creation. Because the liquidity crisis of the banking sector was the cause of the 2007/2008 financial crisis, regulators have tightened banking liquidity ratios. Indeed, under the BIS (Bank of International Settlement) requirement and, in particular, Basel III, the LCR (liquidity coverage ratio) and NSFR (net stable funding ratio) have been created. The former underpins the short-term resilience of a 123 bank's liquidity risk profile, whereas the latter covers medium-and long-term liquidity capabilities. The LCR regulatory framework has been prioritized and will be deployed in an orderly fashion within a common timeframe among a significant majority of countries 13 .

Although the deployment of the LCR will begin in 2015 and be completed in 2019, it will be subject to a transitional arrangement before reaching full implementation on 1 January 2019.

In addition, certain requirements, such as the increase in tier 1 and core tier 1 capital, started in 2013.

In January 2014, the Basel Committee issued the final requirement for bank LCRrelated disclosures; the regulatory and supervisory agencies required the banks belonging to their jurisdictions to disclose the LCR on 1 January 2014. For instance, calculation and reporting requirements have been applicable since 1 January 2014 in the EU 14 . In Switzerland, all banks reported the Basel III leverage ratio for Q3 2014. In the US, the leverage ratio included in the final Basel III rule was approved in July 2013 and has been in effect since 1 st January 2014. To comply with this regulatory framework and as a potential signal addressed to the market, banks have started to anticipate these liquidity constraints.

What will the effects of such regulation be? Will the treatment (the Basel III regulatory framework) be positive, or will it ultimately weaken the banking sector? Overall, there is no consensus within the academic community on whether the effects will be positive or negative [START_REF] Bancel | Focus on basel 3 and bank liquidity creation[END_REF]). On one hand, by providing a cushion layer of extra 13 An exhaustive list of countries that have signed the Basel III regulatory framework is available on the BIS web site. This list includes not only the G20 and EU countries but also Chile, Norway, the Republic of Korea, the Republic of China (Taiwan), and Switzerland. Additional information is available at : "http://www.bis.org/fsi/fsipapers.htm". 14 Articles 451 and 521 of Regulation (EU) N° 575/2013. liquidity, many experts estimate that this regulation will strengthen the banking sector against a liquidity crisis without additional social costs and will have overall positive effects. On the other hand, other authors wish to highlight the risk that inappropriate regulation can generate for liquidity creation; by obliging banks to allocate fewer available resources to fund their activity, the regulation reduces liquidity creation. From this perspective, the regulation ultimately weakens the macroeconomic environment, encourages shadow banking, and weakens the banking system.

Because banks began progressively implementing the required liquidity constraint before the official deadline of 1 January 2015, this paper proposes estimating the effect of this treatment on banking liquidity creation. To achieve this objective, two sets of methods have been used. The first, is [START_REF] Card | Minimum wages and employment: A case study of the fast-food industry in new jersey and pennsylvania[END_REF], who use the difference-in-difference (D-in-D) method to estimate the effect of a wage increase policy on the fast food industry. Since their publication, this method has been broadly used by most econometricians to estimate a policy's effect. For robustness, the effect by using other methods, in particular, life table and Cox regression methods were analyzed. These methods offer additional insights into the policy effects and complete the analysis of the D-in-D estimation. This paper is organized as follows: Section 1 reviews the literature on the effects of banking regulation on liquidity creation. It also provides a brief summary of the components of the Basel III regulatory framework. Section 2 describes the bank sample and the methodology and models applied to estimate the effects of Basel III. Section 3 estimates the effects of Basel III, using a set of methods that includes the D-in-D and additional methods for robustness. Concluding remarks are drawn from these empirical findings and directed toward policy makers. The effect of regulation on the banking sector has been the subject of debate for decades, and there is an extensive strand of the literature that addresses these effects [START_REF] Admati | Fallacies, irrelevant facts, and myths in the discussion of capital regulation: Why bank equity is not expensive[END_REF]), in particular since the financial crisis. This literature first poses questions regarding the proper objective of financial regulation and sufficient measures to achieve the goal [START_REF] Morris | Financial regulation in a system context[END_REF]). For instance, [START_REF] Repullo | Liquidity, risk-taking and the lender of last resort[END_REF] shows that the existence of the lender-of-last-resort mechanism has both positive and negative effects: although it improves the safety of the financial system, it reduces the incentive for banks to self-insure against payment shocks. As a result, a liquidity buffer is necessary and must be supported by a regulatory framework. However, it is difficult to anticipate the effects of a regulation. Indeed, [START_REF] Morris | Liquidity black holes[END_REF] suggest that inappropriate regulations can be the source of liquidity shortages, and [START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF] shows that an augmentation of liquid assets unexpectedly increases the instability of banks and the probability of failure. It is the inherent fragility of banksthat is, liquidity creation and the risk transformation process [START_REF] Diamond | Liquidity risk, liquidity creation and financial fragility: A theory of banking[END_REF]); Diamond and Rajan (2001)) -that motivates policy makers to establish regulatory constraints as a means to protect against banking fragility and to limit the spillover effects when a bank defaults. Ultimately, regulatory constraints may in turn reduce banking liquidity creation [START_REF] Berger | Bank risk taking and liquidity creation following regulatory interventions and capital support[END_REF].

Finally, at least one consensus can be reached. Given the complexity of the interconnection between the banking sector and the macroeconomic environment, it is necessary to measure the amplitude of the effect of Basel III on banking liquidity creation.

How can the effect of the regulation be measured? Several surveys have attempted to study the effect of banking regulation on liquidity. From a liquidity perspective, [START_REF] Berger | Bank risk taking and liquidity creation following regulatory interventions and capital support[END_REF]

From Basel I to Basel III , a brief history

At the onset of the 1980s, many American Latin countries were not able to pay their foreign debt. This "Latin American debt crisis" deteriorated the capital and generated systemic risk for the US banks, which triggered the regulators of the central banks to establish common standards in all important financial marketplaces. As a result, the G10 countries signed the 1988 accord that called for a minimum capital ratio of capital to risk-weighted assets of 8%. This accord evolved over time to entail precise definitions such as general provisions or general loan-loss reserves included in the capital adequacy calculation or rules on netting in derivative products. Finally, this framework was extended to all other countries with active international banks (Basel I). Despite these attempts to improve this regulatory framework, Basel I had a structural weakness. It did not provide the appropriate method to accurately compute the underlying risks. For this reason, the revised capital framework of Basel II was released in June 2004; it had three objectives, also named the "three pillars": (1) developing and expanding the standardized rules established in Basel I by imposing minimum capital requirements on all; (2) establishing a common supervisory review framework on institutions' capital adequacy and internal assessment process; and (3) strengthening market discipline and encouraging sound practices by enforcing higher disclosure standards for the banking sector. However, the collapse of Lehman Brothers in September 2008 demonstrated that the combination of poor governance and risk management with excessively leveraged activities and insufficient liquidity buffers were the basements for misprices of credit and liquidity risk. To respond to these risks, in November 2010, the G20 Summit in Seoul established the new capital and liquidity standards, referred as Basel III.

Brief presentation of the Components of Basel III

This section presents briefly the main components of the new Basel III regulatory framework.

The Basel III regulatory framework has several facets, i.e., increasing tier 1 and core tier 1 capital, in terms of both quality and amount; imposing limits on leveraged activities; obliging banks to create cushions against liquidity risk; and promoting the development of organized markets for derivatives. The table below summarizes these components.

Insert Table 30.

From a broad liquidity perspective, the objective of this new regulatory framework is to ensure that banks hold sufficient liquid assets to protect against unpredictable payment shocks [START_REF] Ashcraft | Precautionary reserves and the interbank market[END_REF]), the contagious effects of which may lead to a global financial crisis. Thus, the principle is straightforward: banks must hold a certain amount of high-quality liquid assets (HQLA) to be able to potentially cover their liquidity needs for a certain number of days. Therefore, the liquidity risk is expected to decrease among the banks. The number of days depends mainly on the bank size in order to mitigate systemic risk. The table below summarizes the types of banks that are subject to the liquidity requirements of the new banking regulation. I acknowledge that there are multiple size thresholds in Basel III, and their application is not a simple function of bank size 15 . However, the observation indicates that 95% of US banks have a size of less than $10 bln (see section "3.4.1 Bank sample and the fraction of the market covered"). Consequently, it can be estimated that the population identified as untreated is virtually not concerned by the liquidity ratios. It enables to break down two types of population, i.e., untreated vs. treated, hence to tests the effects of the treatment i.e. the Basel III regulatory framework. 15 With respect to the liquidity coverage ratio (LCR), here is a summary of the thresholds from the Federal Register (source: Federal reserve System, 12 CFR Part 249, Regulation WW; Docket No. 1525) "The final rule applies to large and internationally active banking organizations, generally, bank holding companies, certain savings and loan holding companies, and depository institutions with $250 billion or more in total assets or $10 billion or more in on-balance sheet foreign exposure and to their consolidated subsidiaries that are depository institutions with $10 billion or more in total consolidated assets. The final rule focuses on these financial institutions because of their complexity, funding profiles, and potential risk to the financial system. Therefore, the agencies do not intend to apply the final rule to community banks. In addition, the Board is separately adopting a modified minimum liquidity coverage ratio requirement for bank holding companies and savings and loan holding companies without significant insurance or commercial operations that, in each case, have $50 billion or more in total consolidated assets but that are not internationally active. The final rule is effective January 1, 2015, with transition periods for compliance with the requirements of the rule."

Insert Table 31.

By obliging banks to hold a certain amount of cash as insurance against liquidity shocks, it forces banks to internalize the cost of a financial crisis. Because the consequences of financial crises generate social costs, by internalizing this social cost within the banking sector, the liquidity ratios can finally be viewed as Pigouvian taxes.

However, does this new banking regulation affect the banking sector on a global basis and are the effects equally distributed? In the desire to ensure that all of the main players follow the same rules and to avoid distortions between competitors, the G20 countries (including all EU member states) and Chile, Norway, the Republic of Korea, the Republic of China (Taiwan), and Switzerland have agreed to adopt Basel III 16 . Equality of treatment is also ensured by the progressive deployment schedule of the Basel III LCR requirements, as shown in the table below.

Insert Table 32. From 2009 to 2014, the table below shows the number of banks per year.

Insert Table 33.

Because the panel data set is uniquely composed of listed banks, it is dominated by small banks. However, 95% of US banks have a small size of less than $10 bln and represent approximately 15% of the sum of assets of all US banks (figures not displayed), neutralizing the effect size of the number of small banks.

Following [START_REF] Berger | Financial crises and bank liquidity creation[END_REF], I include data from the consolidated financial statements of mother companies, which include the financial statements of branches and subsidiaries but eliminate intragroup activities. I have carefully selected unique values for each bank and excluded their subsidiaries or branches. As shown by [START_REF] Cetorelli | Liquidity management of us global banks: Internal capital markets in the great recession[END_REF] and [START_REF] Houston | Capital market frictions and the role of internal capital markets in banking[END_REF], banking groups tend to establish internal capital markets. Thus, if the liquidity creation of each bank belonging to the same group is summed, the total liquidity created is inflated. Therefore, to avoid double counting liquidity creation, the banks selected exclude subsidiaries and branches. Additionally, foreign banks and their branches and subsidiaries are excluded. Therefore, each selected bank is a unique unit that corresponds to the mother company of a banking group. I acknowledge that these data are composed of 555 listed banks, excluding small banks. However, this exclusion does not obstruct the results because the panel is sufficiently representative. As shown in the table below, the panel data set indeed covers a substantial fraction of the US market.

Insert Table 34.

The banks selected in Thomson Reuters correspond to those classified in ICB industry sector 800 as "banks providing a broad range of financial services, including retail banking, loans and money transmissions," which may aggregate different business lines that correspond to various banking activities (e.g., M&A, commercial banks) or banks with state involvement (partially state-owned banks, fully private banks). Observe that the sample covers 61% and 60% of the deposits and loans, respectively, of the US market. Thus, the panel elaborated can be considered representative of the US market.

Methodology and econometric model

This section presents the results of the econometrics used to estimate the effect of Basel III on banking liquidity creation.

Measuring ban ki ng li qui dity creation

This section describes the main measures used to estimate the banking liquidity creation and justifies why these measures were used.

To determine the impact on banking liquidity creation, I have first calculated liquidity creation based on the most common measures established: the LTGap and the BB-MatNonFat 18 established by [START_REF] Deep | Are banks liquidity transformers?[END_REF] and by [START_REF] Berger | Bank liquidity creation[END_REF], respectively. Given that Thomson Reuters does not provide off-balance sheet data or offer the category of loans, only the BB-MatNonFat could be calculated. The table below shows how these two measures have been proxied and also details which Thomson Reuters items were used to compute them.

Because of Thomson Reuters' limitations on maturity, I followed [START_REF] Distinguin | Bank regulatory capital and liquidity: Evidence from us and european publicly traded banks[END_REF] to compute the liquidity measures. As a result, for US banks, the deposits were weighted at 33%, whereas for other banks, they were weighted at 80%. The net loans are all weighted at 85%. All amounts were deflated by the IMF inflation index (base 2005).

Insert Table 35. 18 As per as defined in Chapter 2 (Article 2) p.83 : the Liquidity transformation (LT) is calculated as (liquid liabilities minus liquid assets) / total assets. In this article, this measure is abbreviated as "LTGap" The BB-MatNonFat", calculates liquidity creation using a 3-step procedure: it first classifies bank activities as liquid / semi-liquid / illiquid using information on both product category and maturity of each activity, except that for loans only maturity information is used (due to data limitations); it then applies a weighting scheme to these activities; finally, it calculates the dollar amount of liquidity created by an individual bank by multiplying the dollar amounts in the different buckets by the weights and summing the weighted amounts. Where Mat means that the balance sheet items have categorized by maturity and NonFat means that the off balance sheet is excluded because of Thomson Reuter's limitations that do not have these data. In this article, this measure is abbreviated as "BB-MatNonFat",

To check for correctness, I have compared the results of the measures computed in this research with those from the aforementioned authors and for similar periods. The table below demonstrates that, overall, these liquidity creation measures are consistent with those utilized by several authors. 36.

Insert Table

Measuring the effects of a policy

This section explains the models used to estimate the effect of Basel III on liquidity creation of banks: D-in-D and additional models used for robustness, i.e actuarial and Cox regression.

The main principle of the D-in-D method to assess the impact of a regulatory change on banks is similar to the survival models used in medical science to estimate the effects of a medical treatment on a population: it compares 2 populationsone is subject to the treatment, and the other is not -in two periodsbefore the treatment and after. In this paper, Basel III is considered to be the treatment, and the banking population is divided into 2 types of banks: those subject to Basel III ("treated") and those that are not ("untreated"). Following section 3.4, which summarizes the Basel III regulatory framework, the treated vs. untreated populations are divided based on their asset size ($50 billion).

The time series are between 2009 and 2015, where 2013 is the starting year of the implementation of the liquidity constraints. The organizational challenges implied by such a deployment for banks and the time given by regulators to banks to adjust to new capital requirements [START_REF] Kashyap | An analysis of the impact of 'substantially heightened'capital requirements on large financial institutions[END_REF]) indicate that the banks started to prepare some years in advance to satisfy the first deadline requirements. In addition, two major

The table below shows how the two population typestreated and untreatedhave been elaborated.

Insert Table 37.

Categorization as treated vs. untreated is a function of bank size, where untreated banks are banks for which total assets are below 50 bln. A large strand of the literature shows that small and large banks behave differently. Although, from a theoretical perspective, the fundamental function of liquidity creation does not depend on the bank size, [START_REF] Berger | Bank liquidity creation[END_REF] document that large and small banks create liquidity in different ways when using a measure that includes the off-balance sheet balances. Thus, I have compared the averages, medians and standard deviations of liquidity creation per population and do not find large differences. For the untreated population, the average, median and standard deviation are 0.22, 0.23 and 0.06 for LT Gap and 0.30, 0.32 and 0.09 for MatNonFat scaled over assets, respectively. For the treated population, the average, median and standard deviation are 0.16, 0.19 and 0.10 for LT Gap and 0.23, 0.26 and 0.12 for MatNonFat scaled over assets, respectively (these figures are displayed in the table 38 and are shown in Figure 3). In addition, to estimate the degree of heterogeneity between the untreated and treated populations, I have computed the propensity score matching [START_REF] Rosenbaum | The central role of the propensity score in observational studies for causal effects[END_REF]).

The PSM (Propensity Score Matching) is an estimate of the likelihood that any given individual would be in a treatment group, given a set of measures characteristics. Ranging from 0 to 1, the PSM is commonly notated as follow: Equation 9Pr (Tr =1 | x 1 , x 2 , …x n ) Where:

Pr indicates the likelihood that any given individual would be a treatment group X i , represent the set of variables or characteristics used to compare both population To compute the distance between the two populations, the following bank-specific characteristics were aggregated:earnings per share, leverage ratio , standard deviation of RoE, equity over asset ratio; non-performing loans, z-score, paid shareholders over equity, loan growth, net income over assets and market price per bank. Windsorized at 5%, the rate of on support over total number of observations equals 96.55% (2971 over 3077; not displayed in the tables for brevity) for both LTGap and BB-BB-MatNonFat, providing strong evidence that, on liquidity creation perspective, both treated and untreated populations are nearly perfectly comparable, given the aforementioned set of inherent characteristics of banks.

Additional methods that estimate the Basel III effect on banking liquidity creation have been used for robustness. The first method is actuarial and the second method is the Cox regression method. For both the actuarial and the semi-parametric regression, the derivative of liquidity creation was used: it is the variance between y and y-1 of the liquidity creation from the LTGap and BB-MatNonFat. If the function is negativeliquidity creation over assets decreases between 2 reporting yearsthen it is considered to be a failure. If the function is positiveliquidity creation over assets increases between 2 reporting yearsthen it is considered to be a success. In addition, the log-rank tests for equality of derivative functions have been used to determine whether the differences in the functions differ significantly between treated and untreated samples and for both the LTGap and the BB-MatNonFat measures.

For the liquidity creation measures in US$ billions (BB-MatNonFat, columns 9 and 17): liquidity creation by untreated banks steadily increased from US$342 bln to US$412 bln between 2009 and 2014 but decreased to US$410 bln in 2015. These figures are to be reconciled with the sharp decrease of small US banks as observed in table 4. For treated banks, liquidity creation increased from US$621 bln to US$899 bln between 2009 and 2013, then decreased to US$810 bln in 2014. It has sharply increased to US$923 bln in 2015, whereas the number of treated banks has remained stable to 15 banks (table 38).

Second, the observed negative evolution of banking liquidity creation among treated banks in 2014 is very small and is contradicted by an increase of liquidity creation in 2015.

Based on the trends displayed in Figure 3, extrapolating the effects of Basel III on banking liquidity creation is difficult. Hence, the next section estimates the effects of Basel III with more sophisticated approaches.

Estimation of the effects of Basel III on banking liquidity creation

This section presents the results of the models that estimate the effect of Basel III on bank liquidity creation.

Estimation by D -in -D

This section shows the results of the D-in-D estimator. To estimate the policy effect, I have specified the model as follows:

Equation 10 Δ Liquidity created over assets i,tn = β0 + β1G1 + β2R2 + β3(G • R) +ε.

Where:

G equals 1 if the bank is treated; otherwise, 0. R equals 1 if the subject was observed in the second period, otherwise 0. G • R is the interaction term between G and R.

To control for endogeneity, I established a multivariate dynamic panel model GMM20 estimator ( [START_REF] Arellano | Another look at the instrumental variable estimation of error-components models[END_REF]; [START_REF] Berger | Bank liquidity creation[END_REF] and [START_REF] Imbierowicz | The relationship between liquidity risk and credit risk in banks[END_REF]; [START_REF] Distinguin | Bank regulatory capital and liquidity: Evidence from us and european publicly traded banks[END_REF]).

This GMM is itself based on an OLS model that is based on the determinants of liquidity creation from the literature review. Several studies have examined banking liquidity creation in other countries, such as [START_REF] Lei | Liquidity creation and bank capital structure in china[END_REF] The table below shows the items selected to compute the aforementioned determinants.

Insert table 39.

OLS regressions were conducted by using the standard robust approach and by clustering banks to control for heteroskedasticity. For the omitted variables, a time fixedeffect test was conducted to check for average variances in liquidity creation across years that would not be accounted for by the former exogenous variables and to diminish the risk of serial correlation. In addition, the regression models were tested to ensure that no collinearity or size effects were present. They show p-values of less than 0.001 with Eta² or Omega² values of less than 0.4. Student tests show p-values of less than 0.001.

The table below displays the results of the aforementioned econometric model and they indicate that the model is robust to endogeneity and fixed effects. The table below shows also the results of the D-in-D estimator for both the LTGap and BB-MatNonFat measures.

Insert Table 40

For the LTGap, the results show that the interaction term is 0.28** (columns 4). This interaction term, controlled by the determinants of liquidity creation, remains very significant at 0.24** (column 5). For BB-MatNonFat, the interaction term has a coefficient of 0.28** (column 8). Controlled by the determinants of banking liquidity creation, the interaction term remains significant at 0.25*.

Based on the findings of the D-in-D analysis, it can be concluded that Basel III has a positive effects on banking liquidity creation, for both LTGap and BB-MatNonFat scaled over asset measures.

Robustness tests and estimation with additional methods

This section presents the results of additional models used to estimate the effects of Basel III on bank liquidity creation, for robustness: actuarial model and semi parametric regression.

For both the actuarial and the semi-parametric regression, I use the derivative of liquidity creation: it is the variance between y and y-1 of the liquidity creation from the LTGap and BB-MatNonFat. If the function is negativeliquidity creation over assets decreases between 2 reporting yearsthen it is considered to be a failure. If the function is positiveliquidity creation over assets increases between 2 reporting yearsthen it is considered to be a success. In addition, the log-rank tests for equality of derivative functions have been used to determine whether the differences in the functions differ significantly between treated and untreated samples and for both the LTGap and the BB-MatNonFat measures.

Cox regression and c omparison of banks' prope nsity to create liqui dity

The Cox regression provides additional clues on the propensity of banks to create liquidity. This model reports the hazard rate h(t) (that is, the probability of creating liquidity at time t conditional on not creating liquidity) for a set of explicative variables:

Equation 12 h(t) = h0(t) exp (X'β)
where h0(t) is the baseline hazard rate at time t for the covariate vector set at 0, and β is a vector coefficient. The Cox hazard regression is a semi-parametric model that does not require assumptions about the baseline hazard. The table below presents the exponentiated coefficients ((exp(β1), exp (β2),…) rather than the coefficients (β1, β2,…) and also shows the results and the hazard ratio for specific bank characteristics, including bank size, risk level and equity level. These explicative variables have been layered to satisfy the proportionalhazards assumption. For the risk levels, the stratification corresponds to the quintile, where level of risk_2 corresponds to quintile 1 of the std. dev. of RoE less than or equal to 25%, level of risk _3 corresponds to quintile 2 of the std. dev. of RoE less than or equal to 50%, and level of risk_4 corresponds to quintile 3 of the std. dev. of RoE less than or equal to 75%.

With regard to bank size, small banks are those with total assets of less than $US50 bln, medium banks are those with total assets of between $US50 bln and $US250 bln, and large banks are those with total assets of $US250 bln or more.

Insert Table 42.

The results indicate that liquidity creation is closely linked to risk. The estimated hazard ratio exhibits a regular increase when the standard deviation of RoE moves to a higher quintile. For instance, between quintile 2 and 4 the estimates increase from 1.313*** and 1.282*** to 1.625*** and 2.510*** for the LTGap and BB-MatNonFat, respectively. The coefficients of bank size are not significant, which makes it difficult to extrapolate the effects by the bank size category. It also indicates that on liquidity perspective, the bank size does not make the difference, which is consistent with the PSM tests.

The hazard ratio estimates tend to decrease when the level of equity is augmented.

For instance, at the lower level of equity over assets of less than or equal to5% (IStratEqoA0), the coefficient is 1.155 and 1.516** for the LTGap and BB-MatNonFat, respectively. At the level of equity less than 5% to 7.5% (IStratEqoA1), the coefficient is 1.271 and 1.097 for the LTGap and BB-MatNonFat, respectively. At the level of equity less than 7.5 % to 10% (IStratEqoA2), the coefficient is 0.937and 0.762* for the LTGap and BB-MatNonFat, respectively. At the level of equity less than 10 % to 12.5% (IStratEqoA3), the coefficient is 0.865 and 0.748* for the LTGap and BB-MatNonFat, respectively. At the level of equity less than 12.5 % to 15% (IStratEqoA3), the coefficient is 0.845and 0.740* for the LTGap and BB-MatNonFat, respectively. This finding is evidence that as the risk profile of the bank increases, the probability of failure with regard to liquidity creation for both the LTGap and the BB-MatNonFat measures increases. The results also confirm that, increase of capital contributes to increase the probability of liquidity creation.

However, unlike the LTGap measure, the BB-MatNonFat measure is observably sensitive to changes in the funding structure. [START_REF] Berger | Bank liquidity creation[END_REF] highlight this endogeneity issue and view it as a correlation matter instead of a causal relationship. As pointed out by [START_REF] Distinguin | Bank regulatory capital and liquidity: Evidence from us and european publicly traded banks[END_REF]), they address this endogeneity using instrumental variable regressions.

Conclusions

Although the primary focus of this paper has been to determine the effects of Basel III on banking liquidity creation, the results presented herein have broader applicability for policy makers. First, the choice of the measure is not neutral either. Because the BB-MatNonFat measure is constructed with funding structure data, it is more sensitive to the effects of Basel III. It can be concluded that the LTGap measure is more appropriate for measuring the effects of Basel III on liquidity creation, although the BB-MatNonFat measure and its variants offer considerable advantages when estimating liquidity creation in terms of volume.

Though the deployment of the Basel III will be fully in place in 2019, the results are significant and robust. The empirical results of this research indicate that, all components of Basel III taken together, the positive effects are higher than the negative effects. It implies that the synergies of the different component create an overall positive effect (for instance risk reduction and stabilization of the financial sector) that is superior to the negative effects that some components taken individually may have on liquidity creation. These findings have broad implications for policy makers.

Table 30: Key aspects of Basel III This table summarizes the Basel III key components (source: the Bank for International Settlements (BIS) and the US Federal Reserve). Column 1 displays the key components of Pillar1 -Capital, column 2 for pillar 1 -risk coverage, the column 3 for pillar 1-Containing leverage; column 4 pillar 2 -Risk management and supervision, column 5 pillar 3market discipline and column 6 -liquidity. The row at the bottom makes a special note for systemically important financial institutions (SIFIs). -The minimum will be raised to 4.5% of riskweighted assets, after deductions.

Securitisations

-Strengthens the capital treatment for certain complex securitisations.

-Requires banks to conduct more rigorous credit analyses of externally rated securitisation exposures.

Capital loss absorption at the point of nonviability -Contractual terms of capital instruments will include a clause that allows -at the discretion of the relevant authority -write-off or conversion to common shares if the bank is judged to be nonviable.

-This principle increases the contribution of the private sector to resolving future banking crises and thereby reduces moral hazard.

Trading book -Significantly higher capital for trading and derivatives activities, as well as complex securitisations held in the trading book.

-Introduction of a stressed value-at-risk framework to help mitigate procyclicality.

-A capital charge for incremental risk that estimates the default and migration risks of unsecuritised credit products and takes liquidity into account.

Capital conservation buffer

Comprising common equity of 2.5% of riskweighted assets, bringing the total common equity standard to 7%. Constraint on a bank's discretionary distributions will be imposed when banks fall into the buffer range.

Counterparty credit risk

Substantial strengthening of the counterparty credit risk framework. Includes: more stringent requirements for measuring exposure; capital incentives for banks to use central counterparties for derivatives; and higher capital for interfinancial sector exposures.

Principles for Sound Liquidity Risk Management and Supervision

The Committee's 2008 guidance Principles for Sound Liquidity Risk Management and Supervision takes account of lessons learned during the crisis and is based on a fundamental review of sound practices for managing liquidity risk in banking organisations.

Countercyclical buffer

Imposed within a range of 0-2.5% comprising common equity, when authorities judge credit growth is resulting in an unacceptable build up of systematic risk.

Bank exposures to central counterparties (CCPs)

The Committee has proposed that trade exposures to a qualifying CCP will receive a 2% risk weight and default fund exposures to a qualifying CCP will be capitalised according to a risk-based method that consistently and simply estimates risk arising from such default fund.

Supervisory monitoring

The liquidity framework includes a common set of monitoring metrics to assist supervisors in identifying and analysing liquidity risk trends at both the bank and system-wide level

SiFis

All banks

In addition to meeting the Basel III requirements, global systemically important financial institutions (SIFIs) must have higher loss absorbency capacity to reflect the greater risks that they pose to the financial system. The Committee has developed a methodology that includes both quantitative indicators and qualitative elements to identify global systemically important banks (SIBs). The additional loss absorbency requirements are to be met with a progressive Common Equity Tier 1 (CET1) capital requirement ranging from 1% to 2.5%, depending on a bank's systemic importance. For banks facing the highest SIB surcharge, an additional loss absorbency of 1% could be applied as a disincentive to increase materially their global systemic importance in the future. A consultative document was published in cooperation with the Financial Stability Board, which is coordinating the overall set of measures to reduce the moral hazard posed by global SIFIs.

Capital Pillar 1

Supplemental Pillar 2 requirements. Address firm-wide governance and risk management; capturing the risk of offbalance sheet exposures and securitisation activities; managing risk concentrations; providing incentives for banks to better manage risk and returns over the long term; sound compensation practices; valuation practices; stress testing; accounting standards for financial instruments; corporate governance; and supervisory colleges.

Leverage ratio

A non-risk-based leverage ratio that includes off-balance sheet exposures will serve as a backstop to the risk-based capital requirement. Also helps contain system wide build up of leverage.

Revised Pillar 3 disclosures requirements

The requirements introduced relate to securitisation exposures and sponsorship of off-balance sheet vehicles. Enhanced disclosures on the detail of the components of regulatory capital and their reconciliation to the reported accounts will be required, including a comprehensive explanation of how a bank calculates its regulatory capital ratios

Liquidity coverage ratio

The liquidity coverage ratio (LCR) will require banks to have sufficient highquality liquid assets to withstand a 30-day stressed funding scenario that is specified by supervisors. The Liquidity Coverage Ratio applies to U.S. banks as follow: 1-Large Bank Holding Companies (BHC) -those with over $250 billion in consolidated assets, or more in on-balance sheet foreign exposure, and to SiFis : 30 days, 2 -Regional banks, whose gross total asset is between $50 and $250 billion : 21 days 3 -Small banks, whoc gross total asset is lower than $50 billion : reprieve

Net stable funding ratio

The net stable funding ratio (NSFR) is a longer-term structural ratio designed to address liquidity mismatches. It covers the entire balance sheet and provides incentives for banks to use stable sources of funding. The table below shows the number of untreated and treated banks per year. The first column indicates the year, and the second column indicates the number of listed banks. The banks selected in Thomson Reuters correspond to those classified in ICB industry sector 800 as "banks providing a broad range of financial services, including retail banking, loans and money transmissions." To avoid double counting from the same group of banks, the banks selected exclude subsidiaries and branches. Foreign banks and their branches and subsidiaries are also excluded. Thus, each bank is a unique US bank unit. The table below identifies the Thomson Reuters items used to proxy the liquidity creation measures: the "LTGap" measure, as established by [START_REF] Deep | Are banks liquidity transformers?[END_REF], and the "B-B MatNonFat" measure, as established by [START_REF] Berger | Bank liquidity creation[END_REF]. The first column displays the level of liquidity: liquid, semi-liquid and illiquid. Column 2 displays the asset items; column 3, the asset weight; column 4, the liabilities item; and column 5, the liabilities weight for the LTGap measure. This order is replicated for the BB MatNonFat measures. Observe that, for US banks, the deposits were weighted at 33%, whereas for other banks, they were weighted at 80%. The net loans are all weighted at 85%. All amounts were deflated by the IMF inflation index (base 2005).

Table 36: The results of the liquidity creation measures compared with other articles

The table below compares the LTGap and BB-CatNonFat liquidity creation measures for US, German and Chinese banks. Note that I only present the MatNonFat measure, whereas other researchers show only the CatNonFat measures, which makes it difficult to conduct a strict comparison. The differences found are in line with the results of [START_REF] Berger | Bank liquidity creation[END_REF] The table below shows the evolution of the main liquidity creation indicators of both the treated and the untreated banks from 2008 to 2015. The LT gap corresponds to the liquidity creation measure established by [START_REF] Deep | Are banks liquidity transformers?[END_REF]. The second metric corresponds to the liquidity creation measure established by [START_REF] Berger | Bank liquidity creation[END_REF]. The latter is shown scaled over assets. Column 1 displays the year. For untreated banks (columns 2 to 9), columns 2 to 5 display the number of observations and the mean, median, and standard deviation of the LTGap measure, respectively. Columns 6 to 9 display the number of observations and the mean, median, and standard deviation of BB-MatNonFat measure scaled over assets and the BB-MatNonFat in US$ billions, respectively. This order is replicated for treated banks (columns 10 to 17) and for all banks (columns 18 to 25). The graphs below show the results of the actuarial method utilized to pre-estimate the effects of the new banking regulation on liquidity creation between untreated banks and treated banks. The Y-axis corresponds to the survival function, and the X-axis corresponds to the time (year). Data are established from the life table actuarial method, where the survival function is the derivative of the LTGap measure and the BB-MatNonFat measure scaled over assets; the untreated population corresponds to banks that are not subject to Basel III, and the treated population corresponds to banks that are subject to Basel III.

Observe that the interval is one year and that the last interval, 2015/2016, corresponds to the end of the year 2015. This table displays the hazard ratio of the Cox estimation of the LTGap and BB-MatNonFat measures. The derivative function corresponds to the derivative of liquidity creation: it is the variance between y and y-1 of liquidity creation. If negative, then it takes the value of 1, i.e., failure; otherwise, zero. The explanatory variables have been layered. For the level of risk, the stratification corresponds to the quintile, where IStratSDRo_2 corresponds to q1 (less than or equal to 25%), IStratSDRo_3 corresponds to q2 (less than or equal to 50%), and IStratSDRo_4 corresponds to q3 (less than or equal to 75%). For bank size, small banks are those with total assets below $US50 bln; medium banks are those with between $US50 bln and less than $US250 bln; and large banks are those with $US250 bln or more. For the level of equity, the stratification varies from 0% ("_IStratEqoA_0") to 17.5% ("_IStratEqoA_5"), with a regular increase of 2.5 bp at each step. Columns 1 to 3 indicate the type of variable, the variable name used in the model, and the value corresponding to the variable, respectively. Columns 4 to 5 display the hazard ratio of the LTGap and BB-MatNonFat measures, respectively. Z-statistics are reported in brackets below the estimated coefficients, and the number of asterisks indicates the level of significance, where * for p(z) <0.05, ** for p(z)<0.01 and *** for p(z)<0.001.

Conclusion

This thesis has focused on liquidity in the banking sector. As one of the cornerstones of banking, liquidity is a concept that can be viewed from two primary, related angles. The first refers to the cash or assimilated resources that banks hold to meet immediate or shortterm needs. They provide security in the face of unforeseen risks and have proven essential to banks' survival under conditions of stress. The second refers to the classical maturity process, through which resources undergo a transformation that ranges from short-term to long-term and contributes to financing the economy.

The first article, "Why do banks hold cash?", examined the hypothesis that cash optimality exists in the banking sector. To reach this goal, I first explored the empirical determinants of cash-holding for a large sample of listed banks over a period of 35 years.

Using the GMM-system estimation method, this study provides evidence that, in contrast to the nonbanking sector, there is no support for the dynamic optimal cash model in the banking industry. It confirms that agents in the banking sector face difficulties in determining their preferences and optimizing their utility. These results provide new insights into cash management in the banking sector and how it fundamentally differs from that of non-financial industries, offering additional clues as to how banks differ from non-banks.

The next articles focus on liquidity-creation by banks. The article, "Does an increase in capital negatively impact banking liquidity-creation?", isolates the effect of increasing levels of capital to determine which of the "risk absorption" or "crowding out" hypotheses [START_REF] Berger | Bank liquidity creation[END_REF]) prevails. The next and final article, "Positive effects of Basel III on banking liquidity creation", discusses the overall effects of Basel III on liquiditycreation.

The article, "Does an increase in capital negatively impact banking liquiditycreation?" yields several results. First, it is the only cross-national, empirical study documenting bank liquidity-creation over a period of 35 years and providing a unified view of banking liquidity-creation. Second, it confirms results presented in previous studies, which

show that liquidity-creation is determined not only by exogenous factors, such as monetary policy, but also by endogenous factors, such as bank size, equity levels and risk performance.

These findings have broad applications for policy, particularly for reducing inefficiencies in monetary policy transmission. Third, it confirms that an increase in equity levels has two consequences. The first is positive: risk reduction. The second is negative, involving a decline in liquidity-creation. However, taken alone, there is an overall negative effect from an increase in the level of equity.

The primary focus of the last article, "Positive effects of Basel III on banking liquidity creation" was to estimate the overall effects of Basel III, considering all components together. The primary method used to estimate the effects of Basel III is the "D-in-D" method, which, for robustness, has been complemented by additional analyses. The results indicate that during the period studied, all things being equal, the Basel III regulatory framework has not negatively impacted banking liquidity-creation in the U.S.

Overall, this thesis highlights the following key points:

The Basel III regulatory framework can be assimilated to a Pigouvian tax because it internalizes the social costs of financial instability to banks.

Liquidity in the banking sector can be defined as a function for optimal asset structure when bank managers face the dilemma of having to choose between allocating resources to liquid assets to reduce the costs of illiquidity or allocating resources to illiquid assets to finance projects and generate income. Given the market and regulatory constraints that banks face, this arbitrage on the asset side is a complex process that is not yet fully understood. Likely caused by the complexity and interdependence of constraints, the absence of a dynamic optimal cash model may indicate that bank managers use models that are similar to a "pecking order" approach, that is, by order of preference. This process echoes the "pecking order hypothesis" of [START_REF] Myers | Corporate financing and investment decisions when firms have information that investors do not have[END_REF], though the latter focuses on the liability side of corporations. For the sake of future financial stability, further studies on this issue should be conducted.

Finally, cash-holding and liquidity-creation by banks are affected by endogenous factors of banks, such as bank size, level of equity or risk performance. In this regard, this project has demonstrated that specific bank factors play a significant role in the transmission of monetary policy. How could a monetary policy account for these factors appropriately?

This question lies ahead to be addressed by future studies.

But exogenous variables such as regulatory frameworks affect the motives for banks to hold liquid assets or to create liquidity. A balkanized legal and fiscal environment may lead the banks to arbitrate their decisions, based on these differences and ultimately select countries which legal and fiscal frameworks is less compelling. In that sense, the results presented in the articles in this thesis argue in favor of more integrated legal and fiscal frameworks in Europe to mitigating these tradeoff effects.

Looking ahead to the next years, it is likely that the current liquidity ratios will be amended to include the endogeneity property and the stochastic movements of the market as proposed by the Liquidity Mismatch Measurement of [START_REF] Brunnermeier | Liquidity mismatch measurement[END_REF].

  Balkanized regulatory system. Alan Greenspan, Chairman of the Federal Reserve of the United States from 1987 to 2006. October 23 rd 2008 testimony before the U.S. House of Representatives, 110 th Congress.

  data for the banks were extracted from the Thomson Reuters (Bankscope) database on an annual basis for the last 35 years. Data on foreign exchange rates and GDP growth, at closing end of year since 1981, were derived from the IMF. From the original number of listed bank identifiers available from Thomson Reuters, I found 943 unique institutions. Each bank has a minimum of five reporting years, and the number of banks varies per year because newcomers come in and closed banks move out every year.

  in reference to China;[START_REF] Pana | Qep and bank liquidity creation: Evidence from japan[END_REF] in reference to Japan;[START_REF] Lakštutienė | Lithuanian banks liquidity creation in 2004[END_REF] in reference to Lithuania;[START_REF] Al-Khouri | Bank characteristics and liquidity transformation: The case of gcc banks[END_REF] in reference to Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates;[START_REF] Hackethal | Determinants of bank liquidity creation[END_REF] in reference to Germany; Fungáčová, Weill and Zhou (2010) and[START_REF] Fungáčová | Bank liquidity creation in russia[END_REF] in reference to Russia;[START_REF] Esterhuysen | Liquidity creation in south african banks under stressed economic conditions[END_REF] in reference to South Africa;[START_REF] Horváth | Bank capital and liquidity creation: Granger-causality evidence[END_REF] in reference to the Czech Republic. All of these authors measure bank liquidity creation and test the effects of certain factors on it. Capital, risk, and macroeconomic factors are nearly always considered in these studies. Performing the analyses presented in this paper on an international sample of banks is therefore not new.

  factors include the follow ing. Two environmental factors have been identified: (1) institutional regulatory framework and (2) macro-economic factors.Institutional regulatory framework: As argued by[START_REF] Berger | Bank risk taking and liquidity creation following regulatory interventions and capital support[END_REF], the supervisory regime can affect bank risk taking. To control this effect, and because regulations in this regard vary across countries and over time, I have created a supervisory index similar to that used by[START_REF] Shehzad | The impact of bank ownership concentration on impaired loans and capital adequacy[END_REF]. However, the World Bank's 2007 Regulation and Supervisory Database[START_REF] Barth | Bank regulations are changing: For better or worse ?[END_REF]) has been maintained over a relatively short period of time. For this reason, the index has been drawn from two sources and calculated for four periods corresponding to the following: (1) before Basel I, (2) from Basel I to II, (3) from Basel II to III and (4) since Basel III. For the before Basel I period, I follow La[START_REF] Porta | Legal determinants of external finance[END_REF], who established legal zones for measuring the effect of the legal environment on capital market development. To measure the effect of the legal environment on liquidity creation, I use a similar mapping exercise whereby a grade is used to represent the level of

  Macroeconomic factors: Following[START_REF] Imbierowicz | The relationship between liquidity risk and credit risk in banks[END_REF],[START_REF] Hackethal | Determinants of bank liquidity creation[END_REF] and[START_REF] Lei | Liquidity creation and bank capital structure in china[END_REF], I include GDP and the unemployment rate. Government monetary policy effects are also considered in this paper and are supported by two traits. As[START_REF] Bernanke | Inside the black box: The credit channel of monetary policy transmission[END_REF] established, a relatively low government-set interest rate encourages credit supply. Furthermore,[START_REF] Hackethal | Determinants of bank liquidity creation[END_REF] established the close relationship between liquidity and risk transformation. A relaxed monetary policy ends by positively increasing bank liquidity and risk transformation capabilities. The relationship between monetary policy and liquidity creation is thus in opposition in that a decrease in the interest rate is expected to increase liquidity creation. Because the money supply is typically ensured by the government interest rate, in this paper, the 10-year government bond rate and the LT-ST spread are used to assess the effect of a country's monetary policy on liquidity creation in the banking sector. The use of this metric is justified by the sensitivity of bank lending/borrowing to this maturity yield and by the effect of the ST-LT spread structure on a bank's profit margin[START_REF] Bernanke | The financial accelerator in a quantitative business cycle framework[END_REF]). In addition, to control for the "interbank liquidity shock effect," I follow[START_REF] Cornett | Liquidity risk management and credit supply in the financial crisis[END_REF] and for each country use the TED spread indicator, which is the spread between interbank loans and government bonds with maturities of 3 months or thereabouts. Other macroeconomic metrics are also utilized: GDP growth and the unemployment rate, which assumes that a positive economic context encourages investment, which in turn triggers loan demand. To control for market competition and following[START_REF] Lei | Liquidity creation and bank capital structure in china[END_REF], I include the bank-level Herfindahl market competition index (HHI). Based on the loan market share for each country where bank headquarters are located, this variable proxies for market concentration. As shown by[START_REF] Beck | Bank competition and access to finance: International evidence[END_REF], market concentration impacts the availability of credit and therefore may affect liquidity creation.

  records a mean of 0.20, a median of 0.19 and a standard deviation of 0.16. The difference of the standard is due to the fact that the authors of the LT Gap used quarterly data, whereas I used annual data. For a similar period (1993-2003) and US banks, the MatNonFat measure computed with the aforementioned protocol has a mean of 0.25 in 1993 and of 0.27 in 2003, whereas the CatNonFat 9 measure computed by the authors Berger and Bouwman (2009) record 0.22 and 0.26 respectively. Note that the authors do show only means of CatNonFat measures, making it difficult to conduct a strict comparison. Median and standard were not indicated in their article.

  over time. The figure below illustrates the evolution of liquidity creation for each year and geographic zone as established by the LGM and BB-MatNonFat measures. Values are expressed in US$ billion, counter valued at the end-of-year IMF foreign exchange rate and deflated based on the IMF inflation index (base 2005).

  1981 and 2004 increased steadily from $775bln and $632bln to $4 244bln and $2 928bln for the LGM and BB-MatNonFat measures, respectively. However, in 2005-2006, liquidity creation suddenly decreased to $3 143bln and $1 622bln. This diminution occurred as a result of the conjunction of two main factors. First, the EUR/USD rate reached a historical low of 1.16 in December of 2005. The rejection of a referendum on the EC Constitution by France and the Netherlands, the suspension of the UK's entry into the euro zone and the increase of 200 basis points to the FED funds rate may explain this rate variance. Second, French banks hoarded a substantial amount of cash and assimilated in 2005; between 2004 and 2005, cash and assimilated holdings increased from €572bln to €1 610bln (not shown in the tables). This substantial increase in liquid assets on the balance sheets of the French banks caused a sudden upsurge in liquidity destruction, and both the effect of the exchange rate and the relative increase in liquidity destruction largely explain the overall decline in liquidity creation in Europe in 2005-2006. Between 2007 and 2012, liquidity creation varied from $5 015bln and $2 511bln to $6 243bln and $3 584bln for the LGM and BB-MatNonFat measures, respectively. Between 2013 and 2014, signals of financial recovery appeared to be somehow present with increases to $6 104bln and $3 842bln in 2013, but decreases to $5 162bln and $3 176bln in 2015 for the LGM and BB-MatNonFat measures, respectively. Over this period, liquidity creation multiplied seven-and five-fold for the LGM and BB-MatNonFat measures, respectively.

  -12.5%), this value decreases from 0.212** to -0.31*** and then increases to -0.29*** at Equity level 4 (12.5-15%) and decreases to-0.360** at Equity level 5 (15-17.5%) for the LTGap (column 10). Between Equity level 2 (7.5-10%) and Equity level 4 (12.5-15%), the estimates decrease steadily from -0.351*** to -0.508*** and increase to -0.340** at Equity level 5 (15-17.5%) for the LGM (column 11). Between Equity level 2 (7.5-10%) and Equity level 4 (12.5-15%), the estimates decrease steadily from -0.463*** to -0.612*** and increase to -0.548*** at Equity level 5 (15-17.5%) for the BB-MatNonFat (column 12).

  indicated for. Within a context of scarce resources, the allocation of liquid liabilities to illiquid liabilities mechanically increases liquidity destruction. These results complement those of Fungáčová, Weill and Zhou (2010); Horváth, Seidler and Weill (2014) and Lei and Song (2013), who show that more stringent bank capital requirements implemented to support financial stability among Russian, Czech and Chinese banks, respectively, have harmed liquidity creation. This effect is however more pronounced for Asian & Pacific and North American banks than their European peers. The heterogeneity of the banking regulation and the implementation of Basel I and II, is a factor explaining potentially this difference.

Figure 1 :

 1 Figure 1: Liquidity created per measure, year and geographic zone The figure below displays liquidity levels generated for each year and geographic zone. The first graph shows LGM values, and the second graph shows the BB-MatNonFat measures. Amounts were deflated from the IMF index inflation rate for each country (base 2005) and were counter revaluated at the US$ IMF foreign exchange rate for each end-of-year period. They are expressed in billions of US$.

Figure 2 :

 2 Figure 2: Sensitivity analysis comparison for the effect of a marginal increase in the level of equity on liquidity creation and risk performance

  Key words: Difference-in-difference, Cox, Survival, GMM, Basel III, Banking liquidity. JEL Classifications: C3 -Econometric Methods: Multiple/Simultaneous Equation Models; C53 -Forecasting and Other Model Applications; G21 -Banks; Other Depository Institutions; Mortgages.

3. 3

 3 Literature review on the effects of the banking regulation on bank's liquidity creation and brief presentation of Basel III The first part of this section reviews the literature on the effects of the banking regulation on the liquidity creation by banks. The second and last part presents the components of the Basel III regulatory framework. 3.3.1 Literature review on the effects of banking regulation on liquidity creation by banks

  assess the impact of regulatory interventions and capital support banking regulation in Germany on a panel of 2789 banks between 1999 and 2009; Horváth, Seidler and Weill (2014) examine the effect of Basel III on 31 Slovakian banks between 2000 and 2010, and Lei and Song (2013) study its effect on the capital structure of 135 Chinese banks between 1998 and 2009; Lakštutienė, Krušinskas and Rumšaitė (2011) explore the effect of deposit insurance systems on 8 Lithuanian banks between 1995 and 2008; and Fungáčová, Weill and Zhou (2010) study the implementation of deposit insurance by Russian banks between 1999 and 2007. Therefore, evaluating a regulatory framework's effects on banking activity is not new. However, based on the literature review, there is no study that estimates the effects of all component taken together of the Basel III regulatory framework on banking liquidity creation based on a broad set of methods. By analyzing the impact of Basel III on a panel of US listed banks between 2009 and 2015 and by using a set of various methods to estimate the effect of a policy or a treatment, this paper addresses this gap. The set of methods used is detailed in the next section. 3.3.2 Summary of Basel III This section discusses summarizes the Basel III regulatory framework. The first part summarizes the historical background of Basel III, the second and last part presents the components of Basel III.

3. 4

 4 Bank sample, methodology and econometric model This section begins with a short description of the data sources and of the panel representativeness. The second part presents the methodology used to estimate the effect of Basel III on bank liquidity creation. The third and last part is a descriptive statistics of the liquidity creation of the listed banks in the US market from 2009 to 2015. 3.4.1 Bank sample and the fraction of the market covered This section shows the fraction of the market covered by the sample used in this research. The data were extracted from Thomson Reuters. Based on the original number of listed bank identifiers available in Thomson Reuters, 555 17 listed banks have been selected.

  in reference to China;[START_REF] Pana | Qep and bank liquidity creation: Evidence from japan[END_REF] in reference to Japan;[START_REF] Lakštutienė | Lithuanian banks liquidity creation in 2004[END_REF] in reference to Lithuania; Al-Khouri (2012) in reference to Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates;[START_REF] Hackethal | Determinants of bank liquidity creation[END_REF] in reference to Germany; Fungáčová, Weill and Zhou (2010) and[START_REF] Fungáčová | Bank liquidity creation in russia[END_REF] in reference to Russia;[START_REF] Esterhuysen | Liquidity creation in south african banks under stressed economic conditions[END_REF] in reference to South Africa;[START_REF] Horváth | Bank capital and liquidity creation: Granger-causality evidence[END_REF] in reference to the Czech Republic. All of these authors measure banking liquidity creation and test the effects of specific factors on it. Capital, risk, and macroeconomic factors are nearly always considered in these studies.

Figure 3 :

 3 Figure 3: Evolution of liquidity creation-treated vs. untreated The graphs below show the evolution of liquidity creation from two perspectives. The figure on the left side displays the evo lution of the LTGap measure and BB-MatNonFat measure scaled over assets. It displays the results from 2009 to 2015 for both the treated and the untreated bank populations. The figure on the right side displays the evolution of the BB-MatNonFat measure from 2009 to 2015 in US$ billions for both the treated and the untreated populations. Data correspond to the averages displayed in Table38.

Figure 4 :

 4 Figure 4: Actuarial method -Estimated effects of the new banking regulation on liquidity creation: Untreated vs. treated banks for the LTGap and BB-MatNonFat measures

EQUATION 1 :

 1 ........................................................................................................................................................ EQUATION 2 ......................................................................................................................................................... EQUATION 3 ......................................................................................................................................................... EQUATION 4 ......................................................................................................................................................... EQUATION 5 ......................................................................................................................................................... EQUATION 6......................................................................................................................................................... EQUATION 7 ......................................................................................................................................................... EQUATION 8 ......................................................................................................................................................... EQUATION 9 ....................................................................................................................................................... EQUATION 10 ..................................................................................................................................................... EQUATION 11 ..................................................................................................................................................... EQUATION 12 .....................................................................................................................................................

Table 2 :

 2 every year from 2005 to 2014 are displayed. Fraction of the market covered by the sample

	Region -Country						Year
		1981	1985 1990	1995	2000	2005	2010 2011	2012	2013
	Asia & Pacific	14	34	61	84	108	153	166	166	166	166
	Australia	3	3	4	6	7	7	7	7	7	7
	China	0	0	1	1	2	10	11	11	11	11
	India	0	0	0	0	10	30	36	36	36	36
	Japan	11	31	56	73	74	80	84	84	84	84
	South Korea	0	0	0	0	2	5	7	7	7	7
	Taiwan	0	0	0	3	12	20	20	20	20	20
	Turkey	0	0	0	1	1	1	1	1	1	1
	Europe	17	26	69	84	120	134	145	142	141	131
	Austria	0	0	5	6	6	6	6	6	6	6
	Belgium	1	1	1	1	2	2	2	2	2	2
	Cyprus	0	0	0	0	0	0	1	1	1	1
	Czech Republic	0	0	0	0	1	1	1	1	1	1
	Denmark	1	1	16	18	24	24	24	24	24	19
	Finland	0	0	2	2	2	2	2	2	2	2
	France	2	3	5	5	6	6	6	6	6	6
	Germany	2	3	4	4	6	9	10	10	9	9
	Greece	0	2	3	5	6	9	9	6	6	6
	Hungary	0	0	0	0	1	1	1	1	1	1
	Ireland	1	1	2	2	2	2	2	2	2	2
	Italy	2	3	7	10	11	13	13	13	13	13
	Netherlands	0	0	0	0	1	1	1	1	1	1
	Norway	0	0	2	4	16	16	21	21	21	20
	Poland	0	0	0	2	7	9	9	9	9	9
	Slovakia	0	0	0	0	1	3	4	4	4	3
	Slovenia	0	0	0	0	0	0	2	2	2	0
	Sweden	3	3	3	4	4	4	4	4	4	4
	Switzerland	1	5	15	17	20	22	22	22	22	21
	United Kingdom	4	4	4	4	4	4	5	5	5	5
	North America	29	37	92	262	459	535	546	540	521	510
	Canada	5	5	8	8	9	9	9	9	9	9
	United States	24	32	84	254	450	526	537	531	512	501
	Total général	60	97	222	430	687	822	857	848	828	807

Table 5 :

 5 Evolution of aggregated cash holdingper type of bank

							Large banks
	Period		Cash ratio Asia & Pacific Paid out	LR	N	Cash ratio Europe Paid out	LR	N	Cash ratio North America out Paid	LR
	1	26	27	28	29	30	31	32	33	34	35	36	37
	1981-1988
	1989-2007 49 40%	3% 562% 201 41%	5% 1093% 63 40%	6% 327%
	2008-2010 35 38%	4% 417% 79 46%	3% 1075% 33 36%	5% 268%
	2011-to date 73 39%	4% 368% 96 44%	2% 725% 44 35%	4% 187%

Table 6 :

 6 Summary of variables (part 1)

	Bln

Table 7 :

 7 Summary of variables (part 2)This table sums up the discussion above and shows the exhaustive list of variables used in this research. The first column indicates the motive, the second the variable name, the third the corresponding code used in the regression models, the fourth whether the variable has been scaled under a Siegel-Tukey test, the fifth the predictive sign and the sixth how the variable has been computed (Thomson Reuters' code). Equity (item WC03995) represents the sum of Preferred Stock and Common Shareholders' Equity.

	Motive	Variable name	Variable name in the OLS	Tukey' scale type	Predictive sign	How this variable is computed
	1	2	3	4	5	6
						Cash dividends (item WC04551) over Shareholders' equity (item WC03995).
						Cash dividends (item WC04551) represents the total common and preferred dividends paid to shareholders of the company.
						It excludes:
						Dividends paid to minority shareholders
	5. Agency	Dividend paid out over shareholders' equity ratio	X7PaisShroEq	NA	+	Footnotes:
						A. Included in other sources or uses
						B. Includes bonuses to directors
						C. Prior year's proposed dividend
	Total Shareholder's Endogenous Bank size X8BkSUSD Log + The firm size is measured as follow in this research: Log Total Assets (item WC02999).
		Governement monetary policy. Government 10
	Exogeneous	years rate : The cash holding creation may depend	X9Rf	Y (Sqrt)	-	The government bond rate of where is located the head quarter of the bank.
		on the monetary policy of country.
	Exogenous	Macroeconomic environment -GDP growth	X10GDPgwth	NA	+	The GDP growth corresponds to the yearly growth of GDP of the country in which the bank is located. Source : International Monetary Fund, World Economic Outlook Database, October 2014.
	Control variable 1	Liquidity shock	X11TED	NA	+
						Jense's Excess Return
	Control variable 2	Excess Return	X12ExccsR	NA	+

Spread between interbank loan 3 months and free risk rate 3 months. For all countries : yearly average of Interbank loan 3 months -T-bills 3 months. Exceptions : For Japan yearly average of interbank 1M -Gensaki T-bills 1 month. For Australia and Chile yearly average of Interbank O/N -T-Bills 3 month

Table 8 :

 8 Regression models per period -All banks(Basel period) This table presents the results of the regression models for all banks per Basel and calendar period. Column 1 shows the cash holding motives, and column 2 the corresponding independent variables. The next columns show the estimated coefficients with their level of confidence. T-tests are reported in parentheses. The symbols ***, ** and * indicate significance levels at 0.1%, 1% and 5%, respectively. Column 3 shows the results for all banks. Columns 4 to 6 show the result per Basel period(Basel I = 1998[START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF], Basel II = 2008-2010, and Basel III = 2011-2014).

	All
	Unstandardized coefficient	Basel I	Basel II	Basel III

Table 9 :

 9 Regression models per period -All banks (ten-years period and fixed effect)This table presents the results of the regression models for all banks per Basel and calendar period. Column 1 shows the cash holding motives, and column 2 the corresponding independent variables. The next columns show the estimated coefficients with their level of confidence. T-tests are reported in parentheses. The symbols ***, ** and * indicate significance levels at 0.1%, 1% and 5%, respectively. Column 3 shows the results for all banks. Columns 4 to 6 show the result per Basel period (Basel I =

Table 10 :

 10 Regression models per period -Asian and Pacific banks(Basel period) This table presents the results of the regression models for Asian and Pacific banks per Basel and calendar period. Column 1 shows the cash holding motives, and column 2 the corresponding independent variables. The next columns show the estimated coefficients with their level of confidence. T-tests are reported in parentheses. The symbols ***, ** and * indicate significance levels at 0.1%, 1% and 5%, respectively. Columns 3 and 4 show the unstandardized and standardized beta coefficients, respectively, for Asian and Pacific banks. Columns 5 to 7 show the result per Basel period(Basel I = 1998[START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF], Basel II = 2008-2010, and Basel III = 2011-2014).

	1981-1990	1991-2000	2001-2010	2011-2014	Per bank	Per country

Table 11 :

 11 Regression models per period -Asian and Pacific banks (ten-years period and fixed effect)This table presents the results of the regression models for Asian and Pacific banks per Basel and calendar period. Column 1 shows the cash holding motives, and column 2 the corresponding independent variables. The next columns show the estimated coefficients with their level of confidence. T-tests are reported in parentheses. The symbols ***, ** and * indicate significance levels at 0.1%, 1% and 5%, respectively. Columns 8 to 11 show the results per ten-year period

Table 12 :

 12 Regression models per period -European banks (Basel period)This table presents the results of the regression models for European per Basel and calendar period. Column 1 shows the cash holding motives, and column 2 the corresponding independent variables. The next columns show the estimated coefficients with their level of confidence. T-tests are reported in parentheses. The symbols ***, ** and * indicate significance levels at 0.1%, 1% and 5%, respectively. Columns 3 and 4 show the unstandardized and standardized beta coefficients, respectively, for European banks. Columns 5 to 7 show the result per Basel period(Basel I = 1998[START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF], Basel II = 2008-2010, and Basel III = 2011-2014).

	1981-1990	1991-2000	2001-2010	2011-2014	Per bank	Per country

Table 13 :

 13 Regression models per period -European banks (ten-years period and fixed effect)This table presents the results of the regression models for European per Basel and calendar period. Column 1 shows the cash holding mot ives, and column 2 the corresponding independent variables. The next columns show the estimated coefficients with their level of confidence. T-tests are reported in parentheses. The symbols ***, ** and * indicate significance levels at 0.1%, 1% and 5%, respectively. Columns 8 to 11 show the results per ten-year period

Table 14 :

 14 Regression models per period -North American banks(Basel period) This table presents the results of the regression models for North American banks per Basel and calendar period. Column 1 shows the cash holding motives, and column 2 the corresponding independent variables. The next columns show the estimated coefficients with their level of confidence. T-tests are reported in parentheses. The symbols ***, ** and * indicate significance levels at 0.1%, 1% and 5%, respectively. Columns 3 and 4 show the unstandardized and standardized beta coefficients, respectively, for North American banks. Columns 5 to 7 show the result per Basel period(Basel I = 1998[START_REF] Wagner | The liquidity of bank assets and banking stability[END_REF], Basel II = 2008-2010, and Basel III = 2011-2014).

	1981-1990	1991-2000	2001-2010	2011-2014	Per bank	Per country

Table 15 :

 15 Regression models per period -North American banks (ten-years period and fixed effect)

  Thomson Reuters database correspond to those classified in ICB As the aforementioned breakdown was not provided, I followed[START_REF] Imbierowicz | The relationship between liquidity risk and credit risk in banks[END_REF] and based the bank typologies on three inherent characteristics: bank size, risk level and the level of capital. would have inflated the results otherwise. But this exclusion does not obstruct the results because the panel is sufficiently representative. As can be observed in the table21below, the panel dataset indeed covers a substantial fraction of Asia & Pacific, European and US market.

	The original number of unique listed banks extracted from Thomson Reuters is 940,
	with the minimum and maximum numbers of years ranging from five to 35. The table below
	shows a breakdown of the number of banks per geographic zone, country and year, with the
	highest number of banks (857) recorded in 2010. North American listed banks include 537
	US banks and nine Canadian banks, the latter of which represent a minor portion of this
	geographic zone. A total of 166 Asia-Pacific banks are listed, with Japan, India, Taiwan and
	China accounting for 84, 36, 20 and 11 listed banks, respectively. A total of 145 are European
	banks, with Denmark, Switzerland and Norway accounting for 24, 22 and 21 listed banks,
	respectively.
	Following Berger and Bouwman (2008), I include data from consolidated
	industry sector 800 as "Banks providing a broad range of financial services, including retail
	banking, loans and money transmissions," which may aggregate different lines of business financial statement of mother companies, which includes the financial statement of branches
	corresponding to various banking activities (e.g., M&A, commercial banks) or banks with and subsidiaries but eliminates intragroup activities. By excluding subsidiaries and branches, I
	avoid double counting the liquidity created by banks and for banks that belong to the same
	group, which

state involvement (partially state-owned banks, fully private banks).

Because the panel data set is composed uniquely of listed banks, it is dominated by US banks that represent 60% of the number of banks toward the end of the sample period. However, 95 % of the US banks have a small size of less than $10 bln and represent around 15% of the sum of assets of all US banks (figures not displayed). Besides, the coverage of the initial year is rather limited to 60 banks in 1981. This coverage is much improved in 2000 with 687 banks. The results are thus broken down per geographical zones (Asia & Pacific, Europe and North America) per period and types bank (small, medium and large).

  β1 Bank size i,t + β2 Std. dev. RoE i,t + β3 Equity scaled over asset i,t + β4 Non performing loans over loans i,t + β5 Z-score i,t + β6 cash dividend paid over shareholder's equity i,t + β7 Loan growth i,t + β8 Jensen's excess return i,t + β9 Operating income over assets i,t + β10 Market value per share i,t + β11 10-years bond yield j,t + β12 Institutional regulatory framework j,t + β13 GDP growth j,t + β14 HHI market concentration index j,t + β15 LT-ST yield curve spread j,t + β16 Unemployment rate j,t + β17 Interbank liquidity shock TED j,t + µ

  , although their study focused only on German banks during the period 1997-2006. For instance, they found that the Table28shows the coefficient of the other variables for the standard OLS. Monetary policy plays a significant role in liquidity creation. The 10-year bond yield significantly affects the LTGap, with

	liquidity creation estimator at the 2 nd correlation had a coefficient of 0.842*** and of
	0.938*** for the LTGap and BB (not indicated whether MatNonFat or CatNonFat),
	respectively, whereas I found European peers coefficients of 0.910*** and 0.906*** for the
	LTGap and BBMatNonFat, respectively. The results for other countries are also similar to
	these coefficients, with 0.864*** and 0.809*** found for LTGap values and with 0.910***
	and 0.878*expected negative coefficients of -1.256***, -0.619*** and -0.815*** found for the Asia-
	Pacific region, Europe and North America, respectively (table 28, columns 4, 6

** found for BBMatNonFat values for the Asia-Pacific region and North America, respectively

(table 26, columns 6-8)

. Overall, this indicates that liquidity created at time t-1 has an autoregressive effect. The autoregressive effect can be intuitively attributed to traditional banking model contributions to the stable structure of commercial banks: When a loan is granted to a customer, it is typically allocated for a number of years. The customer may repatriate extra resources that are deposited and may open a current account, which may in turn be used to finance new loans for the same customer or for other customers. However, this autoregressive effect remains relatively low and below 0.5 for all regions (i.e., 1-0.910 and 1-0.906 for LTGap and BBMatNonFat of European banks, respectively)

12

. It results that this autoregressive effect is not sufficient to harm the OLS specification.

Table 20 :

 20 Liquidity creation measure results compared with those of other articlesThe table below compares LTGap and B-B CatNonFat liquidity creation measures for US, German and Chinese banks. Note that I only present the MatNonFat measure, whereas other researchers show only CatNonFat measures, making it difficult to conduct a strict comparison. The differences found are in line with the results of[START_REF] Berger | Bank liquidity creation[END_REF], wherein CatNonFat values are generally lower than MatNonFat values.

			LTGap		Liquidity Creation-Gross measure : "LGM"		BB MatNonFat
	Liquidity level	Asset		Liabilities		Asset		Liabilities		Asset		Liabilities
		Item	Weight	Item	Weight	Item	Weight	Item	Weight	Item	Weight	Item	Weight
	1	2	3	4	5	6	7	8	9	10	11	12	13
		WC02205 -Treasury				WC02205 -Treasury				WC02205 -Treasury
		Securities				Securities				Securities
		WC02208 -Trading				WC02208 -Trading				WC02208 -Trading
	Liquid	account Securities WC02004 -Cash and	-1	WC03019 -Cust Dep	1	account Securities WC02004 -Cash and	-0.5	WC03019 -Cust Dep	0.5	account Securities WC02004 -Cash and	-0.5	WC03019 -Cust Dep	0.5
		Due from Banks				Due from Banks				Due from Banks
		WC02055 -Interbank				WC02055 -Interbank				WC02055 -Interbank
		Loans				Loans				Loans
	Semi liquid	NA	NA	NA	NA	NA	NA	NA	NA	WC02055 -Interbank Loans	0	WC03051 -Total ST debts	0
	Illiquid	NA NA	NA NA	NA NA	NA NA	WC02271 -Loans	0.5	WC03995 -Total Shareholder's Equity	-0.5	WC02271 -Loans	0.5	WC03251 -LT Debts WC03995 -Total Shareholder's Equity	-0.5
	Scaled over asset					WC02999 -Total Assets

Table 21 :

 21 List of banksThe table below lists the number of banks. The first column denotes the geographic zone and country. The second column shows the number of banks per year. Data for the following years are displayed:1981, 1990, 2000, 2010 and 2014.

	Region -Country	1981	1990	2000	2014
	Asia & Pacific	14	61	108	157
	Australia	3	4	7	6
	China		1	2	7
	India			10	36
	Japan	11	56	74	83
	South Korea			2	6
	Taiwan			12	18
	Turkey			1	1
	Europe	17	69	120	109
	Austria		5	6	3
	Belgium	1	1	2	2
	Cyprus				1
	Czech Republic			1	1
	Denmark	1	16	24	16
	Finland		2	2
	France	2	5	6	5
	Germany	2	4	6	6
	Greece		3	6	4
	Hungary			1	1
	Ireland	1	2	2	2
	Italy	2	7	11	12
	Netherlands			1
	Norway		2	16	17
	Poland			7	8
	Slovakia			1	3
	Slovenia
	Sweden	3	3	4	4
	Switzerland	1	15	20	19
	United Kingdom	4	4	4	5
	North America	29	92	459	438
	Canada	5	8	9	9
	United States	24	84	450	429
	Total	60	222	687	704

Table 27 :

 27 GMM estimators per liquidity creation measure and geographic zone -with z-scores but without the std. dev. of RoE and op. income over assets

β1

  Bank size i,t + β2 Equity scaled over asset i,t + β3 cash dividend paid over shareholder's equity i,t + β4 Non performing loans over loans i,t + β5 Z-score i,t + β6 Loan growth i,t + β7 Jensen's excess return i,t + β8 Market value per share i,t + β9 LT-ST yield curve spread j,t + β10 GDP growth j,t + β11 Unemployment rate j,t + β12 HHI market concentration

	index j,t +ε

Table 31 :

 31 Summary of the types of banks potentially affected by the new liquidity ratio requirements. table summarizes the Basel III major high-quality liquid assets (HQLA) required for the liquidity coverage ratio (LCR). The first column displays the bank size in US$ billions and its equivalent. The second column shows the number of days required by the LCR. Source: BIS.

	This

Table 32 :

 32 Summary of the LCR implementation schedule. table below shows the main schedule of the LCR deployment. The first column displays the minimum LCR requirement; the next columns display this minimum in % per year: 2015[START_REF] Bancel | Focus on basel 3 and bank liquidity creation[END_REF], 2017, 2018, and 2019. Source: BIS.

	Less than 50 and shadow banks Between 50 and 250 The Bank size (in $ Bn or equivalent) LCR ratio requirements Reprieve 21 days More than 250 30 days
	Year	2015	2016	2017	2018	2019
	Minimum LCR requirement	60%	70%	80%	90%	100%

Table 33 :

 33 Data setnumber of banks per geographical zone and year

Table 34 :

 34 Fraction of the market covered by the sampleThe table below shows the fraction of the market covered by the sample based on 2011 figures. The first column indicates the country, for instance, the United States. Columns 2 and 3 display the number of US banks, the total deposits and total loans in US$ billions from the current sample of Thomson Reuters. Columns 6 and 7 display the total deposits and total loans in the U.S., as recorded by the DIC and the American Bankers Association, respectively. Columns 8 and 9 display the % of total deposits and total loans covered by the current sample over the total recorded by the American Bankers Association.

	Year		Number of banks
		Untreated Treated	Total
	2009	532	16	548
	2010	521	16	537
	2011	516	15	531
	2012	497	15	512
	2013	486	15	501
	2014	414	15	429
	2015	362	15	377

Table 35 :

 35 Items for computing the liquidity creation measures

  , who find that CatNonFat values are generally lower than MatNonFat values

					LTGap								BB MatNonFat
	Liquidity level		Asset			Liabilities				Asset	Liabilities
		Item	Weight	Item	Weight		Item		Weight	Item	Weight
	1		2		3		4			5		6		7	8	9
		WC02205 -Treasury							WC02205 -Treasury
		Securities							Securities
		WC02208 -Trading							WC02208 -Trading
	Liquid	account Securities WC02004 -Cash and	-1	WC03019 -Cust Dep	1	account Securities WC02004 -Cash and	-0.5	WC03019 -Cust Dep	0.5
		Due from Banks							Due from Banks
		WC02055 -Interbank							WC02055 -Interbank
		Loans									Loans
	Semi liquid		NA	NA		NA		NA	WC02055 -Interbank Loans	0	WC03051 -Total ST debts	0
			NA	NA		NA		NA				WC03251 -LT Debts
	Illiquid		NA	NA		NA		NA	WC02271 -Loans	0.5	Shareholder's Equity WC03995 -Total	-0.5
	Scaled over asset								WC02999 -Total Assets
		Deep and Schaefer		Berger and Bouwman			Hackethal et al.	Lei and Song
			(2004)			(2009)				(2010)	(2013)
			US banks			US banks				German banks	Chinese banks
			1997-2001			1993 -2003				1997 -2006	1988 -2008
		Their article This article	Their article	This article		Their article	This article	Their article This article
		Quarter -all periods	Annual -all periods	1993	2003	1993	2003	BB -CatNonFat	LT Gap	BB-MatNonFat	LT Gap	BB -CatNonFat	BB-MatNonFat
	1	2		3	4	5	6	7		8	9	10	11	12	13
	Mean		0.20	0.17	0.22	0.26	0.25	0.28	0.18	0.14		0.29	0.11	0.28	0.37
	Median		0.21	0.18 NA	NA	0.27	0.30	0.18	0.13		0.28	0.11 NA	0.37
	Standard deviation	0.16	0.08 NA	NA	0.09	0.10	0.09	0.02		0.22	0.24 NA	0.05

Table 38 :

 38 Comparative evolution of liquidity creation indicators: Untreated, treated and all banks

Table 42 :

 42 Cox regression for the LTGap and BB-MatNonFat measures

  Tables des graphes, des tables, des équations et références bibliographiquesTables des graphesFIGURE 1: LIQUIDITY CREATED PER MEASURE, YEAR AND GEOGRAPHIC ZONE ......................................................................FIGURE 2: SENSITIVITY ANALYSIS COMPARISON FOR THE EFFECT OF A MARGINAL INCREASE IN THE LEVEL OF EQUITY ON LIQUIDITY CREATION AND RISK PERFORMANCE.................................................................................................................FIGURE 3: EVOLUTION OF LIQUIDITY CREATION-TREATED VS. UNTREATED ........................................................................FIGURE 4: ACTUARIAL METHOD -ESTIMATED EFFECTS OF THE NEW BANKING REGULATION ON LIQUIDITY CREATION: UNTREATED VS. TREATED BANKS FOR THE LTGAP AND BBMATNONFAT MEASURES .......................................................................

166

Table

des tables

TABLE 1 :

 1 BREAKDOWN OF THE NUMBER OF BANKS PER GEOGRAPHICAL ZONE AND COUNTRY ...................................................

TABLE 2 :

 2 FRACTION OF THE MARKET COVERED BY THE SAMPLE .........................................................................................

TABLE 3 :

 3 COMPARED EVOLUTION OF THE CASH AND PAID OUT OVER EQUITY AND LEVERAGE RATIOS -PER GEOGRAPHICAL ZONE, TYPE OF BANK AND PERIOD -SMALL AND MEDIUM BANKS ............................................................................................

TABLE 4 :

 4 COMPARED EVOLUTION OF THE CASH AND PAID OUT OVER EQUITY AND LEVERAGE RATIOS -PER GEOGRAPHICAL ZONE, TYPE OF BANK AND PERIOD -LARGE BANKS ...............................................................................................................

TABLE 5 :

 5 EVOLUTION OF AGGREGATED CASH HOLDING -PER TYPE OF BANK ........................................................................

TABLE 6 :

 6 SUMMARY OF VARIABLES (PART 1) ................................................................................................................

TABLE 7 :

 7 SUMMARY OF VARIABLES (PART 2) ................................................................................................................

TABLE 8 :

 8 REGRESSION MODELS PER PERIOD -ALL BANKS (BASEL PERIOD) ...........................................................................

TABLE 9 :

 9 REGRESSION MODELS PER PERIOD -ALL BANKS (TENYEARS PERIOD AND FIXED EFFECT) .............................................

TABLE 10 :

 10 REGRESSION MODELS PER PERIOD -ASIAN AND PACIFIC BANKS (BASEL PERIOD) ....................................................

TABLE 11 :

 11 REGRESSION MODELS PER PERIOD -ASIAN AND PACIFIC BANKS (TENYEARS PERIOD AND FIXED EFFECT) ....................... 65

TABLE 12 :

 12 REGRESSION MODELS PER PERIOD -EUROPEAN BANKS (BASEL PERIOD) ................................................................

TABLE 13 :

 13 REGRESSION MODELS PER PERIOD -EUROPEAN BANKS (TENYEARS PERIOD AND FIXED EFFECT) .................................. 67

TABLE 14 :

 14 REGRESSION MODELS PER PERIOD -NORTH AMERICAN BANKS (BASEL PERIOD) ......................................................

TABLE 15 :

 15 REGRESSION MODELS PER PERIOD -NORTH AMERICAN BANKS (TENYEARS PERIOD AND FIXED EFFECT) ........................ 69

TABLE 16 :

 16 GMM ESTIMATION OF DYNAMIC CASH HOLDING -PROPENSITY TO REVERT TO OPTIMAL CASH COMPARED PER REGION AND BANK SIZE ...................................................................................................................................................

TABLE 17 :

 17 SUMMARY OF VARIABLES (PART 1) ............................................................................................................

TABLE 18 :

 18 SUMMARY OF VARIABLES (PART 2) ............................................................................................................

TABLE 19 :

 19 ITEMS FOR COMPUTING THE THREE LIQUIDITY CREATION MEASURES ..................................................................

TABLE 20 :

 20 LIQUIDITY CREATION MEASURE RESULTS COMPARED WITH THOSE OF OTHER ARTICLES ............................................

TABLE 21 :

 21 LIST OF BANKS ......................................................................................................................................

TABLE 22 :

 22 FRACTION OF THE MARKET COVERED BY THE SAMPLE .....................................................................................

TABLE 23 :

 23 LIQUIDITY CREATED OVER ASSET MEASURES PER PERIOD AND GEOGRAPHIC ZONE. .................................................

TABLE 24 :

 24 COMPARATIVE EVOLUTION OF EQUITY LEVEL, RISK PERFORMANCE, AND LIQUIDITY CREATION MEASURES ....................

TABLE 25 :

 25 CORRELATION MATRIX ............................................................................................................................

TABLE 26 :

 26 GMM ESTIMATORS PER LIQUIDITY CREATION MEASURE AND GEOGRAPHIC ZONE -WITHOUT ZSCORES.....................

TABLE 27 :

 27 GMM ESTIMATORS PER LIQUIDITY CREATION MEASURE AND GEOGRAPHIC ZONE WITH ZSCORES BUT WITHOUT THE STD. DEV. OF ROE AND OP. INCOME OVER ASSETS .....................................................................................................

TABLE 28 :

 28 DETERMINANTS OF LIQUIDITY CREATION OLS AND FIXED EFFECTS ESTIMATORS PER COUNTRY ..................................

TABLE 29 :

 29 COX REGRESSION (SURVIVAL ANALYSIS).......................................................................................................

TABLE 30 :

 30 KEY ASPECTS OF BASEL III ........................................................................................................................

TABLE 31 :

 31 SUMMARY OF THE TYPES OF BANKS POTENTIALLY AFFECTED BY THE NEW LIQUIDITY RATIO REQUIREMENTS. .................

TABLE 32 :

 32 SUMMARY OF THE LCR IMPLEMENTATION SCHEDULE. ....................................................................................

TABLE 33 :

 33 DATA SET -NUMBER OF BANKS PER GEOGRAPHICAL ZONE AND YEAR .................................................................

TABLE 34 :

 34 FRACTION OF THE MARKET COVERED BY THE SAMPLE .....................................................................................

TABLE 35 :

 35 ITEMS FOR COMPUTING THE LIQUIDITY CREATION MEASURES ...........................................................................

TABLE 36 :

 36 THE RESULTS OF THE LIQUIDITY CREATION MEASURES COMPARED WITH OTHER ARTICLES ........................................

TABLE 37 :

 37 DEFINITION OF POPULATION TREATED VS. POPULATION UNTREATED ..................................................................

TABLE 38 :

 38 COMPARATIVE EVOLUTION OF LIQUIDITY CREATION INDICATORS: UNTREATED, TREATED AND ALL BANKS ....................

TABLE 39 :

 39 LIST OF VARIABLES THAT AFFECT LIQUIDITY CREATION .....................................................................................

TABLE 40 :

 40 DIND, ROBUST REGRESSIONS WITH FIXED EFFECTS AND GMM ESTIMATORS .....................................................

TABLE 41 :

 41 ACTUARIAL METHOD -ESTIMATED EFFECTS OF THE NEW BANKING REGULATION ON LIQUIDITY CREATION: UNTREATED VS. TREATED BANKS FOR THE LTGAP AND BBMATNONFAT MEASURES .......................................................................

TABLE 42 :

 42 COX REGRESSION FOR THE LTGAP AND BBMATNONFAT MEASURES ................................................................

168

Table

des équations

Afin d'assurer la reproduction des résultats, l'ensemble des calculs présentés ont été effectués avec STATA®. Les do-files et fichiers log constitués sont disponibles sur demande.

M*v = P*Y, where M is the Quantity of Money (stock of money present in the economy); v is the speed at which the money circulates; p is the price index; and Y is the total volume of transactions performed in that period.

Attributed to the common works of A. Marshall, A.C. Pigou and John Maynard Keynes: M=k.p.Y,where M is the Supply of Money; Y is the total resources; p is the price of the good; and k is the proportion of revenue that the agents want to hold in the form of money.

"Difference in difference"

The ISDA reported the effects of differences in accounting rules between US GAAP and IFRS based on eight major banks. Based on financial reporting data as of the end of the year 2009, the report indicates that the three major US banks JP Morgan, Citi Bank and Bank of America would have had to gross up, respectively, US $1,485, US $600 and US $1,414 billion of their total assets if they had followed the IFRS accounting netting framework. Source: "Reported gross assets and effects of offsetting derivatives, 2012".

Where Mat means that the balance sheet items have categorized by maturity, whereas Cat means that the balance sheet have been categorized by category of products; Fat means that the off balance sheet is included, whereas NonFat means that the off balance sheet data are excluded.

At lag 2, the GMM coefficient measures the degree of mean reversion and thus (1-λ) is interpreted as the coefficient of persistence of the explicative variable Y t-1 for each bank.

The BIS regularly edits a progress report on the adoption of the Basel regulatory framework. See the BIS website for the Basel committee publication : "http://www.bis.org/bcbs/publications.htm".

The number of banks varies per year because, every year, newcomers are in and closed banks are out.

Please refer to the note # 11 p.94 chapter 2 (article2) of the present thesis.

Remerciements

industry of Asia & Pacific and North America. This sensitivity analysis demonstrates that the liquidity creation decreases more that the risks for Asian & Pacific and North American banks suggesting that over a long period of time, the "financial fragility-crowding out hypothesis" prevails for these banks. However, the liquidity tends to decrease less that the risk for European banks, suggesting that over a long period of time, the "risk absorption" hypothesis prevails for these banks. This finding ultimately poses the question the effect of inappropriate regulations [START_REF] Morris | Liquidity black holes[END_REF]). In a context of homogenization of the banking regulation, in particular with Basel III deployment, further study on the effect of this new regulatory framework on banking liquidity creation could be useful. Finally, the results suggest that to reduce inefficiencies of monetary policy transmission, the conventional "bank lending channel" needs to be completed with a model that would include bank specific factors such as bank size, capital structure ( [START_REF] Van Den Heuvel | The bank capital channel of monetary policy[END_REF]; [START_REF] Shehzad | The impact of bank ownership concentration on impaired loans and capital adequacy[END_REF])) and risk performances.

Tables and figures Table 17: Summary of variables (part 1)

The table below lists the explained and explicative variables, including the control variables used for the robustness tests in the regression model established herein. The first column lists the typology of the variable; the second lists the variable name; the third lists the shorthand variable name used in the GMM, OLS and Cox regressions; the fourth indicates whether a variable was scaled; the fifth lists the expected relationship to liquidity creation and the last column shows how the variable was computed. NA LT gap as proposed by [START_REF] Deep | Are banks liquidity transformers?[END_REF]. Deflated based on the IMF inflation index (2005=100)

Liquidity creation variable 2

LGM Y2GLC at t and t+1 (for GMM)

Squaled over assets

Gross Liquidity creation as measured by [START_REF] Fungáčová | Bank capital, liquidity creation and deposit insurance[END_REF] The z-score as proposed originally by [START_REF] Roy | Safety first and the holding of assets[END_REF]: RoA+ Equity over Asset divided by standard deviation of RoA. This bank risk indicator measures the bank's distance to insolvency. Inversely related to the probability of default, the sign is expected to be positive.

-Standard deviation of RoA = Net Income Total (item WC01751) / Total assets (item WC02999). Tthe standard deviation requires at least three observations, is rolling and is computed every year (step of maximum 5 years).

where R(a) is the return of market share, R(f) is the return of benchmark government bonds, R(m) is the return of market index m where is listed the bank. Var(x) and Cov (x, y) require at least three observations. Covariances and variances are rolling and computed every year (step of maximum 5 years). Market price = Market value per share (item WC05001).

X11Rf

Sqrt -

The government bond rate of where is located the head quarter of the bank.

Macro environment

Institutional regulatory framework

X12Legal NA -

To measure the effect of the legal environment and institutional differences on liquidity creation, an index has been established as follow :

(1) before Basel I : I start first from La [START_REF] Porta | Legal determinants of external finance[END_REF] who established legal zones to measure the effect of the legal environment on the development of the capital market. To measure the effect of the legal environment on liquidity creation, a similar mapping is utilized in this paper, where a grade is provided to the level of property right protection from 0 to 4 corresponding from not protective to most protective, respectively.

(2) from Basel I to II, (3) from Basel II to III, and (4) since Basel III : The regulatory environment mapping of banks is elaborated from [START_REF] Barth | Bank regulations are changing: For better or worse ?[END_REF]; [START_REF] Barth | The regulation and supervision of banks around the world[END_REF] for the three last periods corresponding respectively to Basel regulatory frameworks deployments. It is a summary that averages the capital stringency as well as capital activity and restrictiveness per periods. The table below displays the correlation matrix of the explicative variables. The results indicate that only a combination of 2 sets of variables has a correlation superior to the commonly accepted threshold of 0.5 (in bold): The variables X2SdRoe1 and X9OpIncoA record a correlation with the variable X5Z of -0.77 and 0.5, respectively. It results that that the regression models are broken down by two set of variables: Model 1 that excludes the X5Z variable and model 2 that excludes the X2SdRoe1 and X9OpIncoA variables. The first column displays the variables as they appear in the table "Summary of variables (part 1 and 2). The next columns display these variables: X1BkSizeUSD, X2SdRoe1, X3EQoA, X4NPLoLoans, X5Z, X6PayShldrsoEq, X7LnGwth, X8ExcssR, Variable name in the OLS, X9OpIncoA, X10MktPrice, X11Rf, X12Legal, X13GDPgwth, X14HHI, X15YieldSpread, X16UnmpRate and X17TED. The table below shows the results of the GMM estimators with heteroskedasticity robust standard errors. The explained variables are the LTGap, LGM and BB-MatNonFat measures scaled over assets. Columns 1 to 3 list the typology, the comprehensive names and the names of the explicative variables, respectively. Columns 5 to 8 show the LTGap measure results for all regions, for the Asia-Pacific region, for Europe and for North America, respectively. This order is replicated for the LGM (columns 9-12) and BB-MatNonFat (columns 13-16) measures. T-stats are shown in brackets underneath the estimated coefficients, and the number of asterisks denotes the level of significance, where * denotes p<0.05, ** denotes p<0.01 and *** denotes p<0.001. The Sargan J-stat [p(chi²)] is reported at the bottom. It is the variance between the y and y-1 of liquidity creation. If negative, it takes a value of 1, i.e., failure, and it takes a value of zero otherwise. The explanatory variables have been layered. For levels of risk, stratification levels correspond to the quintile, where IStratSDRoE_2 corresponds to q1 (less than or equal to 25%); where IStratSDRoE_3 corresponds to q2 (less than or equal to 50%) and where IStratSDRoE_4 corresponds to q3 (less than or equal to 75%). For bank size, small banks are those with total assets of less than $US 50bln; medium banks are those with total assets of more than or equal to $US 50bln and of less than $US 250bln; and large banks are those with total assets of more than or equal to $US 250bln. Total assets were deflated based on the IMF index inflation rate for each country (base 2005) and were counter revaluated at the US$ IMF foreign exchange rate for each end-of-year period. For equity levels, stratification levels vary from 0% ("_IStratEqoA_0") to 17.5% ("_IStratEqoA_5"), with a regular increase of 2.5bp at each step. Columns 1 to 3 denote the variable type, the variable name used in the model, and the value corresponding to the variable, respectively. Columns 4 to 6 display the hazard ratio of the LTGap, LGM and BBMatNonFat measures for the Asia-Pacific region, respectively. This order is used for Europe (columns 7-9) and North America (columns 10-12 the Minimum common equity capital ratio (3.5%). Thus, it is reasonable to assume that the effects of Basel III on liquidity creation started before the official deployment of liquidity ratios. I acknowledge that this estimation could be re-assessed at various stages in the future.

However, this paper presents estimations that provide the first insights into the effects of Basel III on banking liquidity creation. For purposes of robustness and to complete the D-in-D estimation 19 , a set of additional tools have been used to estimate the effects of the policy.

Data before 2013 are considered as being before the deployment of the Basel III liquidity constraints, whereas data from 2013 are considered as being after this deployment. Given the potential interactions of the different components of Basel III on liquidity creation, this article estimates the effect of all components taken together, rather than estimating the effects of a particular component.

19 However, the D-in-D method is concerned with analyzing the time to occurrence of an event, and though this method is robust, it also relies on key assumptions. The problem with using a linear regression to analyze liquidity creation lies with the assumption that a common trend affects equally both treated and untreated population. This assumption is weakened in particular when the treatment is spread out over time, as is the case for the Basel III deployment for which the time duration turns out to be long. In particular, the underlying causes of changes that do not relate to policy modification have evolved in the same way among the

Descriptive statistics

The table below describes the liquidity creation indicators for the treated and untreated banks. The first indicator is LTGap, and the second is BB-MatNonFat scaled over assets and in US$ billions. Figure 3 shows the data displayed in Table 39 and depicts the evolution of the banking liquidity creation measures.

Insert Figure 3 and Table 38.

Two main observations can be drawn, as discussed below.

First, the 15 treated banks, which represent 3% of the total banks, amount to 70% of the liquidity created in the US, implying that Basel III affects a small number of banks but a significant portion of the liquidity created.

For liquidity creation measures scaled over assets:

 Average LTGap of untreated banks is stable at 0.22 between 2009 and 2015. For treated banks, the LTGap increased from 0.14 to 0.17 between Based on the literature review, the model is constructed as follows. I use the dependent variable of liquidity creation (the LTGap and BB-MatNonFat scaled over assets), and the explanatory variables are (1) bank size, given that small banks may behave differently from large banks; (2) equity over assets, which is a proxy that measures the risk absorption effect [START_REF] Repullo | Liquidity, risk-taking and the lender of last resort[END_REF]; [START_REF] Coval | Financial intermediation as a beliefs-bridge between optimists and pessimists[END_REF]); (3) nonperforming loans over the total loans ratio, which is a proxy for the bank's risk management efficiency and credit risk profile [START_REF] Acharya | Should banks be diversified? Evidence from individual bank loan portfolios[END_REF]); (4) z-score, which is a proxy for bank strength originally proposed by [START_REF] Roy | Safety first and the holding of assets[END_REF]; (5) the dividend paid out over the equity ratio, (6) loan growth, (7) Jensen (1968)'s alpha for excess returns, and (8) the market share value, which all proxy for bank performance [START_REF] Hackethal | Determinants of bank liquidity creation[END_REF]); ( 9) the LT-ST spread, which is used to assess the effect of a country's monetary policy on liquidity creation in the banking sector; the use of this metric is justified by the sensitivity of bank lending/borrowing to the LT maturity yield and by the effect of the ST-LT spread structure on a bank's profit margin [START_REF] Bernanke | The financial accelerator in a quantitative business cycle framework[END_REF]); (10) GDP growth and (11) the unemployment rate, which are proxies for the economic environment [START_REF] Beck | Bank concentration, competition, and crises: First results[END_REF], [START_REF] Imbierowicz | The relationship between liquidity risk and credit risk in banks[END_REF], [START_REF] Hackethal | Determinants of bank liquidity creation[END_REF] and [START_REF] Lei | Liquidity creation and bank capital structure in china[END_REF]); and ( 12) the bank-level Herfindahl market competition index (HHI), which controls for market competition [START_REF] Lei | Liquidity creation and bank capital structure in china[END_REF]). This variable is a proxy for market concentration; as shown by [START_REF] Beck | Bank competition and access to finance: International evidence[END_REF], market concentration impacts the availability of credit and, therefore, may affect liquidity creation.

Consequently, the model can be notated as follows:

Equation 11

Liquidity created over assets i,t = β0 + β18 Liquidity created over assets i,t-1 +

Actuarial

The results of the actuarial method are displayed in the graph below and demonstrate that Basel III has little effect on banking liquidity creation. The data are sourced from the actuarial life table that has been calculated.

Insert Figure 4 and Table 41 The evolution of the curves of the untreated and treated banks shows that there are no substantial differences between these two types of bank. From the actuarial perspective, between 2008 and 2013, the results indicate that the new banking regulation did not affect substantially the liquidity creation of the banking sector. The curve, however, indicates that the survival function, which was lower for untreated banks, crosses the curve of treated banks in 2011 and is higher in 2015 for both measures. The life table below shows the results of this actuarial estimation and indicates that the liquidity creation for treated population becomes higher than untreated population. It suggests that the cure enables the treated banks to create more liquidity than their untreated peers, which is consistent with the D-in-D.

Table 37: Definition of population treated vs. population untreated

The table below identifies the types of banks that are affected by Basel III LCR requirements ("treated") and those that are not ("untreated"). The first column indicates the bank size in US$ billions or equivalent, the second column indicates whether the bank is subject to Basel III, and the last column indicates whether the bank is considered to be "treated" or "untreated."

Bank size (in $ Bn or equivalent) Banking regulation Treated / Untreated Less than 50 and shadow banks Reprieve Untreated More than 50 Impacted Treated

+

The z-score as proposed originally by [START_REF] Roy | Safety first and the holding of assets[END_REF]: RoA+ Equity over Asset divided by standard deviation of RoA. This bank risk indicator measures the bank's distance to insolvency. Inversely related to the probability of default, the sign is expected to be positive.

-Standard deviation of RoA = Net Income Total (item WC01751) / Total assets (item WC02999). Tthe standard deviation requires at least three observations, is rolling and is computed every year (step of maximum 5 years).

-Net Income Total (WC01751) , represents the net income of the company, before extraordinary items. The derivative function corresponds to the derivative of liquidity creation: it is the variance between y and y-1 of liquidity creation. If negative, then it takes the value of 1, i.e., failure; otherwise, zero. Column 1 indicates the type of population, i.e., untreated or treated. Columns 2 to 10 show the results for the LTGap measure : Columns 2-3 indicate the time interval, columns 4 displays the number of subject that were present at the beginning, column 6 the number of subject who "died" during this interval, and column 6 the number of subject that were lost. Column 7-10 provide the estimates of the survival rate, the standard error, the lower level and upper level of the confidence interval, respectively. This order is duplicated for columns 11 to 19 which show the results for the BB-MatNonFat measure.

Loan growth