Isabelle Distinguin
email: isabelle.distinguin@unilim.fr

Clovis Rugemintwari
email: clovis.rugemintwari@unilim.fr

The role of market discipline on bank capital buffer: Evidence from a sample of European banks

Keywords: bank capital buffer, prudential regulation, Basel accords, market discipline. JEL Classification Numbers: G21, G28, G32

Using a sample of European commercial banks over the period 1993-2006, we show that market discipline significantly and positively affects banks' capital buffer. By distinguishing junior from senior debt holders, we find that both types of investors exert a pressure on banks to hold more capital but that the pressure exerted by junior debt holders is higher. Furthermore, junior debt holders exert a pressure on banks whatever the importance of their non-traditional activities. By contrast, we find that senior debt holders exert a pressure only on banks that are heavily involved in non-traditional activities that are badly taken into account in the current bank capital regulation framework. These results might help us to better understand the role of market discipline as a complement to capital regulation.

Introduction

After the implementation of Basel I, there has been a noticeable upward trend in bank capital ratios throughout G-10 countries with banks holding capital ratios well beyond the minimum regulatory constraint. This has raised the question of why banks hold such high regulatory capital ratios, or put differently, why they hold capital in excess 1 of what is required by the regulator. Indeed, bankers often argue that capital is more expensive than debt. Therefore, several studies aim to determine what underlines this unexpected behavior by studying the determinants of capital buffer. In this vein, [START_REF] Lindquist | Banks' buffer capital: how important is risk?[END_REF] considers Norwegian banks and investigates if risk is an important determinant of the buffer 2 . He does not find any significant link. [START_REF] Ayuso | Are capital buffers pro-cyclical? Evidence from Spanish panel data[END_REF], [START_REF] Stolz | Banks' regulatory capital buffer and the business cycle: Evidence for Germany[END_REF] consider Spanish and German banks respectively and [START_REF] Jokipii | The cyclical behaviour of European bank capital buffers[END_REF] consider banks from 25 European countries to investigate how the business cycle influences the buffer. Their results globally indicate that banks tend to decrease the buffer during the upturn and increase it in the downturn.

In our analysis, we mainly focus on the role played by market discipline. Using a sample of European commercial banks on 1993-2006 3 , we study the influence 4 of market discipline on the build-up of capital buffer. According to [START_REF] Evanoff | Subordinated Debt and Bank Capital Reform[END_REF], banks can be exposed to ex-ante or ex-post market discipline. Ex-post market discipline implies that banks change their behaviour following a change in debt spread whereas ex-ante market discipline refers to the fact that banks exposed to market discipline may change their behaviour ex-ante in order to avoid the costs imposed by market participants through higher spreads. In this paper, we consider ex-ante discipline assuming that this discipline encourages banks to behave more prudently. Other papers have considered market discipline. For example, [START_REF] Flannery | What Caused the Bank Capital Build-up of the 1990s?[END_REF] using large US banks, investigate the causes of the bank capital build-up of the 1990s. They find that even though several factors explain the 1 Throughout this paper, this excess capital is called capital buffer and defined as the difference between the actual capital ratio ((Tier1+Tier2)/Risk weighted assets) and the Basel minimum required capital ratio (8%), except for special cases (see 2.2). 2 A study by Jokippi and Milne (2010) focuses on the relationship between risk and the buffer adjustments and finds a positive two-way link. 3 Our sample period ends in 2006 to avoid that the anticipation of Basel II implementation and the subprime crisis affect our analysis. 4 Bliss and Flannery (2001) distinguish two components of market discipline: monitoring that corresponds to the fact that investors accurately assess changes in banks financial condition and promptly incorporate it into their stock and bond prices, and influence that is the ability of market participants to affect banks' financial decisions. In this paper, we focus on influence.

the activities of banks have rapidly and deeply changed these last decades: market activities have expanded with the creation of more and more complex financial instruments and banks have broadly used securitization. These changes have been reflected in the structure of banks' income with an increasing proportion of non-interest income 7 . By contrast, capital regulation seems quite rigid; it is difficult to adapt it quickly and adequately to this highly evolving environment. We assume that market participants may adapt more rapidly and may consider these changes to determine the adequate level of capital of the bank. Thus, it appears interesting to determine whether the impact of market discipline on bank capital buffer is different depending on whether the bank is highly involved in non-traditional activities or not.

If market discipline is effective for banks highly involved in non-traditional activities, it might be used as a complement to capital regulation.

The rest of the paper is structured as follows. In section 2, we set our hypotheses and the method used to test them, define our variables and present the sample of banks. The results and the robustness checks are presented in section 3. Section 4 concludes the paper.

Hypotheses, model, variables and sample

Hypotheses

Firstly, we consider that banks whose debt holders are more sensitive to default risk are expected to hold more capital than prescribed by the regulator. Indeed, we assume that these debt holders may lack confidence in the ability of a bank to survive if it operates with a capital ratio very close to the regulatory minimum. In that case, they may pressure the bank to hold more capital than required by regulation. Hence, we consider that the type of funding could impact bank capital buffer. Accordingly, we investigate the impact of market discipline on capital buffer by focusing on the extent to which banks rely on market funding. Capital buffer should be positively related to the proportion of market funding. Indeed, their holders are the creditors who have the highest incentives to exert a discipline and thus it is more costly for banks to increase their risk of default when they have a larger proportion of market liabilities [START_REF] Nier | Market discipline, disclosure and moral hazard in banking[END_REF]). Besides, it has been shown both theoretically and empirically by [START_REF] Gropp | Deposit insurance, moral hazard and market monitoring[END_REF] that banks with a larger share of uninsured funding have incentives to take less risk. They suggest that the larger is the proportion of uninsured funding, the stronger is the effect of market discipline. Indeed, the larger is the proportion of uninsured liabilities, the stronger is the cost impact related to market discipline for a given increase in bank risk8 . Thus, following these studies, we consider that the structure of bank liabilities is a crucial factor and assume that banks heavily relying on market funding may exhibit higher capital buffer as they are potentially more subject to market discipline.9

H1: Market debt holders exert a pressure on banks to hold capital buffer: the more the bank relies on market funding the higher is its capital buffer.

Secondly, there is a variety of uninsured debt holders of banks and they may behave differently. A large part of the literature on market discipline is dedicated to subordinated debt [START_REF] Bliss | Market discipline and subordinated debt: A review of some salient issues[END_REF], [START_REF] Evanoff | Subordinated Debt and Bank Capital Reform[END_REF], [START_REF] Morgan | Market Discipline of Banks: The Asset Test[END_REF], [START_REF] Sironi | Testing for Market Discipline in the European Banking Industry: Evidence from Subordinated Debt Issues[END_REF]). The reason is that for market discipline to be effective, market participants must have the incentives to exert it, that is they must feel at risk, and subordinated debt holders are particularly at risk due to their junior status. Indeed, junior debt also named subordinated debt corresponds to a debt that has a lower priority than other debt in case of failure of the issuer. It comes after government tax authorities and senior debt holders in the hierarchy of creditors and just before equity. Thus, subordinated debt holders are particularly at risk and have higher incentives to monitor banks and to exert a discipline. Therefore, we distinguish junior from senior debt holders and study whether both of them exert a pressure on banks to hold capital buffer. We expect that the pressure of the market on banks to hold capital buffer may be different depending on the status of the creditors: senior or junior debt holders. Junior debt holders should have more incentives to exert a pressure on banks.

H2: The market pressure exerted by junior debt holders on banks to hold capital buffer is higher than the one exerted by senior debt holders.

Lastly, we depart from the fact that trading activities and securitizations have gained an increasing importance in recent years but that they are more imperfectly taken into account in the current Basel accords than bank traditional activities (BCBS (2009a), BCBS (2009b)).

We therefore conjecture that, the more the bank is involved in trading activities, the more capital buffer market participants require. Indeed, we assume that the market, contrary to regulators, can adapt quickly [START_REF] De Young | The Information Content of Bank Exam ratings and Subordinated Debt Prices[END_REF]) and consider the risk of these activities which are not well taken into account in the regulatory constraint. We hypothesize that the type of activity of banks affects capital buffer. The market pressure on banks heavily involved in non-traditional activities (market activities as opposed to loan activity) 10 to hold capital buffer may be higher than on those more turned towards traditional activities as it reflects the imperfection of the current regulation.

H3: The market pressure on banks to hold capital buffer is higher for those more involved in trading activities.

Model and main variables

To test our three hypotheses, we estimate the two following models. Subscripts i and t denote bank and period respectively.

, 0 1 , , , 1

J i t i t j ji t i t i t j buffer mktdisc C u              (1) , 0 1 , 2 , , , 1 _ _ J i t i t i t j ji t i t i t j buffer mktdisc senior mktdisc junior C u                (2)
Buffer is the regulatory capital buffer variable, mktdisc, mktdisc_senior, and mktdisc_junior the market discipline variables, C j the j th control variable and The dependent variable buffer corresponds to the amount of capital banks hold in excess of what is required by national regulators. More precisely, we construct the variable buffer as the bank's actual total risk-weighted capital ratio less its regulatory minimum 10 Non-traditional activities not only consist of market activities. For example, there are also insurance activities and other financial services. However, in this paper, we focus on activities generating market risks as they are considered to be imperfectly taken into account in the current Basel accords. 11 The regressions include individual and time fixed effects as the Fisher test rejects the null hypothesis of homogeneity in both individual and time dimensions.

requirement. This regulatory minimum requirement is set to 8% in most countries of our sample except in Germany where it is set to 12.5% for newly established banks in the first two years of business and in the United Kingdom where we consider 9%. Indeed, the Financial Stability Authority (FSA) sets two separate capital requirements for each UK bank: a 'trigger ratio', which is the minimum individual capital ratio; and a 'target ratio' set above the trigger. We therefore follow [START_REF] Jokipii | The cyclical behaviour of European bank capital buffers[END_REF] and consider 9% minimum capital requirement ratio for all UK banks.

Hypothesis H1 is tested by estimating Model 1 and testing the significance of the coefficient associated with our market discipline indicator mktdisc. We expect to find a positive and significant relationship with capital buffer. The market discipline indicator reflects the importance of market funded liabilities in total liabilities. This ratio is constructed as (total liabilities minus total deposits)/ total liabilities.

In order to test our second hypothesis H2, we estimate Model 2 in which we replace the previous market discipline indicator by two separate indicators: one for senior debt (mktdisc_senior) and one for junior debt (mktdisc_junior). Our variable mktdisc_junior corresponds to the ratio of subordinated debt to total liabilities 12 . The ratio of senior market debt mktdisc_senior is constructed as (total liabilities minus total deposits 13 minus subordinated debt)/ total liabilities 14 . This ratio considers only senior market debt that is market debt that takes priority over junior debt. In case of bank default, senior debt holders are reimbursed before junior debt holders. We expect to find higher significance level and/or higher coefficient magnitude for the variable mktdisc_junior than for the variable mktdisc_senior.

To test the third hypothesis H3 that is whether the pressure exerted by market participants on banks to hold capital buffer is different depending on bank activities, we estimate Models 1 and 2 on different sub-samples defined on the basis of two alternative

12 Tier 2 contains subordinated debt that consists only in "conventional unsecured subordinated debt capital instruments with a minimum original fixed term to maturity of over five years and limited life redeemable preference shares" and that is limited to 50% of Tier 1 (BCBS, 1988). However, this does not imply a straightforward positive relationship between our independent variable of interest mktdisc_junior and buffer. Indeed, our dependent variable is not Tier 2 but capital buffer. Even if a bank issues subordinated debt to increase its Tier 2, it does not necessarily imply that it holds a higher capital buffer. For example, the bank can substitute Tier 2 to Tier 1. Besides, to check that our results are not mechanically driven by the potential inclusion of some subordinated debt into Tier 2 capital,we have run regressions considering only subordinated debt which cannot be potentially eligible for Tier 2. Our conclusions remain unchanged (see section 3.2.). 13 Our variable mktdisc_senior does not contain interbank deposits as they are included in total deposits. 14 We also consider as a robustness check a narrower definition of senior debt focusing on money market funding. This leads to the same conclusions (see section 3.2.).

ratios. First, we consider the revenue generated by trading activities and construct the ratio of net trading revenue to net operating income where net operating income is defined as net interest income plus net non interest income 15 . We also consider the rough ratio of off-balance sheet activities to total assets as another proxy for the involvement of banks in non-traditional activities which generate market risk. The higher are these ratios, the higher is the involvement of banks in non-traditional activities. These ratios are used alternatively to separate our sample in two groups. For each ratio, we separate banks with a value of the considered ratio higher than the median from those with a ratio lower than the median 16 . Our hypothesis is that our market discipline variables may be more significant or only significant for banks more involved in non-traditional activities.

In all our regressions, in line with the existing literature, we consider several control variables C j likely to explain banks' capital buffer.

Following [START_REF] Flannery | What Caused the Bank Capital Build-up of the 1990s?[END_REF], we consider the fact that capital buffer could simply reflect an unusual period of bank profitability. When raising new capital is costly, capital accumulation could rely on internally generated funds, in line with the "Pecking order theory" of capital structure. Bankers may increase capitalization through higher retained earnings and weaker dividend payments and stock repurchase. We therefore expect a positive relationship between profit, which is defined as post tax profit/ total assets, and capital buffer.

In a world different from that of [START_REF] Modigliani | The cost of capital, corporate Finance and the theory of investment[END_REF], equity is more costly compared to other bank liabilities because of information asymmetries. Equity may also be disadvantaged because interest payments on debt are deducted from earnings before tax.

Capital buffer is hence expected to be negatively associated with the cost of equity. However, direct measurement of this cost is difficult. Therefore, previous studies have considered the return on equity (ROE) as a proxy variable for the direct cost of capital buffer 17 .

We consider the ratio of loan loss provisions to net loans (llpnl) as the risk variable and the expected sign between this variable and capital buffer is not clear cut. Indeed, on the 15 Net non interest income is defined as the sum of net commission and fee income and net trading revenue. 16 Note that 63% of the observations are classified similarly on the basis of these two different criteria. As a robustness check, we also consider another criterion than the median. In order to have sub-samples of banks with very different characteristics in terms of activity, we consider the median value of the considered ratio and delete the 10% of our sample observations with a value of the ratio around the median. Then, we separate banks with low values from banks with high values. Using this criterion leads to similar conclusions (see section 3.2.). 17 As stressed by [START_REF] Jokipii | The cyclical behaviour of European bank capital buffers[END_REF], ROE reflects both cost and revenue and is strongly correlated with the profit variable (in our sample, the coefficient of correlation between ROE and profit is of 77.4%). As the cost of equity may be an important determinant of capital buffer, we deal with the issue of correlation by orthogonalizing the variable ROE with our profit variable. The variable roe used in our regressions corresponds to the orthogonalized variable. Thus, we make sure that we do not omit an important determinant.

one hand a strand of literature outlines a significant positive impact of risk on capital [START_REF] Flannery | What Caused the Bank Capital Build-up of the 1990s?[END_REF], [START_REF] Gropp | The determinants of Bank Capital Structure[END_REF] and [START_REF] Berger | How Do Large Banking Organizations Manage Their Capital Ratios?[END_REF]). The rationale for this finding is that good bank management implies that the more the risk the bank plans to take, the more the capital it keeps aside. On the other hand, there is another strand of literature that supports the idea that the increase of ex-post measure of risk should lower capital buffer given that capital is kept to face unexpected losses [START_REF] Ayuso | Are capital buffers pro-cyclical? Evidence from Spanish panel data[END_REF], [START_REF] Nier | Market discipline, disclosure and moral hazard in banking[END_REF] and Fonseca and Gonzalez (2010)).

We also consider that banks which operate in a highly competitive environment are expected to hold more capital than prescribed by the regulator. The rationale for this behaviour might stem from the fact that capital buffer may serve as an instrument, which the bank is willing to pay for, in the competition with its peers for unsecured deposits and money market funding [START_REF] Lindquist | Banks' buffer capital: how important is risk?[END_REF], [START_REF] Dietrich | Why do banks hold capital in excess? A functional approach[END_REF], [START_REF] Bernauer | Taking Firms and Markets Seriously: A Study on Bank Behavior, Market Discipline, and Regulation[END_REF],

and [START_REF] Schaeck | Banking Competition and Capital Ratios[END_REF]). Thus, we consider the annual mean of capital buffer of the bank's competitors in the same country, comp, which should positively affect capital buffer.

All else equal, an increase in assets through loans should increase capital requirements and therefore decrease capital buffer [START_REF] Ayuso | Are capital buffers pro-cyclical? Evidence from Spanish panel data[END_REF]). Thus, we expect a negative relationship between loang, the annual net loans growth rate, and the dependent variable. The importance of loans activity may also affect capital buffer. Indeed, we assume that loans activities are relatively better taken into account into the capital regulatory constraint than other non-traditional activities. Hence, we consider the variable nla, corresponding to the proportion of net loans in total assets, and expect a negative relationship between capital buffer and this variable.

A consensus among the previous literature also emerges: it indicates that larger banks hold less average capital in excess of regulatory requirements due to scale economies in screening and monitoring and larger diversification. The dependent variable should be negatively related to size that is the natural logarithm of total assets. Another reason for large banks to hold a smaller buffer may be their Too Big To Fail (TBTF) nature. Indeed, if a bank is perceived as TBTF, this implies that it benefits from government implicit guarantee.

Consequently, it could be less prudent in the building of its capital buffer.

The level of capital banks hold may also depend on macroeconomic conditions. We therefore introduce the business cycle to determine whether it has any effect on the capital held by institutions. Previous studies have mostly shown that capital buffer and economic cycle tend to be negatively linked [START_REF] Ayuso | Are capital buffers pro-cyclical? Evidence from Spanish panel data[END_REF], [START_REF] Lindquist | Banks' buffer capital: how important is risk?[END_REF], [START_REF] Jokipii | The cyclical behaviour of European bank capital buffers[END_REF]). This is to say that banks tend to decrease their capital buffer during the upturn and increase it in the downturn. The rationale for this finding may be found in [START_REF] Berger | The role of capital in financial institutions[END_REF] who argue that banks may hold capital buffer to be able to exploit unexpected investment opportunities. Thus, we expect a negative link between the annual growth rate of the real Gross Domestic Product 18 , gdpg, and capital buffer.

< Insert Table 1 > Table 1 summarizes our set of variables with some descriptive statistics on our sample of banks that we present in the following section 19 .

Presentation of the sample

Our sample consists of commercial banks 20 established in 16 European countries 21 .

The sample period is from 1993 to 2006 22 . Accounting data (annual financial statements) for individual banks are obtained from Bankscope Fitch IBCA. Bankscope reports balance sheets and income statements for 1985 commercial banks for the countries we consider in this study.

Departing from these 1985 banks we end up with a sample of 742 banks. Indeed, the information about the total capital ratio 23 is available only for 766 banks among which 24 banks present outliers in the distribution of this ratio and were deleted. We verify that, on average, our sample of banks constitute over 56% of the banking assets of commercial banks of the respective sample countries in 2006 24 . We can notice that, except for four countries 18 We also consider the output gap obtained by applying the Hodrick-Prescott filter to the real GDP series as an alternative indicator and get similar results. 19 We observe that our dependent variable buffer is on average equal to 4.72% which stands for the extra capital ratio that European commercial banks hold in excess of the regulatory minimum requirement. Nevertheless, our sample discloses a minimum of -6.48% which means that some banks do not comply with the regulatory constraint. We verify that only few observations correspond to a negative buffer (less than 3% of total observations) and that this does not affect our results. Therefore we keep them in our sample in order to avoid a selection bias. However, given that our investigation relates to capital buffer, we perform two robustness checks in which we exclude banks with negative capital buffer or banks whose capital ratio is close to the regulatory minimum (see section 3.2 for details). 20 To identify commercial banks, we consider the Bankscope Fitch IBCA's classification. However, we notice that a bank classified as "commercial bank" can have a ratio (net loans/ total assets) equal to 0% or a ratio (market funding/ total liabilities) equal to 100%. Thus, to ensure that all the banks in our sample are commercial banks, i.e. they have loans and deposits activities, we clean our sample by deleting the observations of the ratios (net loans/ total assets) and (market funding/ total liabilities) that are respectively in the first and in the last percentile of their distribution. However, running our regressions with these observations does not affect our conclusions. 21 Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and United Kingdom (see table A1 in Appendix for details). 22 Notice that during the whole sample period banks are under the Basel I framework. 23 Total capital ratio is (Tier 1 + Tier 2)/ Risk weighted assets and is used to construct our dependent variable. 24 See table A1 in Appendix.

(Austria, Denmark, Germany and Switzerland), the final set of banks used in this study represents more than half of the banking system in terms of total assets of each country. Table 2 gives some descriptive statistics on the starting sample and on our final sample. It allows us to verify that our final sample does not considerably differ from the starting sample. Indeed, we consider 10 key variables and disclose their mean and their standard deviation for the full sample available in Bankscope and the final sample that we use. Overall, we can see that the two samples are very close even though the banks in our sample seem to be, on average, larger in terms of total assets.

< Insert Table 2 >

Results and robustness checks

Results

In line with the previous literature [START_REF] Ayuso | Are capital buffers pro-cyclical? Evidence from Spanish panel data[END_REF], [START_REF] Jokipii | The cyclical behaviour of European bank capital buffers[END_REF]), we suspect bank level variables to be endogenous, i.e. themselves dependent on capital buffer 25 .

Following [START_REF] Nier | Market discipline, disclosure and moral hazard in banking[END_REF], we therefore consider the TSLS procedure with estimators of variance-covariance matrix that are robust to heteroskedasticity. Our set of instruments consists of the one year lagged values of these variables.

< Insert Table 3 > First, we estimate a model with our control variables and the market discipline variable mktdisc (model 1) on the full sample of banks. The results are presented in Table 3 column (1). The coefficient associated with the variable mktdisc is significant at the one percent level with the positive expected sign. Thus, market participants exert a pressure on banks to hold capital buffer. This result is in line with previous studies [START_REF] Nier | Market discipline, disclosure and moral hazard in banking[END_REF], [START_REF] Flannery | What Caused the Bank Capital Build-up of the 1990s?[END_REF] and Fonseca and Gonzalez (2010)) which find, with 25 Indeed, a reverse relationship with capital buffer can exist for almost all our explanatory variables. For example, another recent literature deals with the role of capital buffer as a strategic variable to attract and monitor borrowers [START_REF] Allen | Credit market competition and capital regulation[END_REF] or to charge higher borrowing interest rates [START_REF] Kim | Endogenous product differentiation in credit markets: What do borrowers pay for?[END_REF] and [START_REF] Fischer | Bank Capital Ratios, Competition and Loan Spreads[END_REF]). In our models, following the previous literature on the determinants of capital buffer, the only bank level variable which is considered as exogenous is the size of the bank.

different proxy variables, that market discipline is an important factor to explain banks' capital ratios. Second, we split our market discipline indicator by separating junior from senior debt holders. We can see (column (2)) that both exert a significant pressure on banks to hold capital buffer. This result is shown through the high level of significance (at the 1% level) of the both positive coefficients associated with the variables mktdisc_senior and mktdisc_junior. However, consistent with the second hypothesis, the mktdisc_junior coefficient is 13 times higher than the one of mktdisc_senior26 .

The remaining columns of Table 3 present the results obtained by estimating models

(1) and ( 2) on different sub-samples defined on the basis of the degree of involvement of the bank in non-traditional activities. Hence, we study whether the pressure of the market taken globally or the pressure of junior and senior debt holders taken separately on banks to hold capital buffer is different depending on their activity. We consider two different ratios to split banks into two different categories. When we consider the importance of trading activities through the ratio net trading revenue/ net operating income, we find that the ratio of market funded liabilities to total liabilities (mktdisc) is significant only for banks heavily involved in these activities (columns (3) and ( 4)). Consistent with hypothesis H3, this result implies that market participants exert a pressure only on banks that are highly involved in trading activities which are imperfectly taken into account in the current capital regulation. When we distinguish senior debt holders from junior debt holders, we notice that this result holds only for senior debt holders, junior debt holders always exert a pressure, whatever the importance of trading activities (columns ( 5) and ( 6)). The significance and the comparative high value of the coefficient of the mktdisc_junior variable irrespective of the bank's activity denote the high pressure exerted by these junior debt holders on banks to hold capital buffer. Using the ratio off-balance sheet activities/ total assets as an alternative criterion to separate banks gives similar results (columns ( 7)-( 10)). Indeed, the market funding variable (mktdisc) is significant at the five percent level only for banks that have a high proportion of off-balance sheet activities that is for banks highly involved in non-traditional activities whereas it is not significant for banks with a low ratio. Besides, we also find that this result holds for senior debt but is different for junior debt as the variable mktdisc_junior is significant whatever the importance of off-balance sheet activities.

To summarize, our results validate our three hypotheses H1, H2 and H3. Consistent with H1, we find that, after controlling for other determinants, market discipline is a significant determinant of banks' capital buffer. Both senior debt holders and junior debt holders seem to exert a pressure on banks to hold capital buffer. However, as assumed in hypothesis H2, this pressure is higher for junior debt holders. In accordance with hypothesis H3, we find a higher pressure of market discipline on banks highly involved in non-traditional activities. Indeed, for banks highly involved in non-traditional activities, our market discipline indicators are always significant to explain bank capital buffer. By contrast, for those less involved in such activities, the importance of market funded liabilities as a whole is always insignificant to explain capital buffer. However, the behavior of senior and junior debt holders appears different: senior debt holders do not exert a pressure on such banks whereas junior debt holders do. Thus, junior debt holders exert a pressure on banks to hold capital buffer whatever the importance of non-traditional activities.

These results might suggest that senior debt holders exert a pressure to hold capital buffer on banks heavily involved in non-traditional activities because these activities are not well taken into account by the capital regulation. The buffer required by senior debt holders would reflect the capital needed for the risks not correctly embedded into the capital constraint. This would explain why they do not exert a pressure on banks mainly involved in traditional activities: the risks generated by these activities are already taken into account in the capital constraint. By contrast, junior debt holders always require capital buffer whatever banks' activities. This result might be due to the junior status of these debt holders: they are particularly at risk in case of bank default which might explain that they require higher capital buffer. Irrespective of banks' activities, they require capital buffer because they find the capital required by regulation insufficient even for traditional activities.

Regarding the control variables, we can notice that the coefficient of the loan activity variable nla is almost always negative and highly significant. This expected finding shows that banks highly involved in credit activities hold less capital buffer. We also confirm the well known result which stipulates that large banks operate with less capital buffer than small banks [START_REF] Ayuso | Are capital buffers pro-cyclical? Evidence from Spanish panel data[END_REF] for instance). In fact, our variable size has a negative and significant coefficient across almost all our specifications. The peer pressure variable comp is significant in 6 out of 10 of our specifications and its coefficient is positive as expected.

Therefore, consistent with the findings from [START_REF] Lindquist | Banks' buffer capital: how important is risk?[END_REF] and [START_REF] Alfon | What determines how much capital is held by UK banks and building societies? Occasional paper No 22[END_REF], the higher the peer pressure is, the higher capital buffer banks hold. In addition, we can notice that it is more significant for banks highly involved in non-traditional activities and hence the bank peer discipline seems consistent with the market discipline. The loan growth variable (loang) is significant in some specifications and its coefficient has the expected negative sign.

The risk variable llpnl is not significant; a result backed by [START_REF] Lindquist | Banks' buffer capital: how important is risk?[END_REF] who shows with Norwegian data that risk is not a significant determinant of bank buffer under Basel I.

Contrary to the results of [START_REF] Jokipii | The cyclical behaviour of European bank capital buffers[END_REF], we find no significant relationship between the business cycle (gdpg) and bank capital buffer.

Robustness checks

Before checking the robustness of our results, we show in the first place that the significant and positive relationship between one of our variables of interest, mktdisc_junior, and regulatory capital buffer is not due to the potential inclusion of some of the subordinated debt in Tier 2 capital. Indeed, as we mentioned in section 2.2., we know that some subordinated debt can be eligible for Tier 2 and it consists in "conventional unsecured subordinated debt capital instruments with a minimum original fixed term to maturity of over five years and limited life redeemable preference shares. During the last five years to maturity, a cumulative discount (or amortization) factor of 20% per year will be applied to reflect the diminishing value of these instruments as a continuing source of strength" (BCBS, 1988). Unfortunately, we cannot isolate subordinated debt included in Tier 2 using standard databases. Therefore, we redefine our mktdisc_junior variable by eliminating the part of subordinated debt that might be included in Tier 2 27 . More precisely, for each bank, we consider the whole amount of subordinated debt less the maximum amount that can be included in Tier 2 that is 50% of Tier 1 or the amount of Tier 2 if it is less than 50% of Tier 1 28 . This restricts considerably our sample as we need to have information about the amount of both Tier 1 and Tier 2. Besides, this is a very restrictive definition of subordinated debt not included in Tier 2 as we cannot check for the maturity and the amortization factor. For these reasons, the regression model is run only using the whole sample. We define mktdisc_junior2

as the ratio of subordinated debt non potentially eligible for Tier 2 to total liabilities and mktdisc2 as the ratio of total market funding less subordinated debt potentially eligible for Tier 2 to total liabilities. The results obtained using these variables are presented in Table 4.

< Insert Table 4 >

27 Tier 2 is limited to 100% of Tier 1 and subordinated debt is limited to 50% of Tier 1. 28 If we find an amount of subordinated debt not eligible for Tier 2 which is negative, we normalize it to zero.

We find a highly significant (at 1% level) and positive relationship between mktdisc2 and capital buffer as before. More importantly, despite the high restrictions imposed on our new variable mktdisc_junior2, consistent with our main results, we still find a positive relationship between this restricted new variable reflecting the pressure of junior debt holders (mktdisc_junior2) and capital buffer even if less significant (at 10% instead of 1% level).

Moreover, our second hypothesis (H2) is still validated as the coefficient magnitude of mktdisc_junior2 is more than 5 times higher than that of mktdisc_senior. Overall, we conclude that the positive and significant relationship between mktdisc_junior and capital buffer is not mechanically driven by the potential inclusion of some subordinated debt into Tier 2 capital.

We then perform several robustness checks reported in appendix in Tables A2 to A9.

First, in our regressions, we consider capital buffer of banks without any restriction.

To check the robustness of our results, we perform new regressions restricting our sample to

(1) positive capital buffer and, (2) capital buffer higher than 1, to deal with the issue that buffers could be explained by the fear of falling below the minimum regulatory requirement.

We re-run all the regressions and we obtain the same conclusions (see Tables A2 andA3).

Second, we perform a robustness check regarding a potential sample bias. French and Italian banks are comparatively more represented in our sample. To make sure that our results

do not depend on this unbalanced sample representation, we run again all our regressions by excluding the banks from these two countries. We also find that the conclusions remain globally unchanged (Table A4). The only noticeable difference is that the market discipline variable mktdisc_junior is no longer significant for banks with a low proportion of off-balance sheet activities.

Third, banks in United Kingdom are somewhat differently regulated compared to other European banks in our sample (cf 2.2. and FSA (2001)29 for details). Therefore, in our main regressions, we consider 9% (instead of 8%) as the minimum regulatory capital requirement.

Thus, to ensure that this particular aspect of British banks regulation does not distort our results, we repeat all the regressions by excluding them. All our conclusions remain similar (Table A5). In order to take into account other potential differences in terms of capital regulation across European countries, we also introduce a capital regulatory variable. This country level variable is constructed from the databases of Barth et al. (2000Barth et al. ( , 2003Barth et al. ( , and 2007) ) and is a measure of capital regulatory stringency 30 . This variable appears significant in only two regressions and our conclusions remain the same (Table A6).

Fourth, we can suspect that some banks in our sample have experienced mergers and acquisitions during the considered period. Unfortunately, we have no direct way to identify those banks. An indirect way to do so is to look at the bank's total assets growth. Hence, we compute the total assets growth rate and we exclude banks that have experienced a growth rate exceeding 40 % 31 . We find similar results except that the market discipline variable mktdisc_junior is no longer significant for banks with a low proportion of trading revenues (Table A7).

Fifth, concerning the separation of our sample in two sub-samples on the basis of the values of the ratios net trading revenue to net operating income and off-balance sheet activities to total assets, we consider another criterion than the median. In order to have subsamples of banks with very different characteristics in terms of activity, we consider the median value of the considered ratio and delete the 10% of our sample observations with a value of the ratio around the median. Then, we separate banks with low values from banks with high values. This criterion ensures that banks in the high category one year are not in the low category the year after. Using this criterion leads to similar conclusions (Table A8).

Finally, as we consider a broad definition of senior debt corresponding to total liabilities minus total deposits and subordinated debt, we decide to check the robustness of our results using a narrower definition that focuses only on total money market funding. Total money market funding corresponds to certificates of deposits, commercial paper, debt securities, securities loaned and other securities. Thus, our senior debt variable mktdisc_senior2 consists of total money market funding/ total liabilities. Our conclusions concerning the discipline exerted by senior debt holders remain unchanged even though the significance of the new senior debt variable (mktdisc_senior2) becomes slightly lower compared with our main results (Table A9). 30 To construct this variable, we use the 2000 database for the 1992-2000 period, the 2003 database for the 2001-2003 period and the 2007 database for the 2004-2006 period. This variable corresponds to the capital regulatory index defined in [START_REF] Barth | Bank regulation and supervision: what works best?[END_REF]. 31 As there is no objective cut-off, we have considered other percentages less restrictive (50%) and more restrictive (30%) and we have found similar conclusions.

Conclusion

The aim of this paper was to investigate firstly whether market participants taken globally lead banks to hold a capital ratio higher than the minimum regulatory capital requirement. Secondly, we went a step further and studied whether market participants who are highly exposed to losses in case of bank failure (junior debt holders) exert a higher pressure than those (senior debt holders) less exposed to it. Finally, we investigated if market participants, taken globally or not, differentiate banks according to their involvement in nontraditional activities inappropriately taken into account in the Basel capital regulation framework.

Using an unbalanced panel data of European commercial banks from 16 countries on 1993-2006, our results show that, after controlling for other determinants of capital buffer, the higher the reliance on market funding is, the higher capital buffer banks hold. We also show that when we distinguish junior from senior debt holders, although they both have a positive impact on capital buffer, the former exert a higher pressure on banks to hold capital buffer due to their junior status. When we differentiate traditional from non-traditional bank activities, our results indicate that market players taken as a whole require capital buffer only for nontraditional activities reflecting the idea that they take into account the slow reaction of regulators concerning the rapid changes of bank activities. Besides, contrary to senior debt holders, junior debt holders do not distinguish banks according to their activities and exert a pressure whatever the importance of non-traditional activities.

These results highlight the benefits of the use of market discipline in complement to capital regulation: banks subject to market discipline behave more prudently as the pressure exerted by debt holders leads them to hold higher capital buffer. Besides, consistent with the proposals for mandatory subordinated debt, we show that this debt is the most disciplining one: junior debt holders exert a pressure on banks to hold capital buffer whatever their activities and this pressure is always higher than the one exerted by senior debt holders.

However, one of the limits of mandatory subordinated debt is that due to its cost, it cannot be implemented for all banks. Interestingly, our results indicate that senior debt can also be an effective tool for market discipline. Indeed, we find that senior debt holders require capital buffers for banks involved in non-traditional activities that is when capital regulation is supposed to be the less efficient. This table shows estimation results obtained using the TSLS method. Our set of instruments consists of the one year lagged value of the endogenous variables. The regression includes time and individual fixed effects. Trading revenue/ net operating income is considered as high (low) if it is greater (lower) than the median value on the whole sample (6.10%). Off-balance sheet activities/ total assets is considered as high (low) if it is greater (lower) than the median value on the whole sample (18.13%). Standard errors are adjusted robust to heteroskedasticity. ***, ** and * pertain to 1, 5 and 10% level of significance, respectively. T-stats are between parentheses. Variables definition: Buffer = ((Tier 1 + Tier 2 capital)/ risk-weighted assets) -regulatory minimum requirements ; Nla = Net loans/ Total assets ; Mktdisc_Junior = Subordinated debt/ total liabilities ; Mktdisc_Senior = Other market funding/ total liabilities ; Mktdisc = Total market funding/ total liabilities ; Logta = Natural logarithm of total assets ; Comp = Annual mean of the buffer of banks in the same country ; Gdpg = Annual growth rate of the gross domestic product (deseasonalized) ; Roe = the residuals obtained when we regress the ratio (Net Income/ Total Equity) on the profit variable ; Llpnl = Loan loss provisions/ Net loans ; Loang = Annual net loan growth ; Profit = Post tax profit/ Total assets. This table shows estimation results obtained using the TSLS method. Our set of instruments consists of the one year lagged value of the endogenous variables. The regression includes time and individual fixed effects. Trading revenue/ net operating income is considered as high (low) if it is greater (lower) than the median value on the sample (6.10%). Off-balance sheet activities/ total assets is considered as high (low) if it is greater (lower) than the median value on the whole sample (18.13%).

             Model 2: , 0 1 , 2 , , , 1 2 __

  Standard errors are adjusted robust to heteroskedasticity. ***, ** and * pertain to 1, 5 and 10% level of significance, respectively. T-stats are between parentheses. Variables definition: Buffer = ((Tier 1 + Tier 2 capital)/ risk-weighted assets) -regulatory minimum requirements ; Nla = Net loans/ Total assets ; Mktdisc_Junior = Subordinated debt/ total liabilities ; Mktdisc_Senior = Other market funding/ total liabilities ; Mktdisc = Total market funding/ total liabilities ; Logta = Natural logarithm of total assets ; Comp = Annual mean of the buffer of banks in the same country ; Gdpg = Annual growth rate of the gross domestic product (deseasonalized) ; Roe = the residuals obtained when we regress the ratio (Net Income/ Total Equity) on the profit variable ; Llpnl = Loan loss provisions/ Net loans; Loang = Annual net loan growth ; Profit = Post tax profit/ Total assets.

Table 1 : Presentation of the dependent and independent variables with their descriptive statistics on our sample period (1993-2006) 33

 1 Notice that in our regressions, the variable roe corresponds to the residuals of the regression of the Return on Equity on our profit variable (see footnote 17).

	Variable	Mnemonic	Definition	Mean	Median	Standard deviation	Min	Max	Expected Sign of the coefficient
	Capital buffer	buffer	((Tier 1 + Tier 2 capital)/Risk-weighted assets) -regulatory minimum requirements	4.72	3.70	4.45	-6.48	33.30
	Profitability	profit	Post tax profit/ Total assets	0.69	0.63	0.78	-5.56	8.26	+
	Equity cost	roe	Return on equity = Net income/ Equity 33	9.53	10.18	10.49	-98.81	56.37	-
	risk	llpnl	Loan loss provisions/ Net loans	0.96	0.66	1.10	0.00	15.65	-/+
	Peer discipline	comp	Annual mean of the buffer of banks in the same country	6.29	6.01	1.87	2.03	13.62	+
	Asset structure	nla	Net loans 34 / Total assets	56.33	56.84	17.82	2.12	95.37	-
		mktdisc	Total market funding 35 / Total liabilities	23.27	21.86	16.81	0.48	87.43	+
	Market discipline	mktdisc_junior	Subordinated debt/ Total liabilities	1.87	1.74	1.59	0.00	13.59	+
		mktdisc_senior	Other market funding 36 / Total liabilities	21.39	19.92	16.74	0.48	85.92	+
	Credit demand	loang	Annual net loan growth rate	11.28	9.51	20.43	-75.41	234.18	-
	Economic cycle	gdpg	Annual growth rate of the real gross domestic product (deseasonalized)	2.38	2.24	1.76	-0.99	15.43	-
	Size	size	Natural logarithm of total assets	15.44	15.46	2.34	10.40	20.63	-

34

Net loans are: gross loansloan loss reserves.

35

Total market funding corresponds to Total Liabilities minus total deposits.

36

Other market funding corresponds to Total Liabilities minus total deposits minus subordinated debt.

Table 2 : Descriptive statistics on average over the period 1993-2006

 2

		Full sample of commercial banks	Our sample
		available in Bankscope
		Mean	Std. Dev.	Mean	Std. Dev.
	Total assets	13 185.34	66 569.40	44 929.00 105 000.00
	Total deposits/ total assets	70.16	21.64	70.67	14.78
	Net loans/ total assets	48.25	28.39	56.33	17.82
	Loan loss provisions/ total	0.62	1.00	0.52	0.57
	assets
	Return on assets = Net	0.76	3.05	0.72	0.82
	income/ total assets
	Net trading revenue/ Net	9.41	24.81	7.52	8.62
	operating income
	Equity/ Total assets	10.55	8.99	7.42	3.94
	Tier 1/ risk weighted	11.29	6.96	9.37	4.09
	assets
	(Tier 1 + Tier 2)/ risk	14.24	6.41	12.80	4.47
	weighted assets
	Off-balance sheet	28.59	75.87	26.00	27.50
	activities/ total assets

All variables are expressed in percentages, except Total assets which is in millions of Euros.

Table 3 : Capital buffer, market discipline and bank activity differentiation

 3

														J
	Model 1:	, i t buffer	0    	1	, i t mktdisc				j	C	, ji t	i      t	, i t u
													j	1 
																							J
	Model 2:	i buffer	,	t	0   1  	mktdisc	, __ 2 i t senior mktdisc  	i junior	,	t				j	C	ji	,	t	i      t	i u	,	t
																							j	1 
	Eq Name:				(1)		(2)			(3)					(4)	(5)		(6)	(7)	(8)	(9)	(10)
	Model:	Model 1 Model 2 Model 1 Model 1 Model 2 Model 2 Model 1 Model 1 Model 2 Model 2
	Sample:				Whole sample	(Trading revenue/ net operating income) high	(Trading revenue/ net operating income) low	(Trading revenue/ net operating income) high	(Trading revenue/ net operating income) low	(Off-balance sheet activities/ total assets) high	(Off-balance sheet activities/ total assets) low	(Off-balance sheet activities/ total assets) high	(Off-balance sheet activities/ total assets) low
	Mktdisc		0.084						0.106			0.047				0.107	0.098
		(3.824)***					(3.590)*** (1.069)				(2.371)** (0.714)
	Mktdisc _Senior				0.065											0.093	0.006	0.100	0.046
						(3.083)***											(2.940)*** (0.131)	(2.353)** (0.663)
	Mktdisc_Junior				0.892											0.690	0.862	0.793	1.055
						(3.815)***											(3.759)*** (2.362)**	(2.852)*** (3.484)***
	Nla	-0.118	-0.136		-0.096			-0.121	-0.107	-0.145	-0.067	-0.177	-0.087	-0.172
		(-5.489)*** (-6.243)*** (-3.644)*** (-3.093)*** (-3.480)*** (-3.445)*** (-1.124) (-3.364)*** (-1.348) (-5.185)***
	Size	-2.672	-2.720		-2.129			-1.723	-2.211	-1.794	-0.921	-4.730	-0.985	-4.012
		(-6.216)*** (-6.018)*** (-4.603)*** (-1.594) (-4.353)*** (-1.330) (-0.616) (-2.094)** (-0.654) (-3.405)***
	Comp		0.178	0.208		0.270			0.033	0.272	0.082	0.200	-0.010	0.204	-0.026
		(2.231)** (2.343)** (3.755)*** (0.382) (3.447)*** (0.974) (1.917)* (-0.058) (1.878)* (-0.270)
	Gdpg		0.057	0.032		0.014			0.170	-0.008	0.155	0.045	-0.278	0.028	-0.153
		(1.253)	(0.727)		(0.298)			(0.965)	(-0.156)	(0.761)	(0.494)	(-0.563)	(0.306)	(-0.616)
	Roe	-0.001	0.037		-0.122			-0.012	-0.019	-0.029	-0.041	0.042	0.026	-0.086
		(-0.031)	(0.762)	(-0.992) (-0.151) (-0.131) (-0.311) (-0.154)	(0.130)	(0.096)	(-0.447)
	Llpnl	-0.875	-1.113		-0.998			1.222	-1.377	1.837	-0.403	-1.730	-0.279	-0.685
		(-0.645) (-0.732) (-0.898) (0.591)	(-0.984)	(0.766)	(-0.293) (-0.334) (-0.202) (-0.320)
	Loang	-0.012	-0.016		-0.014			0.000	-0.018	0.002	-0.021	-0.010	-0.023	-0.006
		(-1.319) (-1.489) (-1.763)* (0.015) (-1.831)* (0.163) (-2.953)*** (-0.364) (-3.060)*** (-0.492)
	Profit		0.654	0.256		0.954			2.771	0.327	3.034	3.247	1.671	3.181	0.862
		(0.742)	(0.255)		(1.196)			(1.519)	(0.303)	(1.452)	(0.556)	(1.192)	(0.539)	(0.749)
	Constant	49.978	51.055		39.759			33.356	41.641	34.191	16.853	87.346	17.617	74.999
		(7.087)*** (6.909)*** (5.454)*** (1.849)* (5.202)*** (1.521)	(0.535) (2.314)** (0.557) (3.897)***
	Nb of Obs.:	1902	1902			951					951	951		951	952	951	952	951
	R-squared:	0.806	0.805		0.870			0.854	0.855	0.852	0.746	0.782	0.731	0.853

Table 4 : Capital buffer, market discipline and bank activity differentiation excluding subordinated debt potentially eligible for Tier 2.

 4

							J
	Model 1:	, i t buffer	0	1	mktdisc	2	, i t	j	C	, ji t	i	t	, i t u
							j	1

Table A1 : Distribution of banks by country and percentage of the total banking assets of each country present in our sample in 2006 Country Number of banks Percentage of the total banking assets present in our final sample

 A1 This table shows estimation results obtained using the TSLS method. Our set of instruments consists of one the year lagged value of the endogenous variables. The regression includes time and individual fixed effects. Standard errors are adjusted robust to heteroskedasticity. ***, ** and * pertain to 1, 5 and 10% level of significance, respectively. T-stats are between parentheses. Variables definition: Buffer = ((Tier 1 + Tier 2 capital)/ risk-weighted assets) -regulatory minimum requirements ; Nla = Net loans/ Total assets ; Mktdisc_Junior2 = (Total Subordinated debt-subordinated debt potentially eligible for Tier 2)/ total liabilities ; Mktdisc_Senior = Other market funding/ total liabilities ; Mktdisc2 = (Total market funding -subordinated debt potentially eligible for Tier 2) / total liabilities ; Logta = Natural logarithm of total assets ; Comp = Annual mean of the buffer of banks in the same country ; Gdpg = Annual growth rate of the gross domestic product (deseasonalized) ; Roe = the residuals obtained when we regress the ratio (Net Income/ Total Equity) on the profit variable ; Llpnl = Loan loss provisions/ Net loans ; Loang = Annual net loan growth ; Profit = Post tax profit/ Total assets.

	APPENDIX:
													J
	i t buffer	   	i t mktdisc senior			mktdisc junior	i t				j	C	ji t	i      t	i t u
	Austria				19						j		12.60
	Belgium Denmark Finland	Eq Name: Model: Sample:		18 65 11			(1) Model 1		74.53 22.98 Whole sample 51.07	(2) Model 2
	France				147								73.27
	Mktdisc2 Mktdisc_Senior Germany Greece Ireland	28 18 14			0.096 (3.763)***					45.31 61.94 68.83	0.092 (3.672)***
	Italy	Mktdisc_Junior2	198								67.94	0.504
	Netherlands Norway Portugal	Nla Size		50 21 20			-0.148 (-7.287)*** -3.077					67.84 66.50 67.93	(1.697)* -0.148 (-7.309)*** -3.125
	Spain				31			(-5.322)***					67.84	(-5.395)***
	Sweden Switzerland	Comp Gdpg		31 20			0.057 (0.804) 0.023					69.39 22.64	0.054 (0.765) 0.015
	United Kingdom		51			(0.237)					68.56	(0.157)
	Roe Llpnl Source: Bankscope Fitch IBCA Total 742			-0.101 (-1.466) -0.279					56.82	-0.092 (-1.376) -0.239
									(-0.395)							(-0.337)
				Loang					-0.009							-0.010
									(-1.384)							(-1.502)
				Profit					1.237							1.161
									(2.880)***							(2.734)***
			Constant					57.481							58.244
									(6.260)***							(6.328)***
			Nb of Obs.:					1249							1249
			R-squared:					0.838							0.837

Table A2 : Capital buffer, market discipline and bank activity differentiation: the case of banks with positive buffer.

 A2 This table shows estimation results obtained using the TSLS method. Our set of instruments consists of the one year lagged value of the endogenous variables. The regression includes time and individual fixed effects. Trading revenue/ net operating income is considered as high (low) if it is greater (lower) than the median value on the whole sample (6.10%). Off-balance sheet activities/ total assets is considered as high (low) if it is greater (lower) than the median value on the whole sample (18.13%).

													J
	Model 1:	, i t buffer	0    	1	, i t mktdisc				j	C	, ji t	i      t	, i t u
												j	1 
																						J
	Model 2:	i buffer	,	t	0   1  	mktdisc	, __ 2 i t senior mktdisc  	i junior	,	t			j	C	ji	,	t	i      t	i u	,	t
																						j	1 
																				(Trading	(Off-balance	(Off-balance	(Off-balance	(Off-balance
																				revenue/	sheet	sheet	sheet	sheet
																				net	activities/	activities/	activities/	activities/
																				operating	total assets)	total assets)	total assets)	total assets)
																				income) low	high	low	high	low
	Mktdisc	0.098					0.118				0.077				0.118	0.087
		(3.200)***				(3.471)***		(1.218)				(2.695)***	(1.240)
	Mktdisc_Senior				0.076										0.105		0.038	0.113	0.061
						(2.688)***										(2.747)***		(0.778)	(2.752)***	(1.067)
	Mktdisc_Junior				0.903										0.615		0.927	0.850	0.983
						(3.082)***										(3.269)*** (2.414)**	(3.367)*** (2.532)**
	Nla	-0.123	-0.141	-0.091				-0.128	-0.098		-0.143	-0.085	-0.176	-0.097	-0.179
		(-5.140)*** (-5.970)*** (-2.550)** (-3.506)*** (-2.215)** (-4.108)*** (-1.866)* (-4.678)*** (-1.879)* (-5.447)***
	Size	-2.436	-2.601	-1.858				-1.975	-1.917		-1.585	-0.816	-4.689	-1.070	-4.158
		(-5.452)*** (-5.628)*** (-3.009)*** (-2.109)** (-2.515)**		(-1.624)	(-0.511) (-3.053)*** (-0.627) (-3.231)***
	Comp	0.159	0.191	0.287				0.015	0.302		0.055	0.189	-0.042	0.185	-0.050
		(1.562)	(1.670)* (2.647)***		(0.166)	(2.250)**		(0.662)	(2.058)**	(-0.372)	(1.823)*	(-0.519)
	Gdpg	0.058	0.029	0.004				0.160	-0.015		0.171	0.047	-0.219	0.023	-0.178
	(1.172) (-0.899) Profit (0.620) (0.071) (0.885) (-0.229) (0.953) (0.472) 0.459 0.056 0.828 1.814 0.211 2.663 2.636 1.258	2.804	0.770
		(0.416)	(0.046)	(0.707)			(1.129)	(0.133)		(1.566)	(0.518)	(1.615)	(0.509)	(0.839)
	Constant	47.068	49.927	35.522				38.959	36.963		32.139	17.173	86.910	19.905	78.314
		(6.483)*** (6.628)*** (3.711)*** (2.451)** (3.135)*** (1.971)**	(0.562)	(3.515)***	(0.609)	(3.865)***
	Nb of Obs.:	1834	1834		922					912	922		912	925	910	925	910
	R-squared:	0.798	0.799	0.856				0.861	0.826		0.865	0.782	0.827	0.760	0.850
																						Standard errors are adjusted robust
	to heteroskedasticity. ***, ** and * pertain to 1, 5 and 10% level of significance, respectively. T-stats are between parentheses. Variables
	definition: Buffer = ((Tier 1 + Tier 2 capital)/ risk-weighted assets) -regulatory minimum requirements ; Nla = Net loans/ Total assets ;
	Mktdisc_Junior = Subordinated debt/ total liabilities ; Mktdisc_Senior = Other market funding/ total liabilities ; Mktdisc = Total market
	funding/ total liabilities ; Logta = Natural logarithm of total assets ; Comp = Annual mean of the buffer of banks in the same country ;
	Gdpg = Annual growth rate of the gross domestic product (deseasonalized) ; Roe = the residuals obtained when we regress the ratio (Net
	Income/ Total Equity) on the profit variable ; Llpnl = Loan loss provisions/ Net loans ; Loang = Annual net loan growth ; Profit = Post
	tax profit/ Total assets.

Table A3 : Capital buffer, market discipline and bank activity differentiation: the case of banks with a buffer of more than 1%.

 A3 This table shows estimation results obtained using the TSLS method. Our set of instruments consists of the one year lagged value of the endogenous variables. The regression includes time and individual fixed effects. Trading revenue/ net operating income is considered as high (low) if it is greater (lower) than the median value on the whole sample (6.10%). Off-balance sheet activities/ total assets is considered as high (low) if it is greater (lower) than the median value on the whole sample (18.13%).

											J
	Model 1:	, i t buffer	0    	1	, i t mktdisc				j	C	, ji t	i      t	, i t u
										j	1 
														J
	Model 2:	, i t buffer	0   1  	, __ 2 i t mktdisc senior mktdisc junior i t ,  			j	C	, ji t	i      t	, i t u
														j	1 
														(Trading	(Off-balance	(Off-balance	(Off-balance	(Off-balance
														revenue/	sheet	sheet	sheet	sheet
														net	activities/	activities/	activities/	activities/
														operating	total assets)	total assets)	total assets)	total assets)
														income) low	high	low	high	low
	Mktdisc	0.124					0.135			0.074	0.108	0.103
		(2.015)**					(3.676)***		(1.004)	(2.179)**	(1.551)
	Mktdisc_Senior		0.099							0.117	0.032	0.100	0.062
				(1.725)*							(2.572)**	(0.525)	(1.905)*	(1.223)
	Mktdisc_Junior		0.998							0.553	0.837	0.856	1.094
				(2.201)**							(2.513)** (2.030)**	(2.873)*** (2.994)***
	Nla	-0.115	-0.135	-0.088			-0.128	-0.090	-0.136	-0.075	-0.186	-0.091	-0.186
		(-3.065)*** (-3.894)*** (-1.562) (-3.824)*** (-1.101) (-4.086)*** (-1.447) (-4.843)*** (-1.539) (-5.422)***
	Size	-2.177	-2.440	-1.242			-1.973	-1.577	-1.494	0.461	-5.008	0.465	-4.355
		(-3.817)*** (-4.036)*** (-1.676)* (-2.008)** (-1.780)*	(-1.428)	(0.140) (-4.130)***	(0.115) (-4.779)***
	Comp	0.190	0.232	0.297			0.032	0.355	0.070	0.204	-0.041	0.197	-0.038
		(0.915)	(0.979)	(1.133)			(0.365)	(0.997)	(0.864)	(1.532)	(-0.434)	(1.233)	(-0.463)
	Gdpg	0.071	0.044	0.005			0.140	-0.007	0.148	0.063	-0.156	0.060	-0.100
		(1.182)	(0.848)	(0.094)			(0.843)	(-0.122)	(0.856)	(0.685)	(-0.936)	(0.567)	(-0.686)
	Roe	-0.099	-0.003	-0.187			-0.115	-0.024	-0.147	-0.288	0.016	-0.295	-0.093
		(-0.577)	(-0.012)	(-0.507)			(-1.331)	(-0.053)	(-1.655)*	(-0.395)	(0.089)	(-0.326)	(-0.509)
	Llpnl	-1.718	-1.855	-1.658			0.712	-2.294	1.065	-1.194	-0.223	-0.637	-0.099
		(-0.530)	(-0.528)	(-0.521)			(0.417)	(-0.515)	(0.662)	(-1.855)*	(-0.173)	(-0.956)	(-0.108)
	Loang	-0.018	-0.021	-0.018			-0.001	-0.023	-0.002	-0.025	-0.001	-0.027	-0.002
		(-0.791)	(-0.844)	(-0.919)			(-0.177)	(-0.869)	(-0.229) (-2.888)*** (-0.177) (-2.640)*** (-0.303)
	Profit	0.445	0.047	0.841			1.964	0.127	2.925	3.016	1.983	4.588	1.918
		(0.278)	(0.026)	(0.444)			(0.970)	(0.050)	(1.272)	(0.532)	(0.963)	(0.706)	(0.902)
	Constant	42.582	46.820	25.755			38.256	31.453	29.731	-2.312	90.942	-4.776	79.877
		(4.639)*** (4.812)*** (2.216)** (2.341)** (2.225)**	(1.686)*	(-0.041) (4.557)***	(-0.070) (5.409)***
	Nb of Obs.:	1669	1669		847				821	847	821	859	811	859	811
	R-squared:	0.760	0.757	0.843			0.860	0.794	0.859	0.742	0.837	0.696	0.863
														Standard errors are adjusted robust to
	heteroskedasticity. ***, ** and * pertain to 1, 5 and 10% level of significance, respectively. T-stats are between parentheses. Variables
	definition: Buffer = ((Tier 1 + Tier 2 capital)/ risk-weighted assets) -regulatory minimum requirements ; Nla = Net loans/ Total assets ;
	Mktdisc_Junior = Subordinated debt/ total liabilities ; Mktdisc_Senior = Other market funding/ total liabilities ; Mktdisc = Total market
	funding/ total liabilities ; Logta = Natural logarithm of total assets ; Comp = Annual mean of the buffer of banks in the same country ;
	Gdpg = Annual growth rate of the gross domestic product (deseasonalized) ; Roe = the residuals obtained when we regress the ratio (Net
	Income/ Total Equity) on the profit variable ; Llpnl = Loan loss provisions/ Net loans ; Loang = Annual net loan growth ; Profit = Post tax
	profit/ Total assets.

Table A4 : Capital buffer, market discipline and bank activity differentiation excluding French and Italian banks.

 A4

									J
	Model 1:	, i t buffer	0    	1	, i t mktdisc				j	C	, ji t	i      t	, i t u
								j	1 
														J
	Model 2:	, i t buffer	0   1  	, __ 2 i t mktdisc senior mktdisc junior i t ,  			j	C	, ji t	i      t	, i t u
														j	1 
														(Trading	(Off-balance	(Off-balance	(Off-balance	(Off-balance
														revenue/	sheet	sheet	sheet	sheet
														net	activities/	activities/	activities/	activities/
														operating	total assets)	total assets)	total assets)	total assets)
														income) low	high	low	high	low
	Mktdisc	0.079			0.151				0.076	0.127	0.152
		(2.369)**			(2.482)**			(1.255)	(1.822)*	(1.238)
	Mktdisc_Senior	0.060								0.135	0.052	0.116	0.147
			(2.122)**								(2.406)**	(0.862)	(1.773)*	(1.171)
	Mktdisc_Junior	0.777								0.567	1.112	0.776	0.291

Table A5 : Capital buffer, market discipline and bank activity differentiation excluding British banks.

 A5 .019)*** (-6.243)*** (-3.081)*** (-2.877)*** (-2.756)*** (-3.551)*** (-1.150) (-4.390)*** (-1.394) (-5.187)***

											J
	Model 1:	, i t buffer	0    	1	, i t mktdisc				j	C	, ji t	i      t	, i t u
										j	1 
														J
	Model 2:	, i t buffer	0   1  	, __ 2 i t mktdisc senior mktdisc junior i t ,  				j	C	, ji t	i      t	, i t u
														j	1 
	Eq Name:	(1)		(2)		(3)				(4)	(5)	(6)	(7)	(8)	(9)	(10)
	Model:	Model 1	Model 2	Model 1			Model 1	Model 2	Model 2	Model 1	Model 1	Model 2	Model 2
	Sample:
	Mktdisc	0.093					0.100			0.058	0.108	0.106
		(3.675)***					(3.238)***		(1.590)	(2.358)**	(0.954)
	Mktdisc_Senior		0.065							0.089	0.006	0.100	0.057
				(3.083)***							(2.637)***	(0.197)	(2.337)**	(0.836)
	Mktdisc_Junior		0.892							0.701	1.055	0.794	1.081
				(3.815)***							(3.778)*** (3.329)***	(2.830)*** (3.589)***
	Nla	-0.109	-0.136	-0.086			-0.124	-0.095	-0.158	-0.069	-0.170	-0.090	-0.173
	(-4Size -2.809	-2.720	-2.148			-1.794	-2.380	-1.866	-0.970	-4.869	-1.063	-4.211
		(-5.819)*** (-6.018)*** (-4.330)*** (-1.800)* (-4.530)*** (-1.715)*	(-0.627) (-2.648)*** (-0.686) (-3.554)***
	Comp	0.190	0.208	0.272			0.011	0.278	0.077	0.209	0.025	0.215	0.010
		(1.730)*	(2.343)** (3.403)***		(0.114)	(3.002)***	(0.846)	(1.523)	(0.102)	(1.521)	(0.075)
	Gdpg	0.072	0.032	0.021			0.227	-0.004	0.184	0.049	-0.205	0.028	-0.136
		(1.432)	(0.727)	(0.432)			(1.497)	(-0.083)	(1.186)	(0.517)	(-0.625)	(0.301)	(-0.636)
	Roe	0.015	0.037	-0.118			-0.020	0.013	-0.033	-0.030	0.061	0.041	-0.036
		(0.296)	(0.762)	(-0.811)			(-0.229)	(0.070)	(-0.339)	(-0.107)	(0.231)	(0.140)	(-0.209)
	Llpnl	-1.122	-1.113	-1.003			0.945	-1.436	1.436	-0.465	-1.681	-0.352	-1.002
		(-0.633)	(-0.732)	(-0.864)			(0.497)	(-0.934)	(0.723)	(-0.311)	(-0.403)	(-0.235)	(-0.466)
	Loang	-0.015	-0.016	-0.015			0.000	-0.019	0.001	-0.022	-0.010	-0.023	-0.008
		(-1.232)	(-1.489)	(-1.730)*		(0.042)	(-1.745)*	(0.173) (-2.962)*** (-0.434) (-3.058)*** (-0.570)
	Profit	0.606	0.256	0.959			2.756	0.257	2.880	3.112	1.803	2.960	1.111
		(0.597)	(0.255)	(1.144)			(1.558)	(0.217)	(1.616)	(0.510)	(1.590)	(0.483)	(1.115)
	Constant	51.245	51.055	39.451			34.352	43.649	35.925	17.753	87.783	19.151	77.220
		(6.570)*** (6.909)*** (5.106)*** (2.084)** (5.256)*** (2.010)**	(0.549)	(3.034)***	(0.593)	(4.191)***
	Nb of Obs.:	1766	1902		893				873	893	873	904	863	904	863
	R-squared:	0.783	0.805	0.869			0.846	0.846	0.852	0.734	0.767	0.721	0.837

Table A6 : Capital buffer, market discipline and bank activity differentiation taking into account capital regulatory stringency

 A6

												J
	Model 1:	, i t buffer	0    	1	, i t mktdisc				j	C	, ji t	i      t	, i t u
											j	1 
																						J
	Model 2:	i buffer	,	t	0   1  	mktdisc	, __ 2 i t senior mktdisc  	i junior	,	t			j	C	ji	,	t	i      t	i u	,	t
																					j	1 
																		(Trading	(Off-balance	(Off-balance	(Off-balance	(Off-balance
																		revenue/	sheet	sheet	sheet	sheet
																		net	activities/	activities/	activities/	activities/
																		operating	total assets)	total assets)	total assets)	total assets)
																		income) low	high	low	high	low
	Mktdisc	0.086				0.110				0.046					0.077	0.070
		(2.877)***			(3.395)***			(1.630)					(1.382)	(1.125)
	Mktdisc_Senior			0.064									0.093			0.009	0.047	0.032
					(2.310)**									(2.679)***			(0.330)	(0.865)	(0.656)
	Mktdisc_Junior			0.977									0.678			0.820	1.052	1.101

Table A7 : Capital buffer, market discipline and bank activity differentiation excluding banks that might have experienced M&A.

 A7

									J
	Model 1:	, i t buffer	0    	1	, i t mktdisc				j	C	, ji t	i      t	, i t u
								j	1 
														J
	Model 2:	, i t buffer	0   1  	, __ 2 i t mktdisc senior mktdisc junior i t ,  			j	C	, ji t	i      t	, i t u
														j	1 
														(Trading	(Off-balance	(Off-balance	(Off-balance	(Off-balance
														revenue/	sheet	sheet	sheet	sheet
														net	activities/	activities/	activities/	activities/
														operating	total assets)	total assets)	total assets)	total assets)
														income) low	high	low	high	low

Table A8 : Capital buffer, market discipline and bank activity differentiation considering another criterion to define sub-samples.

 A8

												J
	Model 1:	, i t buffer	0    	1	, i t mktdisc				j	C	, ji t	i      t	, i t u
											j	1 
																					J
	Model 2:	i buffer	,	t	0	1	mktdisc	, __ 2 i t senior mktdisc	i junior	,	t	j	C	ji	,	t	i	t	i u	,	t
																					j	1

[START_REF] Flannery | What Caused the Bank Capital Build-up of the 1990s?[END_REF] consider bank's quasi-market value of assets volatility as the risk variable and assume that bank counterparties require higher capital buffers accordingly. Thus, if BHCs are subject to market forces the coefficient associated with the risk variable should be significant and the more market discipline there is the higher should be the coefficient.

Fonseca and Gonzalez (2010) consider countries with very different banking systems and therefore, they are able to use the cost of deposits as a market discipline proxy.

Non-interest income includes trading income beyond commission and fee income.

This assumes that the conditions of effectiveness of market discipline are fulfilled. These conditions are: i) debt holders consider themselves at risk if the bank defaults, ii) they have sufficient information to assess bank risk and, iii) changes in the bank's risk profile have cost implications for the bank[START_REF] Nier | Market discipline, disclosure and moral hazard in banking[END_REF]).

We do not consider a price measure such as bond spread. Indeed, this would refer to ex-post market discipline and would assume that a higher spread leads a bank to increase its buffer in order to reduce its cost of funding. In this paper, we focus on ex-ante discipline and assume that banks exposed to market discipline change their behaviour ex-ante in order to avoid the costs imposed by market participants through higher spreads.

Besides, these two coefficients are statistically different at the one percent level of significance.

http://www.fsa.gov.uk/Pages/Library/Policy/Policy/2001/pscapitalratios.shtml



Eq Name:

( Variables definition: Buffer = ((Tier 1 + Tier 2 capital)/ risk-weighted assets) -regulatory minimum requirements ; Nla = Net loans/ Total assets ; Mktdisc_Junior = Subordinated debt/ total liabilities ; Mktdisc_Senior = Other market funding/ total liabilities ; Mktdisc = Total market funding/ total liabilities ; Logta = Natural logarithm of total assets ; Comp = Annual mean of the buffer of banks in the same country ; Gdpg = Annual growth rate of the gross domestic product (deseasonalized) ; Roe = the residuals obtained when we regress the ratio (Net Income/ Total Equity) on the profit variable ; Llpnl = Loan loss provisions/ Net loans; Loang = Annual net loan growth ; Profit = Post tax profit/ Total assets; Cap_index is a capital regulatory stringency index from [START_REF] Barth | Bank regulation and supervision: what works best?[END_REF]. It ranges from 0 to 9, with a higher value indicating greater stringency.



Eq Name:

( This table shows estimation results obtained using the TSLS method. Our set of instruments consists of the one year lagged value of the endogenous variables. The regression includes time and individual fixed effects. To define our sub-samples of banks, we consider the median value of the considered ratio (trading revenue/ net operating income or off-balance sheet activities/ total assets) and delete the 10% of our sample observations with a value of the ratio around the median. Then, we distinguish banks with low values from banks with high values. Trading revenue/ net operating income is considered as high (low) if it is greater (lower) than 6.15% (4.30%). Off-balance sheet activities/ total assets is considered as high (low) if it is greater (lower) than 20.17% (16.15%).Standard errors are adjusted robust to heteroskedasticity. ***, ** and * pertain to 1, 5 and 10% level of significance, respectively. T-stats are between parentheses. Variables definition: Buffer = ((Tier 1 + Tier 2 capital)/ risk-weighted assets) -regulatory minimum requirements ; Nla = Net loans/ Total assets ; Mktdisc_Junior = Subordinated debt/ total liabilities ; Mktdisc_Senior = Other market funding/ total liabilities ; Mktdisc = Total market funding/ total liabilities ; Logta = Natural logarithm of total assets ; Comp = Annual mean of the buffer of banks in the same country ; Gdpg = Annual growth rate of the gross domestic product (deseasonalized) ; Roe = the residuals obtained when we regress the ratio (Net Income/ Total Equity) on the profit variable ; Llpnl = Loan loss provisions/ Net loans ; Loang = Annual net loan growth ; Profit = Post tax profit/ Total assets. This table shows estimation results obtained using the TSLS method. Our set of instruments consists of the one year lagged value of the endogenous variables. The regression includes time and individual fixed effects. Trading revenue/ net operating income is considered as high (low) if it is greater (lower) than the median value on the whole sample (6.10%). Off-balance sheet activities/ total assets is considered as high (low) if it is greater (lower) than the median value on the whole sample (18.13%). Standard errors are adjusted robust to heteroskedasticity. ***, ** and * pertain to 1, 5 and 10% level of significance, respectively. T-stats are between parentheses. Variables definition: Buffer = ((Tier 1 + Tier 2 capital)/ risk-weighted assets) -regulatory minimum requirements ; Nla = Net loans/ Total assets ; Mktdisc_Junior = Subordinated debt/ total liabilities ; Mktdisc_Senior2 = Total money market funding/ total liabilities; Mktdisc = Total market funding/ total liabilities ; Logta = Natural logarithm of total assets ; Comp = Annual mean of the buffer of banks in the same country ; Gdpg = Annual growth rate of the gross domestic product (deseasonalized) ; Roe = the residuals obtained when we regress the ratio (Net Income/ Total Equity) on the profit variable ; Llpnl = Loan loss provisions/ Net loans; Loang = Annual net loan growth ; Profit = Post tax profit/ Total assets.


Ronan Gaugne

Jean-Baptiste Barreau

Gaétan Le Cloirec

Valérie Gouranton

Experiencing the past in virtual reality A virtual reality event for the French National Days of Archaeology

Keywords: Virtual reality, archaeology, digital heritage, scientific mediation. I

This document reports a public exhibition organized during the French National Days of Archaeology, that is the result of an interdisciplinary collaboration between archaeologists and computer scientists, centered on the immersive virtual reality platform Immersia, a node of the European Visionair project. This public exhibition had three main goals: (i) presentating our interdisciplinary collaboration, (ii) communicating on the scientific results of this collaboration, and (iii) offering an immersive experience in the past for visitors. This paper presents the scientific context of the event, its organization, and a discussion on feedbacks.

INTRODUCTION

In the context of the French Days of Archeology, the research organization WDCAH, West Digital Conservatory of Archaeological Heritage [START_REF] Barreau | The West Digital Conservatory of Archaeological Heritage project[END_REF], presented an immersive archeological reconstitution to scholar students and public visitors using virtual reality techniques. The WDCAH organization is an association between archaeologists from CReAAH (French acronym for Research Center in Archaeology, Achaeoscience and History) and computer scientists from IRISA (French acronym for Research Institute in Computer Science and Random Systems) which targets the reconstitution of archaeological sites using virtual reality. These reconstitutions rely on the technological platform Immersia which allows to immerse a user in a large virtual universe.

There has been a great deal of effort to communicate archaeological and historical heritage to a large public through various interactive digital media such as web [START_REF] Pescarin | Back to 2nd AD AVR on-line experience with Virtual Rome Project[END_REF], second life environments [START_REF] Morgan | re)building catalhoyuk : Changing virtual reality in archaeology[END_REF], or touchpads [START_REF] Bale | Linking Evidence with Heritage Visualization using a large Scale Collaborative Interface[END_REF]. The use of immersive virtual reality technology for public exhibition remains limited, mainly because of the underlying technical complexity, and also because of the cost of such facilities. An interesting example of such approach is presented in Christou et al. [START_REF] Christou | A versatile large-scale multimodal VR system for cultural heritage visualization[END_REF] where an immersive CAVE-like structure combined with haptic devices and 3D sound is used for pedagogic intentions in a museum exhibition.

The purpose of the underlying work presented in this paper was to build a 1:1 scale reconstitution of a disappeared Gallo-Roman villa in order to validate archaeological hypotheses, and better understand the operation of the global site. Virtual reality technology combined with the use of a large immersive platform made this project attainable [START_REF] Gaugne | Immersia, an open immersive infrastructure: doing archaeology in virtual reality[END_REF].

II. CONTEXT OF THE EVENT

A. West Digital Conservatory for Archaeological Heritage

WDCAH, is a French research organization whose aim is to both ensure the preservation of digital archaeological data, and deliver expertise in production, analysis, visualization and virtual reality exploration techniques. This interdisciplinary project gathers engineers and researchers in archaeology, computer science, virtual reality and 3D interaction with virtual environments. The major objectives of this conservatory project are: (i) sustainable and centralized safeguarding and archiving of 2D/3D data produced by the archaeological community; (ii) free access to metadata; (iii) secure access to data for the different actors involved in scientific projects and (iv) the support and advice for these actors in the 3D data production and exploration through the latest digital technologies, modeling tools and virtual reality systems.

B. Immersia platform

The immersive platform of the IRISA computer science laboratory is a large virtual-reality facility dedicated to realtime, multimodal (vision, sound, haptic, BCI) and immersive interaction. It hosts experiments using interactive and collaborative virtual-reality applications.

Images are rendered on four glass screens, a front one, two sides and a ground with an overall dimension of 10m wide, 3m deep and 3m high. Over 20 millions pixels are displayed using a video projection system combining thirteen 3D HD Barco 1 projectors. The tracking system is composed of 16 ARTTRACK2 infrared cameras by ART 2 . Sound is spatially rendered using a 10.2 sound system with speaker s or a 5.1 headset system, controlled by the user's position.

Immersia is a key node of the FP7 European Project Visionair [START_REF] Kopecki | VISIONAIR: VISION Advanced Infrastructure for Research[END_REF] which goal is to create a European infrastructure that should be a unique, visible and attractive entry towards high-level visualisation facilities for Virtual Reality, Scientific Visualisation, Ultra High Definition, Augmented Reality and Virtual Services. These facilities, distributed across about twenty countries in Europe, are open and easily accessible to a wide set of research communities. Both physical access and virtual services are provided by the infrastructure. Full access to visualization-dedicated software is offered through call for projects, while physical access to high level platforms will be partially accessible to other scientists, free of charge, based on the excellence of the project submitted. Immersia hosted three scientific projects from the beginning of Visionair, one in ergonomics, one in sport training, and one studying 3D sound perception in a large virtual environment.

C. French National Days of Archaeology

The National Days of Archaeology are annually organized by the French Ministry for Culture and Communication, and coordinated by the National Research Institute in Preventive Archaeology (Inrap). This three-day event aims at presenting to a large public the diversity of archaeology work through novel exhibitions and initiatives. All over the country, visits of archaeological sites, conferences, exhibitions, historical reconstitutions are proposed to discover local cultural Heritage.

The 2013 National Days of Archaeology, taking place in June, was the fourth edition of the event. It gathered 1167 local events and attracted around 120.000 visitors.

III. OBJECTIVES OF THE IMMERSIA EVENT

Among all the events of the National Days of Archaeology, the Immersia exhibition aimed at presenting the West Digital Conservatory of Archaeological Heritage project, and immersing visitors in a reconstitution of a Gallo-Roman site in virtual reality for a direct cognitive experience of the past.

The Immersia event was proposed for two days, the first one targeting only scholars. We wanted to emphasize on the scientific work behind the virtual reality reconstitution on both archaeological and computer science sides.

Our objectives were thus three-fold :

IV. SCIENTIFIC CONTENT OF THE IMMERSIA EVENT

The Immersia event relied on two scientific results:

1. The archaeological results of the Bais Gallo-Roman site excavation;

2. The reconstitution in virtual reality of the Gallo-Roman site from archaeologists' discoveries.

A. The Bais gallo-roman villa

The Gallo-Roman villa of Bourg Saint-Père in Bais is located between Vitré and Janzé, in Brittany, France. The site has been excavated between 2006 and 2009 by Pouille [START_REF] Cloirec | Bais, Le bourg St Père, proposition de restitution des volumes[END_REF]. There are no remaining buildings on the site, only foundations. Building foundations in the excavation.

The site consists in remains of a rural area with several buildings spreading over a surface of about two acres and dated from the first to the fourth century AD. The main building, the master's house, is 26m long and 13m large.

One of the main characteristics of the site is the unusual number of religious buildings. Archaeologists discovered two romano-celtic temples, "fana", consisting of a central cella surrounded by a covered porch with columns used for religious ceremonies, one Roman temple with a typical pediment, and one circular little building, a laraire, probably hosting the statue of a deity.

B. The VR implementation

From the result of the excavation, an archaeologist designed a 3D model of what the site should look like. The modeling work is integrated as early as possible in the process, both to help archaeologists in their research work and to ensure the scientific relevance of the resulting 3D model. In order to validate the hypotheses performed in the reconstitution steps, a 1:1 immersive reconstitution was implemented by computer scientists in virtual reality, starting from the 3D model built in the previous phase by archaeologists.

The implementation used the open-source VR software OpenMASK [START_REF] Margery | OpenMASK: Multi-Threaded or Modular Animation and Simulation Kernel or Kit: a General Introduction[END_REF]. Given the goals of the virtual reconstitution, the only interaction implemented was user navigation. More precisely, three navigation modes were proposed:

1. Thanks to the large size of the installation, the user can naturally walk on the 30m 2 of the floor screen.

The images are then adapted in real-time to the user's point of view while he is moving in the environment.

2. The user can rely on the joystick of the ART flystick, a wireless device tracked by the IR cameras, to navigate in the virtual world without moving his body.

3. A set of points of interest was predefined in the virtual universe, that the user could directly access by clicking on a button of the flystick.

The resulting immersive implementation was visited by archaeologists in order to evaluate the coherency of the reconstitution. The feedback was positive and all the architectural hypotheses were validated during working sessions in Immersia : in the 3D model of the site, the arcade of the frontage seemed disproportionate. The scale 1:1 transposition allowed to confirm the hypothesis. It was also interesting to appreciate the covisibility or occlusion relations between the different parts of the villa. The question of flows between the buildings was also easier to conceive. The application was deployed on a nine nodes cluster addressing all thirteen projectors. A tenth node handles the tracking by ART cameras, and a VRPN server, used to centralize the communication with the different interaction devices. Major technical issues to address were the distributed implementation, the rendering efficiency with respect to the resolution of the system and the stereoscopy. The execution was satisfying, with a frame rate of 60 fps, displaying more than 500.000 polygons, on a resolution of 20 millions of pixels, using quad buffer active stereoscopy.

V. ORGANISATION OF THE EVENT

The event was hosted in the "Espace des Technologies Innovantes", Fig. 4, a building in Rennes university campus where the Immersia platform is installed.

The Immersia event was organized in a three-step visit by groups of maximum 20 persons (see Fig. 5). The first day, the visit was booked for scholars. We received four classes, two from middle schools, and two from high schools, each class separated in two groups. The second day was open to every one, after a reservation process during the previous week. We received eight groups of 20 persons. First, a half-an-hour conference presented the scientific context of the work. Then the group moved to the immersive area to experience the virtual reconstitution of the Gallo-Roman site. At last, the group had a free time in an exhibition area. A. Amphitheatre presentation The first step of the Immersia event was a conference (Fig. 6) staged in three presentations: the first presentation focused on the West Digital Conservatory of Archaeological Heritage, the second one introduced Virtual Reality principles and the Immersia platform, and the third one described the Gallo-Roman site and the scientific approach leading to the digital reconstitution. We believe that this introduction was crucial for the visitors to understand the scientific content of the underling work and to prevent the immersive experience to be just considered as a fun entertainment.

B. Immersive experience

The immersive experience (Fig. 7) in the Immersia VR platform was obviously the most awaited for part of the event.

A strong constraint of the facility is that only one person can be immersed at a time. This limitation is due to the fact that only one point of view is valid with respect to the projection on the four orthogonal screens. Indeed, this projection is computed in real-time with respect to the user's head position and orientation.

Active stereoscopy and 1:1 display drove people in a strong immersive experience in past times. Navigation was limited to five points of interest (POI), and natural walking on the scene at each POI area. The first POI was a global view of the site, 6 meter high from ground, establishing the link with the previous presentation. The second POI was located around a well, in the courtyard in front of the villa building. The third one was located in front of one fanum, inside the covered porch with the colonnade. The fourth one was placed around a statue just in front the villa. The last one was placed upstairs a wooden staircase. The well and the staircase allowed people to strongly feel the effect of 3D. The temple area encouraged people to naturally walk all along the front colonnade and discover the organization of the different buildings while moving to the sides of the fanum. The statue encountered lot of success, everybody trying to "touch" it, and walking all around it. Such experiences are out of reach with simple PC displays or even with 3D cinema screens.

C. Exhibition

The last step was the exhibition area (Fig. 8) dedicated to the presentation of the missions and activities of Inrap. A VR reconstitution of a cairn [START_REF] Gaugne | Large-scale immersive reconstitution of a Neolithic corbel dome[END_REF] was displayed on a large screen (Fig. 9). A mouse interaction allowed navigation in 3D in the central chamber of the cairn. This reconstitution is part of an ongoing WDCAH project on several Neolithic sites in Brittany. Movies on Immersia construction, Inrap activities, and Bais excavation were displayed on a monitor. Several rollups, posters and booklets completed the exhibition. Researchers and professors from Inrap and IRISA were present in the exhibition area to exchange with visitors and answer to their questions.

At last, a guestbook was available to gather comments from visitors and collect feedback.

VI. EVENT FEEDBACK

Collecting and analyzing feedback is a mandatory yet uneasy task. We distinguished and exploited three sources to set up an assessment of the Immersia event: (i) visitors' behavior during the event, (ii) visitors feedback from the guestbook and discussions, and (iii) feedback from the fourteen volunteers involved in the organization of these two days.

A. Visitors behavior

While the immersive time was the most awaited for part of the event, people showed obvious interest in the two other areas. Many questions occurred during the presentations in the conference area, and researchers and profesors were highly sollicited in the exhibition area. Most of these questions were relevant, especially from young scholars. The mixing of thematics from archaeology, 3D and virtual reality seemed to be well understood, despite the technicity of some aspects, may be thanks to the digital images attraction which allowed to maintain a good level of attention.

During the immersive experience, people unfamiliar with virtual reality strongly experimented the presence feeling [START_REF] Lombard | At the Heart of It All: The Concept of Presence[END_REF], forgetting the underlying technology, trying to touch (Fig. 10) or to skirt virtual objects and being affected by vertigo (Fig. 11).

B. Visitors feedback

The guestbook gathered many appreciative comments. Beyond the satisfaction of such messages, we noticed that several feedbacks were thankful to the scientific content, which was the most challenging goal of the event.

Visitors also appreciated the opportunity to access a highend technological environment dedicated to research. One of the recurrent questions was of course the date of the next public event in Immersia.

C. Organisators feedback

The event required an important preliminary organization, involving students, researchers and engineers from both computer science and archaeology laboratories, and communication departments from the tutorship institutes. The two days of the event were intense for the supervising volunteers. The global feeling was however a satisfaction of sharing their work and getting such a good feedback from visitors.

VII. DISCUSSION AND CONCLUSIONS

A. A cognitive experience

As pointed in [START_REF] Baranyi | Definition and Synergies of Cognitive Infocommunications[END_REF], virtual reality is closely related to cognitive infocommunications with the establishment of a communication between a real human and a virtual universe. In particular, immersive virtual reality furthers a strong communication level between the two entities by setting a "first-person" relation and enabling "cognitive presence" [START_REF] Winn | A conceptual basis for educational applications of virtual reality[END_REF].

Virtual reality thus constitutes a useful tool for education, whose relevancy is discussed in [START_REF] Pantelidis | Reasons to Use Virtual Reality in Education and Training Courses and a Model to Determine When to Use Virtual Reality[END_REF]. In our case, the presentation of a disappeared cultural heritage building, immersive virtual reality was completely appropriate. However, the immersive experience is not sufficient to allow the user to fully understand, on one hand, the archaeological discoveries in the site, such as the role of the different buildings, or the architectural hypotheses, and on the other hand, the underlying virtual reality concepts implemented, such as navigation, or head-tracking. In [START_REF] Abu Bakar | User Requirements for Virtual Reality in Architectural Heritage Learning[END_REF], five common attributes are identified for using virtual reality in architectural heritage learning : (i) content, related to the knowledge to be acquired by the user, (ii) experience, related to presence feeling, (iii) setting, concerning the immersive facility, (iv) support, relying on the accessibility of the knowledge "for all", and (v) interface which must be intuitive between the user and the virtual universe. We chose to propose a natural and intuitive immersion in the virtual site, accessible to everyone, with a preliminary presentation of the scientific content in a short conference, and a posterior oral exchange with scientists accompanied with a visual exposition. This combination of different modes (intra-and inter-cognitive) and levels (first-and third-person) of communication with the visitors allowed to acquire a progressive and deep understanding of the underlying scientific work.

B. The Immersia event conlusion

This Immersia event fulfilled its three goals. The scientific message was well understood by the visitors, the results of the underlying interdisciplinary work were greeted, and the immersive experience highly appreciated.

If the visit was restricted to the only immersive part, visitors would have been unable to apprehend the underlying scientific work, however strong were the physical feelings in the virtual world. The preliminary conference was a really valuable complement to convey accessible but essential knowledge and understanding of this experience. The exhibition part completed the event with additional information and free exchanges with researchers from both archaeology and computer science domains.

The main drawback was the restriction on the number of persons that could experience the immersive visit due to the single user at a time constraint in Immersia. The reservation list was full after only one day, while this phase was scheduled over a full week. This drawback was offset by the media repercussion of the event including two television channels, two radios, and three newspapers.

Another issue is the cost of the event due to the operating cost of the platform. For this first edition, the event was completely free of charge for the visitors. A new edition of this event may require a financial participation from the visitors.

Globally, the Immersia event was a rich experience both for the public and the organizers. In addition, this event shows how an active and fruitful collaboration between two scientific communities was able to spread joint research results to a large audience.

1 . 2 .

 12 Communicate around our inter-disciplinary collaboration, Diffuse the archaeological discoveries related to this Gallo-Roman sites, the virtual reality principles, and the interest of this technology for archaeologists work, 1 www.barco.com 2 http://www.ar-tracking.com 3. Provide people with an immersive experience of the past in our virtual reconstitution.

Fig. 1 .

 1 Fig. 1.Building foundations in the excavation.

Fig. 2 .

 2 Fig.2.The 3D reconstitution of the Bourg Saint-Père villa.

Fig. 3 .

 3 Fig. 3.The VR reconstitution of the Bourg Saint-Père villa.

Fig. 4 .

 4 Fig. 4. "Espace des Technologie Innovantes" in Rennes, France.

Fig. 5 .

 5 Fig. 5.Course of the Immersia event.

Fig. 6 .

 6 Fig. 6.Conference area.

Fig. 7 .

 7 Fig. 7.Immersive area.

Fig. 8 .

 8 Fig. 8. Exhibition area.

Fig. 9 .

 9 Fig. 9.The virtual cairn.

Fig. 10 .

 10 Fig. 10. Touching the past.

Fig. 11 .

 11 Fig. 11. Vertigo sensation.

ACKNOWLEDGMENTS

The authors wish to thank Florent Violleau for his work on the OpenMASK implementation, Marc Christie for his kind support. They also want to thank all the volunteers of the event.

Finally, the authors thank Inrap, Université Rennes 1, UEB, Images et Réseaux cluster for their logistic or financial support. REFERENCES.