Larefi -Laboratoire D'analyse

Marie-Louise Djigbenou
email: louise.djigbenou@banque-france.fr

Larefi

Sophie Brana

Bruno Cabrillac

Mathieu Gex

Cameron Mcloughlin

Michel Juillard

Dalibor Stevanovic

Sandra Eickmeier

Christiane Baumeister

Keywords: Global liquidity, Ofcial liquidity, Funding liquidity, Quantitative easing, Monetary policy, Factor Model, Financial stability

Global liquidity, which consists of both monetary liquidity (quantitative easing and traditional policies) and funding liquidity, follows specific dynamics. The importance of these dynamics is reflected by the growing interest in international policy fora in the economic efects and determinants of this phenomenon. This paper contributes to this evolving policy debate by capturing the determinants of global liquidity dynamics. To this end, I employ a Factor-Augmented VAR model, with potential explanatory vari-ables based on Augmented-Taylor rules and private determinants. Using data from 1990 to 2011, I find that the factors representing real activity and financial stability are the main determinants of global liquidity dynamics. The impact of these factors is however heterogeneous across pre and post crisis periods.

List of tables

Introduction

Over recent years, Global liquidity has been one of the main issues in academic and policy debates. Economists, central bankers and international institutions have focused on this subject in order to better understand this concept. This willingness has impulsed many studies. In this context, the Bank of International Settlements (BIS) established for instance an ad-hoc global liquidity working group in 2011 2 , having as an output a definition of global liquidity and its measures [START_REF] Cgfs | Global liquidity -concept, measurement and policy implications[END_REF]). According to this report and previous works as the study of [START_REF] Baks | Global liquidity and asset prices : Measurement, implications and spillovers[END_REF], global liquidity can be considered as a multifaceted concept which is related to the "ease of financing". It can be approached by both the monetary view and the funding and market view. Traditionally, it is the monetary approach that is used in the literature. This approach refers generally to the official contribution in global liquidity via monetary authorities. Many papers as those of [START_REF] Rüffer | What is global excess liquidity, and does it matter?[END_REF], [START_REF] Denicolò | What is global liquidity[END_REF] have used this definition of global liquidity. The funding and market liquidity, which concerns the provision of private liquidity, are also increasingly considered in the literature. The paper of [START_REF] Bruno | Capital flows, cross-border banking and global liquidity[END_REF] reflects this idea by focusing on a cross-border funding via the channel of international banks.

All these works focus essentially on the study of the effects of global liquidity. [START_REF] Baks | Global liquidity and asset prices : Measurement, implications and spillovers[END_REF] as well as [START_REF] Darius | Can global liquidity forecast asset prices?[END_REF] study the spillover effects of Global liquidity on asset prices in order to identify any impact of liquidity on another market. [START_REF] Sousa | Global monetary policy shocks in the g5: A svar approach[END_REF] focused on the effects on global output and inflation in the G5 economies (United-States (US), Euro area (EA), Japan(JP), United Kingdom(UK) and Canada(CN)), while [START_REF] Brana | Global excess liquidity and asset prices in emerging countrie: a pvar approach[END_REF] analyze the effects on emerging economies. This growing literature highlights the increasingly interest in global liquidity. However, few recent works have analyzed the main drivers of the dynamics of global liquidity. [START_REF] Cgfs | Global liquidity -concept, measurement and policy implications[END_REF] outlines the main drivers and some channels of global liquidity but does not test empirically these determinants and their impacts in periods of crisis. One of the main contributions of this paper is to study the macroeconomic and financial determinants of global liquidity dynamics and how they impact global liquidity in crisis period according to the respective behaviors of private and official agents.

For achieving this objective, I follow mainly the methodology adopted by [START_REF] Bernanke | Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach[END_REF] which describes a Factor Augmented -Vector Autoregressive (FAVAR) model. This econometric choice is justified by the multifaceted dimension of global liquidity. In fact, the factor model in this method permits to summarize a large number of variables and extract the common factor which is supposed here to be global liquidity. And the VAR approach allows to get the responses to shocks. Based on this econometric method, I used in this paper a large definition of global liquidity which includes both official liquidity and private one proxied here by funding liquidity.

Furthermore, as in few recent works, the indicator of global liquidity takes into account not only the liquidity provided by advanced countries but also the liquidity issued by emerging countries. This last part of global liquidity has been neglected for a long time. However it represents almost 40% of the global official liquidity 3(figure 1). Therefore, it is more reflective of the global liquidity to include in the analysis, not only advanced countries, but also emerging ones.

Concerning the potential drivers of global liquidity dynamics, there are extracted from the literature of Augmented Taylor rules and private determinants of liquidity. In accordance with the basic objectives of central banks, inflation and economic conditions are considered as potential determinants. Exchange rates and financial stability objectives are also integrated to the analysis as suggested by the literature and the new approaches of central banks. These factors can also impact the funding liquidity. For instance, as mentioned by [START_REF] Forbes | Capital flow waves: Surges, stops, flight, and retrenchment[END_REF] and showed by [START_REF] Bruno | Capital flows, cross-border banking and global liquidity[END_REF], the risk or the uncertainty, measured by VIX, plays a significant role in crossborders flows.

This study considers as advanced countries the G5 economies United-States (US), Euro area (EA), Japan (JP), United Kingdom (UK) and Canada (CN) and as emerging countries Brazil (BR), India The outline of this paper is as follows: The second section describes the concept of global liquidity and its dynamics. The next section, section III, presents the potential determinants. In section IV, the econometric method of FAVAR model and hypothesis related to this study are detailed. This section also highlights the adequacy of this model with the issue of global liquidity and its determinants. Section V reports the empirical results, while Section VI concludes.

Global liquidity dynamics

Even though, the concept of global liquidity could be quiet intuitive, its definition, most of time differs based on the problematic of the study. Three main approaches are however considered in the literature. As the recent paper of [START_REF] Eickmeier | Understanding global liquidity[END_REF], this study integrates the multifaceted definition of global liquidity and emphasizes the role of both advanced and emerging countries in the expansion of global liquidity.

A multifaceted concept

Global liquidity is a multifaceted concept. It includes the liquidity provided by official and private sectors (CGFS (2011)).

The official view, which is the traditional one, refers to the liquidity provided by monetary authorities. The monetary authorities inject liquidity in the economy by defining the initial conditions of agents refinancing. Traditionally, they cut the main policy interest rates which drives the corridor of interest rates variations. And over the recent period, they adopt unconventional monetary measures such as quantitative easing. By these both policy measures, monetary authorities impulse the dynamics of the liquidity in the domestic economy but also at the world scale. In fact, if the currency of the given country can move easily and be used without many restrictions outside its own monetary area, the liquidity provided by domestic authorities contribute to expand global liquidity.

For traditional instruments, as the lowering of key policy rates, the local liquidity is directly spread as for carry-trade strategies, whereas for quantitative easing, the increase of global liquidity pass by the rebalancing of investors portfolio. So, through a direct or an indirect funding of the economy, the monetary authorities policies contribute to increase or decrease the liquidity across the world. And this is particularly relevant with the reduction in barriers to international trade and investment and the development of cross border flows. This traditional view has been adopted by many economists, who consider both quantitative and price indicators for measuring global liquidity. The price measures are essentially calculated on the basis of key policy rates. It can be a simple average of interest rates of countries considered in the study (GFSR (2010)) or a more complex calculation (De Nicolò and Wiegand (2007)). There are also some price measures based on global real short term in-terest rate and 10 year nominal term premium as shown in the following papers [START_REF] Denicolò | What is global liquidity[END_REF], CGFS (2011), etc.

Concerning the quantity indicators, different measures are also presented in the literature. First of all, monetary base is used as reflecting the initial condition of access to liquidity defined by the monetary authorities [START_REF] Artus | L'histoire de la liquidité mondiale -the global liquidity history[END_REF]). De Nicolò and Wiegand (2007) and [START_REF] Darius | Can global liquidity forecast asset prices?[END_REF] include the base money as a component of global liquidity as CGFS (2011). Other narrow and broader monetary aggregates (M1, M2 or M3) are also cited in the literature. [START_REF] Sousa | Global monetary policy shocks in the g5: A svar approach[END_REF] define global liquidity in their paper as the "the sum of the monetary aggregates of the US, the euro area, Japan, the UK and Canada" . Another official global liquidity measure, very convenient for emerging countries is the foreign reserves. This part of monetary authorities balance sheet is directly usable for exchanges outside the domestic monetary area. It has been used by Domanski et al. (2011), De Nicolò and[START_REF] Denicolò | What is global liquidity[END_REF] and [START_REF] Darius | Can global liquidity forecast asset prices?[END_REF]. All these indicators can be considered for taking into account the impact of monetary policies on global liquidity and highlights the official contribution of global liquidity dynamics. But they do not represent all the liquidity available in the economy. The private sector contributes also significantly to the global liquidity dynamics.

The private liquidity is the liquidity provided by private agents via banking and financial institutions. It consists of funding liquidity and market liquidity which represents respectively " the ease with which agents can obtain funding" and "the ease with which an asset is traded" [START_REF] Brunnermeier | Market liquidity and funding liquidity[END_REF]. As the official liquidity, funding and market liquidity can also be measured by quantity and price indicators. Concerning quantitative measures, credit aggregates are often used as indicator of funding liquidity (CGFS (2011)). [START_REF] Domanski | Assessing global liquidity[END_REF] consider bank liquidity ratios, maturity mismatch measures and commercial paper volumes. And as price indicator, they suggest Libor-OIS spreads, foreign exchange swap basis, bond -CDS basis or surveys of funding conditions. In a market liquidity perspective, transaction volumes as a quantity measure, as well as Bid-ask spreads on selected global asset, yield differential between less frequently traded and more frequently traded, and qualitative fund manager surveys as a price indicator (De Nicolò andWiegand (2007), BOE (2007) , [START_REF] Domanski | Assessing global liquidity[END_REF]) are considered.

As global liquidity is impacted by these different approaches, this study focuses on both official liquidity and private liquidity. Furthermore, because of recent developments on global liquidity, not only advanced economies are considered, but the liquidity issued by emerging countries is also integrated.

Advanced and Emerging countries implications

The study of global liquidity dynamics has to integrate the liquidity provided by both advanced and emerging countries, especially with the recent development. In a worldwide perspective, the liquidity created in a country has to be able to move outside its monetary area and used easily in this new environment without a significant lost of its value. In other words, this liquidity or the underlying currency, has to be exportable or useable in a sizeable part of the international trade. According to this criteria, the national liquidity issued by emerging countries has often been neglected. On the private side, the access of foreigners to credit is often limited by regulation, and on the official side the liquidity created by local central banks can be non-convertible.

These arguments had justified the focus on only advanced economies. However, considering the assets side of their balance sheet can allow integrating this part of global liquidity.

Emerging countries are now the most foreign reserves holders. Since the end of the 90's with the asian crisis, they have built up more reserves especially for precautionary motives and other motivations. This liquidity, which is directly usable outside their monetary area, reflects also a part of their local monetary policy. Therefore foreign reserves can be used as a proxy of the contribution of emerging countries to global liquidity. Moreover the management of reserves impulses also purchases of foreign risk-free assets in order to reduce the opportunity costs. These acquisitions drive liquidity to another foreign market which becomes more liquid. Thus the integration of foreign reserves from emerging countries is justifying4. This idea is also supported by data. 4 According to TIC data from US Treasury Department, China stays the most foreign holder of Treasuries in 2011. And this holding represents around 60% of total foreign reserves of PBoC.

For capturing the share of each group of countries, a global liquidity indicator is constructed on the basis of foreign reserves of emerging countries (Eme5) and Base money of advanced countries (Adv6): figure 1.

This indicator shows a significant contribution of both groups of country. Emerging countries, representing by the main holder of foreign reserves, shares half of the liquidity provided by monetary authorities with the advanced countries in 2008. Their contributions to global liquidity have increased gradually since the end of the 90's and justify the interest for integrating this group of countries in the global liquidity indicators, even though, over recent period, its share has been reduced to 42% due to the liquidity dynamics in advanced countries. The share of the United states has been increasing dramatically due to the implementation of a continuous Quantitative easing policy since 2008. Her contribution has increased from 13,5% in June 2008 to almost 22% of the total in December 2012. The United Kingdom has quasi-doubled her share over this same period, whereas the share of Euro area remained quasi stable, while Japan's share had declined relatively to the rest of the world. This induces a decrease in the weight of emerging countries, which stayed however significant (42%). 5 Brazil (BR), India(IN), China(CH), Qatar(QA), Saoudi Arabia(SA), Venezuela(VN), Nigeria(NG), United Arab Emirates(UA) 6 United-States (US), United Kingdom(UK), Japan (JP), Euro Area (EA), Canada (CA)

Development of global liquidity

As mentioned earlier, global liquidity in this paper is defined by both its public and its private components. The liquidity provided by monetary authorities is measured by key policy rates and monetary base for the advanced countries (US, UK, JP, EA, CN) and by foreign reserves for the emerging countries (BR, IN, CH, QA, SA, VN, NG, UA). For a longer period empirical analyse, only credit and short term interest rates (3-months and 6-months interbank rates) are considered as a measure of private liquidity. Moreover, the combination of both official indicators and credit permits to get a better view of the dynamics of global liquidity. The interest of that considerations could be for instance to monetary authorities to better focus their monetary decision on improving the financial stability. These relationship have been studied by [START_REF] Christiano | Monetary policy and stock market booms[END_REF]. The authors conclude in their paper that a greater role of credit growth in the interest rate targeting rule would moderate the volatility in the real economy and in asset prices, and therefore would improve the financial stability.

I consider also narrow and broader monetary aggregates M1 and M2. These indicators integrate de facto the actual monetary policy and reflect also the credit distribution.

All these indicators are extracted from Central banks, IFS, WEO, OECD, Reuters databases and other national sources from 1990 to 2012. For quantitative indicator, I follow the calculation method proposed by [START_REF] Baks | Global liquidity and asset prices : Measurement, implications and spillovers[END_REF] which consists in expressing each domestic indicator in terms of local GDP, then to weight them by their relative GDP in terms of PPP. Concerning the price measures, the global liquidity indicator is obtained by a simple average of national interest rates.

On the whole, until 2008, the indicators of global liquidity in figure 2 reports an upward trend of global liquidity. In spite of some episodes of rate rises, the policies adopted by monetary authorities have been wholly accommodative and credit has continued to grow. The cut of key policy rate following the NTIC crisis in 2001 has permitted to inject liquidity inside the economy until 2004.

The credit, slowed-down by the crisis, is relaunched after the monetary policy actions. So in 2004, the global liquidity is sustained by both monetary policy and by private sectors. To keep downing these dynamics, the monetary authorities increased their key policy rate which passed in average from 2,3% in March 2004 at 4% in September 2007. This increase is reflected through the monetary base volume which stayed quasi constant over this period. On the contrary of this restrictive policy, the credit continues to grow on that period.

With the beginning of the crisis in 2007, started first series of lowering interest rates. These rates passed from 3,90 to 2,95% in september 2008. But with the crisis worsening, other plans have been implemented, leading the rate close to zero in september 2009. This substantial decline is followed by a dramatically increases in monetary base which continues to grow with the implementation of unconventional monetary policy (quantitative easing, credit easing, etc.) although the interest rate remained close to zero. On the side of private liquidity, the sustained growth of credit in terms of GDP is interrupted in 2009 and is declining. Therefore the ample liquidity provided by monetary authorities is implemented in a context of weak and unstable private liquidity.

The monetary aggregates M1 and M2 follow the dynamics of monetary policy but integrate information from private sector too by slowdowns in 2009. For emerging countries, the part of foreign reserves in terms of GDP has continued to grow since the 1990's until September 2008. After a short decrease, the build up of reserve start again but with a lower speed.

All these indicators are so useful for understanding the dynamics of global liquidity. The monetary base as a quantity measure permits to take into account the non-conventional mea-sure which are not reflected in interest rate data, especially with zero lower bound. In addition many configurations can be considered according to the policy adopted by monetary authority and the dynamics of private liquidity. For studying the macroeconomics and financial factors behind this dynamics, it can be interesting to focus on the determinants of each component. 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 6% 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Indicator of global liquidity based on foreign reserves (in terms of GDP) 70% 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 20% 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Potential determinants of global liquidity

Determinants of global liquidity are defined in terms of macroeconomic and financial factors which drive the monetary policy and the dynamics of private liquidity proxied in this paper by funding liquidity or by credit. Concerning the determinants of monetary policy, the price stability is the main objective of monetary authorities. In accordance with the experience of monetary authorities, a broad consensus arose from the central banks and the economists around the priority to price stability 7. The control of inflation permits to guide the evolution of purchase power in the economy and therefore fosters an environment conducive to economic growth. Central banks also consider as another objective the economic growth. This second objective is most of the time combined with the inflation controlled in a Taylor rule according to the preferences of monetary authorities. These two objectives are the traditional objectives chosen by almost all central banks.

But other objectives can also be enumerated in the macroeconomic determinants of monetary decisions.

The financial stability and the exchange rates have also be raised as potential factors which drive monetary policy. In spite of the debate around the integration of asset prices in the monetary policy, some central banks have already integrated this variable in their decision. For instance, the Bank of Canada outlines: "as made clear in the past, to the extent that financial imbalances affect the near-term outlook for output and inflation, financial stability considerations are already taken into account in the setting of monetary policy ". And more recently, the Canadian monetary authorities have considered also integrating some flexibility due to financial stability in its inflation-targeting agreement. This choice of taking into account financial stability is essentially based on the capacity of asset prices to help forecasting the output and to some extent the inflation. [START_REF] Cecchetti | Asset prices and central bank policy[END_REF] find also in their paper that taking into account the asset prices in the monetary policy improve the monetary policy and can reduce the output volatility, those which satisfies the traditional and main objective of central banks. Moreover the "Great deviation" 8, observed over recent years, which describes the gap between the observed and the Taylor rule rates, could suggest other factors in the 7 Friedman (1982) "there is today a worldwide consensus, not only among most academic economists but also among monetary partitioner, that the long-run objective of monetary policy must be price stability » 8 [START_REF] Taylor | Macroeconomic Lessons from the Great Deviation[END_REF] rule of central banks. [START_REF] Hofmann | Taylor rules and monetary policy: A global 'great deviation[END_REF] show also this deviation and explain it by different potential factors, and particularly the asset prices and the exchange rates. Taking into consideration the exchange rates in monetary decision can be justified by the willing to keep a quite stable differential rates with other monetary areas in order to limit the unwelcome capital inflows.

Therefore, inflation, economic activity, financial stability and exchange rates could be determinants of global liquidity.

Concerning the private sector, these previous variables can also be considered as determinants of private liquidity. [START_REF] Hofmann | The determinants of bank credit in industrialized countries: Do property prices matter[END_REF] studies the determinants of private sector credit and justifies a role of real activity and real estate prices in the dynamics of credit. The real activity impacts both the demand and the supply side of credit. Households and firms define their demand of credit according to current and expectation of economic activity dynamics. And if the expectations are less beneficial, the demand of credit can be reduced because agents want to be able to pay off principal and interests of the loans. As the dynamics of economic activity induce the degree of creditworthiness, the suppliers are also more reticent to lend. The role of real estate is linked to wealth effect on the demand and the value of collateral required for getting a loan. So asset prices and economic activity could be also used as macroeconomic factors of private liquidity. Inflation impacts by definition the real interest rates. And the effects of exchange rates dynamics are integrated in carry trade strategies which also impact the dynamics of credit and private liquidity.

Empirical studies as those of Hoffmann (2012) and [START_REF] Clarida | Monetary policy rates in practice -some inter-national evidence[END_REF] have already highlighted respectively this taking into account of exchange rates in the monetary decision of ECB and in Bank of Japan.

Therefore, economic activity, inflation, exchange rates and financial stability could be considered as potential determinants of global liquidity either by official part or by private. Some researches also show an asymmetric reactions of some central banks to the volatility of asset prices (VIX). D' Agostino et al. (2005) find that the reaction of the Federal Reserve (FED) de-pends of the volatility regime of asset returns, with a more significant reaction during period of falls. This result is also showed by SÌüren (2012) and [START_REF] Hoffmann | Did the fed and ecb react asymmetrically with respect to asset market developments?[END_REF].

One of the main interests of this paper is, on the one hand, to study if these relations are observed empirically in a larger global liquidity approach which focuses on the two components of global liquidity and in a global perspective, and on the other hand, analyze if the behavior of these determinants have changed with the financial crises of 2008. As these studies require a huge number of variables for the definition of concepts and applied to different countries, a Factor augmentedautoregressive vector (FAVAR) model is adopted.

FAVAR Model and Hypotheses

Methodology and Justification of FAVAR approach

This approach has been proposed by Bernanke, Boivin and Eliasz (2005) and combines a Factor model with structural vector auto-regressive (SVARs) methodology. This econometric method permits to integrate in only one model a rich environment of informations by factor approach.

According to [START_REF] Stock | Macroeconomic forecasting using diffusion indexes[END_REF], 2005) and [START_REF] Forni | Opening the black box: structural factor models with large cross sections[END_REF] works, I consider that each variable has two components linked by a linear relation : Therefore initial variables can be expressed by the following relation:

• A common component χ • An idiosyncratic
= + ξ (1)
By this factor equation, more informations known by agents in the economy are put in the model. So an economic concept can be better described by a set of variables due to common macroeconomic component captured in the model. And Principal component analysis is suitable as method for achieving it. Applied to Global liquidity, this econometric method allows to summarize the information contained in the diferent approaches of the concept, and by this way getting a more efective representation of global liquidity. As mentioned earlier, the global liquidity depends on both the ofcial and the funding liquidity. So the liquidity available in a global perspective could be considered as the common factor resulting from dynamics of its components.

Another benefit of Factor model is to make easier the identification of shocks. In fact, adding more information in the model reduces the risk of getting a wrong shock especially for structural VARs model. In other words, it reduces the unfundamentalness risk [START_REF] Forni | Opening the black box: structural factor models with large cross sections[END_REF]) and improves the model explanatory power. According to factor equation ( 1), expression of VARs becomes:

(2)

From this second equation ( 2), the efects of macroeconomic shocks can be assessed. These variables can be extracted from factor equation or can be directly observable variables . The FAVAR Model is therefore represented by the following relations [START_REF] Bernanke | Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach[END_REF]):

Identification schemes

The identification schemes concern both the identification of factors and the identification of shocks. The idea underlying the identification of factors is to guarantee the structure of the factor and in this paper, to get an economic meaning essential to the interpretation of the factor. As the derived factors issued from the principal component analysis will be afterwards put into a VAR model, the number of factors is restricted to 1. This hypothesis seems not so strong for the explanation power of factors in the main estimation is greater than 30% and the factor of global liquidity is more than 45%.

To keep an economic meaning to economic concept used in this FAVAR model, some restrictions are introduced into the loading matrix (the matrix of the coefficients), as done by [START_REF] Belviso | Structural factor-augmented vars (sfavars) and the effects of monetary policy[END_REF]. The restrictions are described just below with X i a subset of the whole sample which shared the same economic meaning and F i the related factor.

(

) 4
The subsets series Xi are ordered as required by the Cholesky decomposition for defining a structure of the model. This ordering permits to identify the effect of each chock instead of the effect of a combination of chocks issued from a regression without restrictions. The structure of the model retained here considers the global liquidity indicator as the most endogenous variable according to the "augmented Taylor-rules" presented in the above section. As justified earlier, the dynamics of liquidity could be driven by real activity, inflation, exchange rates and financial stability. Among these variables, exchange rate is considered as the most exogenous, because it results from the decision of many authorities and therefore is less controllable than the local variables. This view is also shared by [START_REF] Darius | Can global liquidity forecast asset prices?[END_REF], as many other studies who consider also that "output contemporaneously affects inflation". In fact, the general price in the economy are more sensitive to actual and expected output. Agents adjust more quickly the price than the dynamics of the economic. And this pace of adjustment in prices is even more suitable on financial markets where agents integrates directly the information in their price. Therefore the following order will be adopted :

(5)

Each subset of variables organized by economic meaning is identified as highlighted by the table 1.

Empirical application

5.1.Data

Table 1: Description of economic concepts (UK), Euro Area (EA) and Canada (CAN). Concerning Euro area, proxies will be gotten by data from Germany, France, Italy and Spain if data are not available. Data are on quarterly frequency from 1990 to 2011 and make stationary.

Based on Principal components analysis, Factors are gotten. Two loadings matrix will be calculated for Global liquidity according to the reference period. I distinguish a pre-crisis period from 1990 to 2007 and the whole sample covering the crisis (1990 to 2011).

On the period prior crisis, the main components of Global liquidity factor are monetary liquidity aggregates which integrate the transmission of official liquidity to real economy and the weight of key policy rate stays significant. This is relatively close to reality before crisis. The policy rate was the principal instrument of central banks for impacting liquidity and transmission channels were sufficiently effective. So I can emphasize these components.

But after the crisis, monetary authorities widen their policy instruments and monetary base fluctuates dramatically. The funding liquidity via credit is monitoring closely for avoiding credit crunch. On the side of emerging markets, the foreign reserve weight on global liquidity decrease relatively to previous period.

For real activity factor, the signs of components are consistent with the economic meaning. In other words, an increase in this factor implies a greater consumption, investment or a decrease in unemployment rate. This principal is applied to all potential determinant factor. The international trade factor measures the growing of exchange between countries, that is an increase of both imports and exports.

Determinants of global liquidity

Real activity, the main driver of global liquidity

Global liquidity is mainly driven by economic activity, which explains almost one third of the forecast error variance. This result is consistent with the priorities of central banks and the high weight of economic background in the distribution of credit. In fact, in absence of high inflation as observed during the pre-crisis period, monetary authorities support eco-nomic activity by also limiting the risk of overheating. The global improvement of economic performances until 2008 can therefore contribute to justifying some restrictive measures imple-mented by monetary authorities as in 2004 and 2005. This reflects the negative and significant relationship between real activity and official liquidity shown by figure 6. These monetary policies are integrated in the determination of funding liquidity as suggested by figure 8 and 10. Private agents in this situation face less favorable financing conditions (figure 7), which reduces the private liquidity. So as the whole, the first impact of the economic expansion is a slowdown of global liquidity due to action of monetary authorities (figure 4). However, this effect is not permanent. In the following months, financial institutions dampen progressively the monetary constraints and renew with providing increasing funding to the economy in order to benefit from the growth. Thus increases global liquidity.

Over the recent period with the great recession, the relationship between official and private agents, real activity and global liquidity stays, on the whole, suitable. Monetary authorities impulse the dynamics of liquidity and issue liquidity.Nevertheless, contrary to the growth situation, the reactions of central banks are greater than in the previous case (figure 5). In fact, they have to inject more liquidity in the economy in order to contribute to restoring the economy, and offsetting the lack of private liquidity. Private agents, in this crisis context, are more reluctant to loan and become very pessimistic about the recovery of the economy (figure 9). The accommodative stance of monetary authorities impacts shortly the behavior of financial agents who continue to tighten significantly the funding in the economy. Con-sequently, the actions of central banks have to be strong enough to successfully fund the economy directly. This result justifies the reactions of official agents during the crisis and the non-conventional measures implemented. Quantitative easing and credit easing, which have been widely used, have helped provide funds directly to specific markets or agents and compensating the lack of funding liquidity.

So, depending on the economic situation, official and private agents do not have the same role in the provision of global liquidity according to the economic situation. In period of growth, the banks and other financial institutions are the main issuer of liquidity, and central banks try to contain the liquidity dynamics. But during period of recession, these private agents overshoot compare to the growth framework and reduce considerably the liquidity. In that context, monetary authorities increase dramatically the official liquidity and become a stronger and more strategic player. Therefore, the dynamics of global liquidity depend on economic situation, but also on financial stability.

Financial instability and heterogeneities across agents

Financial stability is the second main determinant of global liquidity according to variance decomposition (figure 11). As defined previously, the financial stability indicator is calculated by taking into account asset prices especially equity and real estate indexes as well as a measure of uncertainty on financial market, the VIX. An increase of this indicator match with higher asset returns and lower financial uncertainty.

The results of this study suggest that there is a negative significant relationship between the indicator of financial stability and global liquidity (figure 4 and3). As for real activity, monetary authorities react contra-cyclicaly. In period of boom, they slowdown the growth of liquidity, especially the market liquidity, which by definition increases dramatically with lower volatility on markets or higher asset prices. However, this action of monetary authorities seems to have just a short and small impact on the distribution of credit (figure9). Banks integrate the constraints of monetary authorities but provide credit few quarters later as the financial stability reduces the risk of insolvency.

Nevertheless, the impact of financial stability on the distribution of credit seems limited as highlighted by figure 11 and the corresponding IRF, contrary to period of stress.

In period of financial instability, as on the recent period, with higher volatility and frozen markets, market liquidity is strongly degraded, weak or nearly inexistent in some markets. Monetary authority react by injecting liquidity for limiting the transmission to the real economy. This accommodative monetary policy is marginally integrated by the banks. However, unlike to period of boom where banks have almost neutral stance, these agents in period of higher uncertainty react significantly by decreasing the volume of credit. This could be as a result of lower demand or supply of credit . But whatever the source, the better funding conditions impulsed by monetary authority does not restore the credit.

This result is emphasized by the variance decomposition (figure 11) where the heterogeneities across agents behaviors are very well highlighted. During period of boom, the share of financial stability for explaining credit does not exceed 10% of the variance of the forecast error, whereas after the crisis, it double and represent 20% at the same horizon. For monetary authorities, they are more concerned with avoiding or limiting the formation of bubbles than responding actively in case of crisis. The impact of financial instability on the official liquidity is lower than the response of monetary authority to financial stability. In average, before the 2007 crisis, financial stability represented 15% of the variance of official liquidity and after the crisis this figure decrease at 6. This result is consistent with the willingness of central banks to limit moral hazard which could follow an extensive involvement of monetary authorities on financial markets.

So real activity and financial stability impact significantly the dynamics of global liquidity by affecting behaviors of official and private agents. However, the relationships with inflation is less obvious.

A mitigating role of inflation

Unlike to what would have been expected, the inflation before crisis has not contributed significantly to explaining global liquidity dynamics. In fact, according to the inflation targeting, a negative relationship between global liquidity and inflation would have been expected. But as suggested by results, Official as well as private agents (respectively figure 5 and figure 9) do not respond to inflation during the pre-crisis period. As the whole, over this period, it represents only 5% of the variance of global liquidity forecast error. However, with the recent crisis, this impact is much more important (11%). Official and Private agents seem to take into account much more inflation than previously. This can be explained by the dynamics of inflation itself (figure 12). As shown by the graph, inflation stays relatively stable between 1% and 3% which did not justify additional reactions from central banks. The credibility of this inflation targeting policy is also integrated by banks which did not react to any inflation shocks during this pre-crisis period or neglect the eventual negative effects of inflation. Consequently, inflation did not contribute significantly to the dynamics of global liquidity before 2007.

However, over the recent period, higher variability in inflation drives monetary authorities to adjust their policy and the weight of inflation becomes more significant. In addition to this, the adoption of unconventional measures, whose the effects are less well known by central banks, can induce distrusts and fears from private sectors. This permits to justify the bigger role of inflation after the crisis.

Concerning exchange rate, it did not play a significant role in the dynamics of global liquidity no matters the agents and the specifications.

This results are robust to the ordering and remain stable even if only the VIX is considered as measure of financial stability.

Conclusion

The dynamics of global liquidity rely essentially on the economic performance, financial stability and to some extent on inflation. Real activity and financial stability, which are the main drivers of liquidity dynamics, hide heterogeneous behaviors between official and private agents. Generally private sector respond by a pro-cyclical manner, which is emphasized with recession and exacerbated with higher uncertainty. In period of financial stress, banks reduce dramatically the distribution of credit, whereas they were quasi neutral during period of fi-nancial stability. This reaction of private agents is very useful for better understanding the reactions of monetary authorities. As a whole, monetary authorities limit the expansion of private liquidity (funding or market) by restrictive policies in period of economic and financial booms. However, during recession or financial instability, they inject liquidity as a liquidity supplier of last resort. This response of central banks quite match their objectives and permit to offset partially, at least, an eventual lack of liquidity. Even when they provide liquidity in period of financial distress, official agents limit their actions in order to avoid moral hazard which could induce from an extensive involvement.

Concerning inflation, its contribution to global liquidity dynamics has to be nuanced before the crisis because of its relatively stable level. However, its higher variability over the recent period can justify a more significant contribution to the dynamics of global liquidity.

Finally, this study has provided a substantial description of the behaviors of each group of agents and contributed to better understanding the dynamics of global liquidity from 1990-to-date.

As concluded by [START_REF] Eickmeier | Understanding global liquidity[END_REF], it's therefore very useful to integrate the multifaceted aspect of global liquidity and the interlinkages between its different components in an analysis. Considering only private or official views could biased the perception of the effective liquidity dynamics. As the behaviors of agents are identified, it could be consequently very interesting, as the next step, to focus on researching the efficiency of each group in providing global liquidity and the limitations of their actions in emerging and advanced countries.

Figure 1 :

 1 Figure 1: Breakdown of global liquidity between Advanced and Emerging countries .................... Figure 2 : Global liquidity indicators .............................................................................................Figure 3: Impulse Response function for GLI ................................................................................Figure 4: Impulse Response function for GLI before the crisis ......................................................Figure 5: Impulse Response function -Official liquidity...............................................................Figure 6: Impulse Response function before the crisis-Official liquidity ......................................Figure 7: Impulse Response function -Private liquidity ................................................................ Figure 8: Impulse Response function before the crisis -Private liquidity ...................................... Figure 9: Impulse Response function -Credit ................................................................................ Figure 10: Impulse Response function before the crisis -Credit ..................................................... Figure 11: Decomposition of the Forecast Error Variance .............................................................. Figure 12: Global annual inflation factor ......................................................................................

Figure 3 :

 3 Figure 1: Breakdown of global liquidity between Advanced and Emerging countries .................... Figure 2 : Global liquidity indicators .............................................................................................Figure 3: Impulse Response function for GLI ................................................................................Figure 4: Impulse Response function for GLI before the crisis ......................................................Figure 5: Impulse Response function -Official liquidity...............................................................Figure 6: Impulse Response function before the crisis-Official liquidity ......................................Figure 7: Impulse Response function -Private liquidity ................................................................ Figure 8: Impulse Response function before the crisis -Private liquidity ...................................... Figure 9: Impulse Response function -Credit ................................................................................ Figure 10: Impulse Response function before the crisis -Credit ..................................................... Figure 11: Decomposition of the Forecast Error Variance .............................................................. Figure 12: Global annual inflation factor ......................................................................................

Figure 4 :

 4 Figure 1: Breakdown of global liquidity between Advanced and Emerging countries .................... Figure 2 : Global liquidity indicators .............................................................................................Figure 3: Impulse Response function for GLI ................................................................................Figure 4: Impulse Response function for GLI before the crisis ......................................................Figure 5: Impulse Response function -Official liquidity...............................................................Figure 6: Impulse Response function before the crisis-Official liquidity ......................................Figure 7: Impulse Response function -Private liquidity ................................................................ Figure 8: Impulse Response function before the crisis -Private liquidity ...................................... Figure 9: Impulse Response function -Credit ................................................................................ Figure 10: Impulse Response function before the crisis -Credit ..................................................... Figure 11: Decomposition of the Forecast Error Variance .............................................................. Figure 12: Global annual inflation factor ......................................................................................

Figure 5 :

 5 Figure 1: Breakdown of global liquidity between Advanced and Emerging countries .................... Figure 2 : Global liquidity indicators .............................................................................................Figure 3: Impulse Response function for GLI ................................................................................Figure 4: Impulse Response function for GLI before the crisis ......................................................Figure 5: Impulse Response function -Official liquidity...............................................................Figure 6: Impulse Response function before the crisis-Official liquidity ......................................Figure 7: Impulse Response function -Private liquidity ................................................................ Figure 8: Impulse Response function before the crisis -Private liquidity ...................................... Figure 9: Impulse Response function -Credit ................................................................................ Figure 10: Impulse Response function before the crisis -Credit ..................................................... Figure 11: Decomposition of the Forecast Error Variance .............................................................. Figure 12: Global annual inflation factor ......................................................................................

Figure 6 :

 6 Figure 1: Breakdown of global liquidity between Advanced and Emerging countries .................... Figure 2 : Global liquidity indicators .............................................................................................Figure 3: Impulse Response function for GLI ................................................................................Figure 4: Impulse Response function for GLI before the crisis ......................................................Figure 5: Impulse Response function -Official liquidity...............................................................Figure 6: Impulse Response function before the crisis-Official liquidity ......................................Figure 7: Impulse Response function -Private liquidity ................................................................ Figure 8: Impulse Response function before the crisis -Private liquidity ...................................... Figure 9: Impulse Response function -Credit ................................................................................ Figure 10: Impulse Response function before the crisis -Credit ..................................................... Figure 11: Decomposition of the Forecast Error Variance .............................................................. Figure 12: Global annual inflation factor ......................................................................................

Figure 7 :

 7 Figure 1: Breakdown of global liquidity between Advanced and Emerging countries .................... Figure 2 : Global liquidity indicators .............................................................................................Figure 3: Impulse Response function for GLI ................................................................................Figure 4: Impulse Response function for GLI before the crisis ......................................................Figure 5: Impulse Response function -Official liquidity...............................................................Figure 6: Impulse Response function before the crisis-Official liquidity ......................................Figure 7: Impulse Response function -Private liquidity ................................................................ Figure 8: Impulse Response function before the crisis -Private liquidity ...................................... Figure 9: Impulse Response function -Credit ................................................................................ Figure 10: Impulse Response function before the crisis -Credit ..................................................... Figure 11: Decomposition of the Forecast Error Variance .............................................................. Figure 12: Global annual inflation factor ......................................................................................

(

  IN), China (CH), Qatar(QA), Saoudi Arabia(SA), Venezuela(VN), Nigeria(NG), United Arab Emirates(UA) for defining global liquidity. The sample covers the period from 1990 to 2011, on a quarterly basis. The potential drivers are extracted only from advanced countries due to lack of data from emerging countries. And a global factor are calculated by potential determinant. This work highlights two main determinants of global liquidity dynamics which are real activity and financial stability factor. The impact of inflation stays mitigate. Moreover, the responses of agents are impacted by the crisis essentially with the rise of uncertainty.

Figure 1 :

 1 Figure 1: Breakdown of global liquidity between Advanced and Emerging countries

Figure 2 :

 2 Figure 2 : Global liquidity indicators

  component ξ By this way, = χ + ξ I consider also that common components are driven by macro-shocks , which are a

( 3 )

 3 Therefore, FAVAR model allows us to get a Factor measure of Global liquidity which capture both dynamics of ofcial and funding liquidity. Moreover, it permits to measure the efects of potential determinants on Global liquidity factor by impulsing response functions and study-ing the direction of causality via Granger causality test. I am also interested by the Variance decomposition, which gives an indication on the explanatory ability of the determinants. But in order to do so efectively, it's necessary to define a structure of the model by identification schemes.

Figure 5 :Figure 6 :FigureFigure 10 :

 5610 Figure 5: Impulse Response function -Official liquidity

Table 1 :

 1 Description of economic concepts ..................................................................................... 24

v

List of figures

This working group has been chaired by JP Landau, Banque de France deputy-governor.

In this paper, only the official part of liquidity issued by emerging countries is used for being sure of the capacity of the currency to be used outside its monetary area

Annexes


Ummad Mazhar
email: umadmazhar@gmail.com

Cheick Kader M'baye

Does Forecasts Transparency Affect Macroeconomic Volatility in Developing Countries? Evidence From Quasi-Natural Experiments

Keywords: Forecasts transparency, monetary policy transparency, central banking, inflation volatility, output volatility. JEL Classification: E58, E63, C33, C36

In this paper, we empirically investigate the link between forecasts transparency and macroeconomic volatility as measured by inflation and output growth volatility in developing economies. We adopt the quasi-random controlled experiments methodology that divides our sample of 49 developing countries into three categories on the basis of their forecasts transparency. The first category is composed of central banks that are completely opaque over our sample period. The second type of countries is constantly transparent about their forecasts over the period of study while the third category includes central banks that have recently started to disclose their forecasts. In contrast to the previous literature, we interestingly find that increasing forecasts transparency unambiguously leads to higher macroeconomic volatility in developing countries. Indeed, we find that both groups of countries that constantly disclose their forecasts and that have only recently started to disclose their forecasts experience an increase in their macroeconomic volatility compared to the remaining group of countries that are completely opaque. Our results however indicate that forecasts transparency may have some stabilizing effects if and only if it is practiced along with other forms of institutional transparency.

Introduction

Monetary policy transparency has various dimensions. For reasons related to both economics and politics, a minimum level of transparency is considered desirable. An issue, however, that still needs a consensus concerns the relationship between different forms of transparency and the macroeconomic environment. Both theoretical and empirical studies have contributed to this debate. From a theoretical perspective, an effective management of agents expectations for instance requires a future-oriented monetary policy. Even more important, forward-looking monetary policy reduces lags in the effects of policy decisions and helps to mitigate the rules versus discretions conflict in policy making [START_REF] Svensson | Inflation forecast targeting: Implementing and monitoring inflation targets[END_REF][START_REF] Woodford | Imperfect Common Knowledge and the Effects of Monetary Policy, chapter[END_REF]2007). Nonetheless, a number of recent studies find that greater disclosure of information can be harmful for stabilization policies [START_REF] Morris | Social value of public information[END_REF][START_REF] Baeriswyl | The signaling role of policy actions[END_REF][START_REF] James | Optimal policy intervention and the social value of public information[END_REF]; or that ambiguity is welfare enhancing if it allows greater discretion in pursuing alternative policy goals [START_REF] Cukierman | A theory of ambiguity, credibility and inflation under discretion and asymmetric information[END_REF][START_REF] Geraats | The mystique of central bank speak[END_REF][START_REF] Walsh | Announcement and the role of policy guidance[END_REF].

Empirical findings on the effects of transparency also highlight the conflicting conclusions of the theoretical literature. For instance, among the few empirical studies on the issue, Demertzis and Hughes-Hallet (2007) find, contrary to their theoretical predictions, a positive relation between output volatility and transparency. Whereas, [START_REF] Chortareas | Does it pay to be transparent? international evidence from central bank forecasts[END_REF] and [START_REF] Dincer | Central bank transparency: Causes, consequences and updates[END_REF] find that transparency reduces macroeconomic volatility.

It has been noted that empirical studies on the effects of transparency fail in many important aspects. For instance, a large part of the empirical literature focuses on a general measure of transparency assuming complementarity between various aspects of transparency. This assumption is not necessarily true as political transparency cannot complement forecasts transparency. Similarly, there are only few studies that relate the effects of transparency to its degree. In this regard, Van der Cruijsen et al., (2010) provide a robust evidence that an intermediate degree of overall transparency is optimal in reducing inflation persistence. What is still not known, however, is to what extent forecasts transparency is desirable in the presence of other forms of transparency. This paper empirically contributes to this literature by focusing on the effects of forecasts transparency on macroeconomic volatility in developing countries. To our knowledge, this paper is the first one to apply the quasi-natural experiments methodology to deal with this issue. There is an abundant theoretical literature that favors forecasts transparency as a feature of monetary policy strategy (see van der Cruijsen and Eijffinger, 2010 for a survey). However, previous studies analyze the influence of transparency clubbing together its various aspects. Thus, there is no distinction between political transparency (which has average score of 2 across our sample of countries) and economic transparency (which has an average score of 0.60 across our sample of countries). It is only recently that empirical literature has started analyzing the individual impact of different forms of transparency and their relative desirability [START_REF] Crowe | Central bank independence and transparency: Evolution and effectiveness[END_REF][START_REF] Cruijsen | Optimal central bank transparency[END_REF][START_REF] Mazhar | Transparency and output volatility: Empirical evidence[END_REF].

Our paper approaches the issue of forecasts transparency in two novel aspects. First, it designs a quasi-random experiment by dividing central banks into different groups according to their levels of forecasts transparency. Second, we focus on a relatively similar set of countries. Our sample includes 49 middle income economies where a middle income economy is defined to be either lower middle income or higher middle income economy following World Bank classification1 . Despite being reasonably similar in their institutional setting and level of economic development, these countries exhibit significant differences in terms of forecasts disclosures. This allows us to perform a meaningful comparison across central banks with different transparency practices.

Our analysis draws on unique insights about transparency to particularly investigate the impact of forecasts transparency on inflation volatility and output growth volatility controlling for the influence of various other forms of transparency. Our findings interesting suggest that forecasts transparency has an independent and significant positive impact on macroeconomic volatility. In other words, forecasts transparency makes the macroeconomic environment more instable in developing countries. However, it may have some stabilizing effects only if it is practiced along with other forms of transparency components.

Our findings contribute to the existing literature in different ways. Firstly, as noted by [START_REF] Calvo | The mirage of exchange rate regimes for emerging market countries[END_REF] and [START_REF] Fraga | Inflation targeting in emerging market economies[END_REF], developing countries are generally characterised by greater macroeconomic volatility compared to developed economies. Our findings suggests that monetary policy transparency can be one way of achieving greater stability if reliable forecasting communication mechanism is established along with other dimensions of transparency. Secondly, we try to quantify the effects of forecasts transparency and to highlight their relative worth in comparison to other sources of stabilization.

The rest of the paper is organized in the following way. Section 2 presents and describes our empirical methodology and dataset. Section 3 presents and analyzes our results.

Finally, we present the conclusion of the paper in Section 4.

Methodology and dataset

Our methodology consists in differentiating between three types of countries in our sample of 49 middle income countries. In the first category, we have central banks with zero forecasts transparency. In other words, the first category includes those central banks that are completely opaque about their forecasts over our sample period that is, from 1998 to 20072 . The second group of countries is transparent about their forecasts but their forecasts transparency remains constant over the period of our study. The final category includes central banks that move from zero to positive transparency over our sample period.

Our measure of transparency comes from Eijffinger and Geraats (2006) transparency index (hence, EG index) which has become a standard gauge of transparency3 . We use the EG index as updated by [START_REF] Siklos | Central bank transparency: Another look[END_REF] 4 to proxy transparency across different dimensions.

The 5 components of the EG index are detailed in Table 1 in the Appendix. Each of these five components has three sub-indices. Hence, 15 sub-indices compose the general EG index. The minimum score of each sub-index is 0, and the maximum is 1, with higher score corresponding to higher level of transparency. Summing all the 15 sub-indices' scores yields the general EG index. Consequently, the EG index has a minimum of 0, and a maximum of 15.

The classification of central banks in our sample depends on their forecasts transparency (sub-index B.1 in Table 1 in Appendix). The group of countries with zero forecasts transparency throughout the period of study (called ZeroTr group) is our control group. The group with positive forecasts transparency throughout the period is our treatment group (abbreviated as PositiveK ). The third group (called ChangeTr ) is primarily meant to highlight the before and after effect of switching from zero to positive forecasts transparency. Ideally we should compare the performance of the group PositiveK before 1998 to its performance over our sample period. However, it is not feasible as there is no forecasts transparency measure available for all the countries prior to 1998 period. Admittedly, absence of measure before 1998 does not mean absence of transparency. Thus, if we compare periods prior to 1998 with period of our study, we cannot identify causal effect of transparency. By number of countries, the largest category is ChangeTr that includes 23 countries. The categories ZeroTr and PostiiveK contain respectively 19 and 7 countries. Table 2 in the appendix provides definitions and sources for all variables used in our study as well as our sample of countries, and summary statistics across our three groups are presented in Table 3.

The empirical model

Our empirical model estimates the following equation:

Inf V ol it = f (dummies of f orecasts transparency, transparency, control variables),
where i =1, 2, ..., 49 denotes country and t=1998, ..., 2007 denotes year.

A similar specification is used for output growth volatility (GrVol ) as dependent variable. We calculate inflation volatility (respectively output growth volatility) as 5 years moving standard deviation. This technique is standard in the literature investigating the determinants of macroeconomic volatility [START_REF] Blanchard | The long and large decline in us output volatility[END_REF][START_REF] Cecchetti | Assessing the sources of changes in the volatility of real growth[END_REF].

As noted before, there are three categories of central banks in our sample according to their numerical forecasts disclosures. We include dummies for PositiveK and ChangeTr categories while taking ZeroTr as the reference category. The value of transparency scores (TrIndexAdj ) is included after excluding the forecasts transparency component. In other words, this variable represents the scores of the EG index after the component numerical macroeconomic forecasts (aspect B.1 in Table 1) has been excluded.

In addition to forecasts dummies and transparency index, we also control for a number of relevant variables. For instance greater trade openness can increase or decrease the macroeconomic volatility depending upon various geographical, logistic, and terms of trade related factors [START_REF] Bowdler | Openness and inflation volatility: Cross-country evidence[END_REF]. Previous studies also find a role for openness although its direction is not yet clear [START_REF] Cecchetti | Assessing the sources of changes in the volatility of real growth[END_REF]and Cabanilas and Ruscher, 2008). Moreover, it is important to control for openness and increased interdependence among countries to account for the impact of global conditions on individual economies [START_REF] Stock | Understanding changes in international business cycle dynamics[END_REF]. All these reasons give rational for the inclusion of openness in our empirical model.

To control for the effect of institutional factors and overall living standard, we include GDP per capita in purchasing power parity US dollars. However, we cannot expect a strong influence of GDP per capita on our dependent variables because of the relatively homogenous nature of our sample in terms of GDP per capita.

Many studies also point out the role of energy ratio (as a percentage of GDP) in reducing macroeconomic volatility. For example, [START_REF] Nakov | Oil and the great moderation[END_REF] show that a lesser reliance on oil mitigates the impact of oil price shocks. In particular, many middle income economies experience greater volatility because of their greater reliance on petroleum imports. Therefore, without controlling for the influence of this factor, it will be hard to interpret our estimates ceteris paribus.

Another important determinant of macroeconomic volatility is inventory investment.

It is found that greater flexibility and timeliness in inventory management stabilizes output (McConell and Perez-Quiros, 2000). However, in cross-country setting, [START_REF] Cecchetti | Assessing the sources of changes in the volatility of real growth[END_REF] and [START_REF] Cabanillas | The great moderation in the euro area: What role had macroeconomic policies played? Technical report[END_REF] find that changes in inventory investment are negatively correlated with output volatility.

The share of services sector in the composition of GDP is found to have a stabilizing effect on output and price volatility. Arguably, increasing share of services in the GDP growth indicates an evolution of the economic structure towards higher stage of development. Therefore, we can expect the share of services to have a negative impact on output growth volatility.

Another issue we should consider is that, it is not obvious that transparency and macroeocnomic volatility are related linearly. Thus, to take into account this possible non-linear relationship, we also construct a non-linear model by introducing interaction terms. We believe that it will allow us to evaluate the robustness of the results we gather from the linear regression analysis.

Results

As a first step towards our empirical analysis, we conduct a simple comparison of means test between our three groups of countries. To this end, we follow [START_REF] Wooldridge | Introductory Econometrics: A Modern Approach[END_REF] and run a simple regression with InfVol (respectively, GrVol ) as our dependent variable taking each of the group dummy variable in return. This test tells whether the groups are significantly different from each other in terms of their macroeconomic volatility or not. For instance, our results in Table 4 indicate that central banks with ZeroTr have significantly low inflation volatility than all the rest. However, although their relatively less volatility is indicated by a negative coefficient on GrVol, it is not significant at conventional levels. Similarly, the category PositiveK exhibits a significantly more output growth volatility than all the rest of central banks, while central banks which change their forecasts transparency status experience a significantly greater inflation volatility than all the remaining groups of central banks in our sample. This test is very general as it cannot differentiate between the more than two categories at a time, which tends to limit its scope. Nevertheless, it allows us to acknowledge the existence of significant differences in the macroeconomic behavior between our three groups of countries.

Table 5 presents the results of the impact of forecasts transparency on inflation volatility. Our basic specification is presented in column (1). It contains transparency scores adjusted for forecasts transparency (TrIndexAdj ) and two dummy variables which represent two out of three groups in our sample namely, PositiveK and ChangeTr. Among control variables, the basic specification includes Openness (Openness), GDP per capita (GDPpc) and energy ratio ( EnergyRatio).

As shown in the last row, the overall model is highly significant. The presented results are also robust against heteroskedasticity and autocorrelation for order one5 . Our results indicate a significant negative effect of TrIndexAdj on inflation volatility. The coefficient has a meaningful magnitude indicating that a one percentage increase in the overall adjusted transparency index reduces the standard deviation of inflation by approximately 0.82 point. Putting this in our context, the mean value of inflation volatility in our sample is 4.51. Thus, our results indicate that for an average central bank, a one point increase in the adjusted transparency index, keeping all the other things unchanged, will reduce inflation volatility to 3.69.

Openness is measured as a percentage of GDP. Its coefficient is negative but insignificant. Even when it is significant (as in models in columns ( 2) and ( 3)), it indicates a marginal reduction in inflation volatility. Thus, with reference to the regression in column (2), a one percentage increase in openness reduces inflation volatility by 0.02 per cent. This result may reflect an inadequate measurement of openness through the ratio of imports plus exports to GDP. Indeed, this measure ignores many factors that determine the impact of openness on macroeconomic variables such as the quality of institutions and logistic facilities.

As for GDP per capita, it appears to have a significant positive impact on inflation volatility although this effect does not contain much economic magnitude. It may indicate inexistence of a linear relationship or an indirect relation between GDP per capita and inflation volatility.

We find that energy ratio has a significant negative impact on inflation volatility. This is consistent with the earlier findings documented by [START_REF] Nakov | Oil and the great moderation[END_REF] and [START_REF] Mazhar | Transparency and output volatility: Empirical evidence[END_REF]. In terms of magnitude, a one percent increase in energy ratio reduces inflation volatility by 0.70 point. This result indicates that a more efficient use of energy or greater productivity due to technological improvement likely stabilizes inflation.

Our findings also indicate a stabilizing influence of inventory investment. Thus, greater efficiency and timeliness in the management of inventory reduces inflation volatility. According to our evidence, if inventory investment increases by a million US dollars, the inflation volatility will reduce by 1 point indicating a weak economic effect.

Coming to our variables of interest, we find that those central banks maintaining a positive level of macroeconomic forecasts transparency experience more inflation volatility than those which maintain zero forecasts transparency. More specifically, the estimated coefficient implies that the PositiveK group of countries has 3.4 points higher level of inflation volatility than the ZeroTr group. Similar pattern is displayed by the ChangeTr group: It almost shows 4 points higher inflation volatility than the ZeroTr group. These results point out a novel aspect of transparency: increasing forecasts transparency increases inflation volatility at least in the case of developing countries. This is in contrast with previous studies which found a stabilizing effect of forecasts transparency [START_REF] Chortareas | Does it pay to be transparent? international evidence from central bank forecasts[END_REF].

Our findings could however be explained by the specific features of developing countries. As noted by [START_REF] Calvo | The mirage of exchange rate regimes for emerging market countries[END_REF] and [START_REF] Fraga | Inflation targeting in emerging market economies[END_REF], one of the main characteristics of developing countries in general and emerging countries in particular is the presence of weak fiscal and monetary institutions. Weak fiscal institutions means that in order to finance their deficits, governments in these countries regularly put pressures on central banks so that to adopt inflationary policies leading to weak monetary institutions.

As agents learn from the behavior of the central bank, increasing forecasts transparency will lead to greater volatility as they disbelieve in the ability of the central bank to provide reliable macroeconomic forecasts. Hence, being opaque about macroeconomic forecasts in these countries may be useful in the sense that it lessens reputation loss by hiding the real intents of central banks.

In columns ( 2) and (3) we add other important determinants of inflation volatility.

In column (2) we add the share of services sector in GDP while in column (3) we add inventory investment change. The results in columns ( 2) and (3) remain similar to those in column (1). The positive effect of forecasts transparency on inflation volatility remains unchanged although the magnitude of the coefficients of dummy variables decreases in both cases.

It is possible that forecasts transparency has stabilizing properties when taken in re-lation to other aspects of transparency. To inspect this, we include interaction terms of our group dummy variable with TrIndexAdj. As is shown in column (4), our control variables (that is, GDP per capita and openness) now appear to be insignificant in mitigating inflation volatility. However, the results of transparency again indicate that forecasts transparency is harmful. The interaction effect of PostiveK and TrIndexAdj is positive and significant while the marginal effect of PositiveK is insignificant. On the other hand however, the interaction effect of ChangeTr and TrIndexAdj is negative and significant, although the marginal effect remains significantly positive. This result indicates that even when its dependence on other forms of transparency is taken into account, forecasts transparency has ambiguous stabilizing effect on inflation volatility.

In Table 6 we follow the same specifications as in Table 5 but with output growth volatility as our dependent variable. In this case however, coefficients of control variables are less significant. For instance, openness has no significant effect on output growth volatility in any of the Table 6 regressions. Similarly, GDP per capita also has a negligible impact, if at all. Both share of services and inventory change reduce output growth volatility which is in line with the previous findings.

Again the dummy variables on forecasts transparency are mostly positive and significant. The results indicate that compared to central banks having zero forecasts transparency, those which remain consistently transparent over our sample period or those which turn toward forecasts transparency experience greater output growth volatility. This is particularly in contrast with the findings of [START_REF] Chortareas | Does it pay to be transparent? international evidence from central bank forecasts[END_REF] who have shown that there is no evidence that forecasts transparency increases output volatility.

However in terms of interaction terms (shown in column (4) in Table 6), the effect of both PositiveK and ChangeTr on output volatility is significantly negative indicating a stabilizing effect on output growth volatility. The marginal effect of PositiveK is nonetheless significantly positive but the marginal effect of ChangeTr is insignificant.

To summarize our findings, we can put forward that forecasts transparency in itself unambiguously increases macroeconomic volatility in developing countries but if it is practiced along with other forms of institutional transparency, it is likely to have some stabilizing effects.

Concluding remarks

In this paper, we empirically analyze the effects of macroeconomic forecasts transparency on inflation and output growth volatility in developing countries. The previous literature, both theoretical and empirical, does not lead to a unanimous consensus about the relation between forecasts transparency and macroeconomic volatility. Given the relevance of forecasts transparency in forward-looking monetary policy effectiveness, it appears necessary to investigate more deeply this issue.

We however adopt a novel approach to this issue by performing a quasi-random controlled experiments methodology that divides our sample of 49 developing countries into three categories on the basis of their forecasts transparency practices. The first group, denoted by ZeroTr, is the one that never discloses any numerical forecasts over our sample period. The second category includes central banks that remain transparent over our sample period. Moreover, their transparency level remains unchanged over our estimation period. The third category includes countries that change their forecasts transparency score from zero to positive over our sample period that is, they are central banks that start disclosing forecasts only recently.

Our results indicate that compared to ZeroTr group, both PositiveK and ChangeTr groups experienced an increase in their macroeconomic volatility, while transparency scores (excluding forecasts transparency scores) appear to stabilize the macroeconomic volatility. These findings are robust to the inclusion of other determinants of macroeconomic volatility and interaction terms.

In terms of policy recommendation, our study has a clear message: forecasts transparency in itself unambiguously increases macroeconomic volatility in developing countries. However, it may have some stabilizing effects if and only if it is practiced along with other forms of transparency. But to the extent that these countries suffer from weak fiscal and monetary institutions, we recommend them to improve the quality of their institutions for instance by improving central bank independence along with increasing their level of overall transparency in order to insure strong macroeconomic effects of transparency policies.

Our evidence is based on a group of developing countries. An open avenue for future research is to perform the same analysis for developed economies so that to highlight more general policy implications.

Table 2 . Variables Definitions and Sources and sample countries Variable Definition (Mean; St Dev) Source

 2

	InfVol	Inflation volatility is the 5	Inflation is taken from World
		year		moving	standard	Economic Outlook, IMF (2011).
		deviation of inflation where
		inflation is annual percentage
		change in consumer price
		index (4.51; 8.38).
	GrVol	Output growth volatility is	Same as above.
		the 5 year moving standard
		deviation of annual output
		growth rate where annual
		growth rate is the percentage
		change in GDP in constant
		2005 US dollars (2.72; 1.79).
	TrIndexAdj	An		overall		index	of	Eijffinger and Geraats (2006)
		transparency. Originally it	transparency index as provided
		includes political, economic,	by Siklos (2011).
		procedural,		policy,	and
		operational		aspects	of
		transparency. Included in the
		regression after subtracting
		the		scores	of	forecasts
		transparency (one of the sub-
		components	of	economic
		transparency) (4.06; 2.09).
	PositiveK	A	dummy	variable	for	Eijffinger and Geraats (2006)
		forecasts	transparency.	It	transparency index as provided
		assumes a value of 1 for	by Siklos (2011).
		those	central	banks
		maintaining	full	forecasts
		transparency throughout our
		sample period. The measure
		of forecasts transparency is
		one sub-index of Eijffinger
		and		Geraats	transparency
		index.	Their	measure	of
		forecasts	transparency	is
		obtained as an answer to the
		question: Does the central
		bank regularly publish its
		own macroeconomic forecast?

Table 2 . (Continued) Variable Definition (Mean; St Dev) Source

 2

	ChangeTr	A	dummy	variable	for	Eijffinger and Geraats (2006)
		forecasts	transparency.	It	transparency index as provided
		assumes a value of 1 for	by Siklos (2011).
		those central banks that have
		changed	their	forecasts
		transparency from zero to
		positive	over our sample
		period.	The	measure	of
		forecasts transparency is one
		sub-index of Eijffinger and
		Geraats transparency index.
		Their of forecasts
		transparency is obtained as
		an answer to the question:
		Does	the	central	bank
		regularly publish its own
		macroeconomic forecasts?
	Openness	Percentage of imports plus	PWT(2011)
		exports to GDP (80.57; 35.89)
	GDPpc	GDP per capita in PPP US	IMF (2011)
		dollars (5759; 3372).
	EnergyRatio	GDP per unit of energy use is	WDI (2011)
		the GDP per kilogram of oil
		equivalent	of	energy	use
		where GDP is measured in
		2005 constant purchasing
		power parity dollars.
	ServShare	Percentage value addition by	WDI (2011)
		services sector in GDP of a
		country (53.73; 11.87).
	InventChg	It is the value of the change
		in inventories (measured in
		constant 2005 US dollars)
		(341.0; 1217).

Table 3 . Summary Statistics Across Groups ZeroTr (19 countries) PositiveK (7 countries) ChangeTr (23 countries) Mean(Std.Dev) Obs. Mean(Std.Dev) Obs. Mean(Std.Dev) Obs.

 3

	InfVol	3.64 (3.31)	123	4.03 (3.37)	49	5.34 (11.58)	157
	GrVol	2.58 (1.76)	123	3.24 (2.05)	49	2.66 (1.70)	157
	TrIndexAdj	3.04 (1.56)	190	3.95 (1.76)	70	4.93 (2.18)	230
	Openness	81.10 (30.57)	190	85.14 (32.48)	70	78.74 (40.64)	230
	GDPpc	5339 (3679)	179	4644 (3159)	70	6425 (3039)	230
	EnergyRatio	4.65 (2.31)	167	7.60 (4.06)	58	6.12 (2.75)	208
	ServShare	51.27 (14.11)	178	57.96 (6.29)	70	54.36 (10.81)	227
	InventChg	101.4 (414.7)	166	256.2 (728.4)	70	539.8 (1625)	230

Table 4 . Difference in Means Test: Each Group versus Rest of the Sample

 4

		InfVol	GrVol	Obs.
	ZeroTr	-1.388*	-0.209	329
		(0.774)	(0.202)
	PositiveK	-0.561	0.613***	329
		(0.719)	(0.308)
	ChangeTr	1.587*	-0.115	329
		(0.958)	(0.196)
	Notes: Test is conducted by regressing InfVol (respectively, GrVol) on each of the
	group dummy. Constant is included but not reported. Robust standard errors in
	parentheses; *** p<0.05, * p<0.1.

Table 5 . Inflation volatility and forecasts transparency.

 5 Notes: HAC Standard errors reported in parentheses. Constant is included but not reported.*** p<0.01, ** p<0.05, * p<0.1. F-test tests the null hypothesis that all the regressors except constant are zero.

	Dependent Variable: Inflation Volatility
		(1)	(2)	(3)	(4)
	TrIndexAdj	-0.818**	-0.739*	-0.768**	-0.210
		(0.404)	(0.368)	(0.337)	(0.203)
	PositiveK	3.394**	2.095***	1.309**	-1.454
		(1.515)	(0.771)	(0.491)	(1.035)
	ChangeTr	3.977**	3.050**	3.010**	5.507***
		(1.818)	(1.348)	(1.211)	(1.806)
	Openness	-0.011	-0.015***	-0.018***	-0.016
		(0.008)	(0.005)	(0.005)	(0.013)
	GDPpc	0.000**	0.000**	0.000**	0.000
		(0.000)	(0.000)	(0.000)	(0.000)
	EnergyRatio	-0.703**
		(0.274)
	ServShare		-0.101**
			(0.041)
	InventChg			-0.001***
				(0.000)
	Interactions
	PositiveK*TrIndexAdj				0.579*
					(0.304)
	ChangeTr*TIndexAdj				-0.831**
					(0.340)
	Marginal Effects
	PositiveK				0.683
	(Delta-Method std.err.)				(0.542)
	Change				2.438**
	(Delta-Method std.err.)				(1.088)
	Observations	285	317	315	329
	R-squared	0.080	0.049	0.046	0.044
	Countries	47	48	46	48
	F-test for overall	0.000***	0.000***	0.000***	0.000***
	significance p-value 1

Table 6 . Output growth volatility and forecasts transparency

 6 Notes: HAC Standard errors reported in parentheses. Constant is included but not reported.*** p<0.01, ** p<0.05, * p<0.1. F-test tests the null hypothesis that all the regressors except constant are zero.

	Dependent Variable: Output growth volatility
		(1)	(2)	(3)	(4)
	TrIndexAdj	-0.166***	-0.200***	-0.211***	0.018
		(0.020)	(0.043)	(0.032)	(0.106)
	PositiveK	0.499	1.314***	0.849**	2.090**
		(0.334)	(0.385)	(0.359)	(1.005)
	ChangeTr	0.172	0.665**	0.381*	1.182***
		(0.275)	(0.301)	(0.225)	(0.438)
	Openness	-0.003	0.001	-0.001	-0.001
		(0.002)	(0.003)	(0.002)	(0.003)
	GDPpc	0.000	0.000*	0.000	0.000
		(0.000)	(0.000)	(0.000)	(0.000)
	EnergyRatio	-0.037
		(0.041)
	ServShare		-0.025***
			(0.008)
	InventChg			-0.000***
				(0.000)
	Interactions
	PositiveK*TrIndexAdj				-0.386*
					(0.230)
	ChangeTr*TrIndexAdj				-0.269**
					(0.119)
	Marginal Effects
	PositiveK				0.667*
	(Delta-Method std.err.)				(0.354)
	ChangeTr				0.188
	(Delta-Method std.err.)				(0.239)
	Observations	285	317	315	329
	R-squared	0.038	0.096	0.060	0.070
	Countries	47	48	46	48
	F-test for overall	0.000***	0.000***	0.000***	0.000***
	significance p-value 1

According to the World Bank classification, a middle income country is one having per capita GDP between 3976 to 12275 US dollars whereas lower middle income group have per capita income between 1006 to 3975 US dollars.

[START_REF] Siklos | Central bank transparency: Another look[END_REF] provides transparency scores for 100 central banks over a period from 1998 to 2009. To avoid any issue relating to financial crisis which will complicate our analysis, we restrict the upper limit of our sample period to 2007.

The EG index is however not free of weaknesses. As pointed out by[START_REF] Claussen | Comparing monetary policy transparency: the eijffinger and geraats index -a comment[END_REF] many times transparency comparisons across central banks blow up the differences and lead to misleading conclusion. We believe that our sample being reasonably similar do not suffer from this weakness.

[START_REF] Geraats | Central bank transparency[END_REF] classify five dimensions of transparency in terms of policy processes: political, economic, procedural, policy, and operational. Political transparency is about the clarity of objectives, economic transparency covers information used for the policy decision, procedural transparency relates to the decision making process, policy transparency to the monetary policy stance, and operational transparency to the effects of monetary policy implementation.

Specifically, we use[START_REF] Driscoll | Consistent covariance matrix estimation with spatially dependent data[END_REF] standard errors that are robust against heteroskedasticity and autocorrelation.

Evaluating monetary policy outcomes

Appendix

 [1998][1999][2000][2001][2002][2003][2004][2005][2006][2007].

India, Mauritius, Moldova, Namibia, Solomon Islands, Sri-lanka, Uruguay (Total 7).

Zero forecasts transparency (ZeroTr):

It includes all the countries that have zero forecasts transparency throughout our sample period.

Armenia, Bhutan, Bulgaria, Cuba, Egypt, El Salvador, Ghana, Jordan, Latvia, Libya, Lithuania, Mongolia, Nigeria, Pakistan, Russia, Tunisia, Ukraine, Yemen (Total 19).

Change in forecasts transparency (ChangeTr):

It includes all the countries that have changed their transparency from zero to positive over our sample period.

Albania, Argentina, Belarus, Belize, Brazil, Chile, China, Columbia, Fiji, Georgia, Guatemala, Indonesia, Jamaica, Kazakhstan, Malaysia, Mexico, Papua New Guinea, Peru, Philippines, Romania, South Africa, Thailand, Turkey (Total 23).