Transformation Risk and its Determinants: A New Approach based on the Basel III Liquidity Management Framework

Transcription

1 Transformation Risk and its Determinants: A New Approach based on the Basel III Liquidity Management Framework Alain Angora, Caroline Roulet Université de Limoges, LAPE, 5 rue Félix Eboué, Limoges Cedex, France This version: April 2011 Preliminary draft - Please do not quote without the permission of the authors Abstract Liquidity creation is one of the pre-eminent functions of banks but it is also a major source of their vulnerability to shocks. Considering US and European publicly traded commercial banks from 2000 to 2008, we consider the new measures of liquidity defined in the Basel III accords to estimate a level of liquidity creation beyond which a bank may not able to meet its liquidity requirements. Besides, as financial innovation provides new ways for banks to manage their liquidity, we investigate how transformation risk is impacted by the concentrations on loans that are potentially securitisable and on short term, potentially unstable market funding. On the whole, we show that transformation risk decreases with a higher concentration on loans that are potentially securitisable. However, transformation risk increases when banks are more concentrated on short term market debts. Thus by better understanding what factors significantly impact transformation risk, it can help banks to improve their risk management framework. Keywords: Liquidity Creation, Transformation Risk, Bank Regulation JEL classification: C23, G21, G28, G32 Corresponding author. Tel: , c.roulet@jplc.fr (C. Roulet). 1

2 1. Introduction According to the theory of financial intermediation, an important role of banks in the economy is to provide liquidity by funding long term, illiquid assets with short term, liquid liabilities. Through this function of liquidity providers, banks create liquidity as they hold illiquid assets and provide cash and demand deposits to the rest of the economy. Diamond and Dybvig (1983) emphasize the preference for liquidity under uncertainty of economic agents to justify the existence of banks: banks exist because they provide better liquidity insurance than financial markets. However, as banks are liquidity insurers, they face transformation risk and are exposed to the risk of run on deposits. More generally, the higher is liquidity creation, the higher is the risk for banks to face losses from having to dispose of illiquid assets to meet the liquidity demands of customers. There is a large body of theoretical literature dealing with bank liquidity creation (Bryant, 1980; Diamond and Dybvig, 1983; Holmstrom and Tirole, 1998, Kashyap et al., 2002). Nevertheless, empirical studies are more recent and deal with the measurement methodologies and the determinants of liquidity creation. Deep and Schaefer (2004) define the liquidity transformation gap (also called, LT gap ) as the difference of liquid liabilities and liquid assets held by a bank, scaled by total assets. If the difference is positive, the bank invests liquid liabilities into illiquid assets and performs a significant amount of liquidity creation. Deep and Schaefer (2004) consider only the maturity to define the liquidity of bank assets and liabilities. They consider as liquid all assets and liabilities that mature within one year. Berger and Bouwman (2009) define the liquidity of bank assets and liabilities not only based on their maturity but also by considering their category. In addition, their indicator includes on and off-balance sheet items. Then, by considering the liquidity transformation gap or the liquidity creation, several studies focus on the determinants of liquidity creation (Deep and Schaefer, 2004; Rauch et al., 2008; Berger and Bouwman, 2009; Choi et al., 2009; Pana et al., 2009; Chen et al., 2010). They consider several determinants such as bank capital, profitability, credit risk, market power, the business cycle and the level of central bank policy rate. All of these studies portray liquidity creation as an essential role of banks but they do not deal with the liquidity pressures that banks may face and the possible excessive liquidity creation. Indeed, the more banks create liquidity, the higher is their illiquidity and their risk to face losses from having to sell some assets at fire sale prices to repay some debts claimed on demand. However, liquidity creation is not likely to be damaging for a bank as long as it holds adequate levels of stable funding to fund the amount of assets that cannot be monetised or that 2

3 cannot be pledged as collateral. In this context, the bank creates liquidity but it is able to repay the liabilities claimed on demand by selling its liquid assets or using them as collateral. Throughout the global financial crisis which began in mid-2007, many banks struggled to maintain adequate liquidity. Unprecedented levels of liquidity support were required from central banks in order to sustain the financial system and even with such extensive support a number of banks failed, were forced into mergers or required resolution. Thus, banks have experienced difficulties for managing their liquidity and face transformation risk, but the problem is not solved yet. Following the Subprime crisis and in recognition of the need for banks to improve their liquidity management, the Basel Committee on Banking Regulation and Supervision has developed an international framework for liquidity assessment in banking (BIS, 2009). Among the several guidelines, the Basel III accords include the implementation of liquidity ratios concomitantly to capital standards in order to strengthen the stability of banks 1. Although banks face liquidity pressures and experience liquidity problems, financial innovation enables them to manage their liquidity by mitigating the liquidity pressures through new asset - liability management (ALM) framework. Following financial globalisation and deregulation, banks have largely enhanced their market activities through financial innovation (Shleifer and Vishny, 2009). On the liability side, banks modify their funding structure and increase the share of market funding. On the asset side, they securitise their loans. Such financial innovations enable banks to access to new sources of liquidity by reducing their reliance on deposits (Mishkin, 2004) and by converting their illiquid loans into cash (Loutskina, 2011). Based on these facts, we suggest in this paper to extend the current literature on bank liquidity creation in two directions. The first objective of this paper is to assess the level of liquidity creation beyond which a bank may not able to meet its liquidity requirements without borrowing money or fire 1 The Basel Committee on Banking Regulation and Supervision has developed two regulatory standards for liquidity (2009). The net stable funding ratio measures the amount of longer term, stable sources of funding used by an institution relative to the liquidity profile of the assets funded and the potential for contingent calls on funding liquidity arising from off balance sheet commitments and obligations. The standard requires a minimum amount of funding that is expected to be stable over a one year time horizon based on liquidity risk factors assigned to assets and off balance sheet liquidity exposures. This metric is intended to promote longer term structural funding of banks balance sheet, off-balance sheet exposures and capital markets activities. The Basel Committee also suggests the liquidity coverage ratio. This metric identifies the amount of unencumbered, high quality liquid assets an institution holds that can be used to offset the net cash outflows it would encounter under an acute short term stress scenario (i.e., over a 30 days time horizon) specified by supervisors. These proposals have been fully calibrated and agreed upon 12, September 2010 (Basel III accords). 3

4 selling its assets. In other words, we assess the level of liquidity creation a bank can perform by being continuously able to face transformation risk. Although through liquidity creation banks face transformation risk, the liquidity creation indicator suggested by Berger and Bouwman (2009) does not indicate to what extent liquidity creation may become damaging for a bank in terms of excessive liquidity creation and exposure to transformation risk (i.e., how much is too much? ). Based on the Basel III accords, we consider the net stable funding difference. It is computed as the difference of the required amount of stable funding and the available amount of stable funding, scaled by total assets. It measures the amount of assets that could be not monetised through the sale or the use as collateral in a secured borrowing compared with the amount of longer-term, stable sources of funding used by an institution. This indicator estimates the liquidity profile at-risk of banks from their liquidity creation activities. It includes the liquidity unbalances of both sides of bank balance sheet. Besides, it accounts for the impact of the liquidity of the financial markets, on the valuation of assets and on the availability of funding, to assess bank exposure to transformation risk. If the difference is negative or null, the required amount of stable funding is lower or equals the available amount of stable funding. It means that the bank is not exposed to the risk of having to sell some assets at fire sale prices to repay the liabilities claimed on demand. In the contrary to the liquidity creation of Berger and Bouwman (2009), the net stable funding difference explicitly shows a threshold beyond which a bank is likely to experience difficulties due to its inability to face transformation risk. As we assume that bank illiquidity and transformation risk increase with liquidity creation, and that the net stable funding difference is a measure of the liquidity profile at-risk of banks, we show the similarity of these two indicators by doing a statistical analysis. Then, we outline the advantages of the net stable funding difference compared with the liquidity creation in order to estimate a level of liquidity creation for which a bank is continuously able to meet its liquidity requirements with its own liquid assets (i.e., when the net stable funding difference is null). We call this level of liquidity creation the transformation risk neutral level of liquidity creation. This issue seems relevant in order to assess banks ability to face transformation risk when they create liquidity. Until this level of liquidity creation, the bank has not to face losses as its holds enough assets that can be readily monetised or that are pledgeable as collateral to meet the liquidity demands of customers. In a regulatory perspective, the transformation risk neutral level of liquidity creation may be useful, to evaluate from what level, liquidity creation may become excessive and damaging for the stability of banks. 4

5 Although through their essential role of liquidity creation, banks face transformation risk and may become fragile, financial innovation provides new ways for banks to manage their liquidity and mitigate liquidity pressures. The second objective is to study how transformation risk is impacted by the concentrations on loans that are potentially securitisable and on short term, potentially unstable market funding. First, we question whether the concentration on the loans that are potentially securitisable decreases transformation risk. Indeed, one of the key issues in bank liquidity analysis is the liquidity of assets. Cash, near cash items and trading assets are not problematic for bank liquidity. These assets are liquid or can be easily monetised 2. However, among the other assets, some assets are totally illiquid and may lead to acute liquidity problems. Nevertheless, other assets even if they are not directly saleable on financial markets may be sold through OTC transactions, such as the loans that are securitised. Thus, we hypothesize that the concentration on the loans that are potentially securitisable rather than on totally illiquid assets is likely to mitigate liquidity pressures on banks and may decrease transformation risk. Second, we test the impact on transformation risk of the concentration on short term, potentially unstable market funding. Indeed, the stability of funding is another important issue for liquidity analysis in banking. Short term debts are less stable than long term ones 3. Besides, according to the BIS (2009), short term deposits may be considered as more stable than short term market debts. Thus, the more banks are funded by short term market debts, the greater is the potential instability of their funding. Consequently, we hypothesize that the concentration on short term, potentially unstable market funding rather than on short term, stable deposits is likely to increase liquidity pressures on banks and transformation risk. However, banks may consider possible liquidity shortages on funding markets (i.e., some market debts may be rolled-off at short notice) to limit their liquidity creation. Thus, we conjecture that the concentration on short term, potentially unstable market funding is likely to discourage banks for increasing their liquidity creation that leads to lower exposure to transformation risk. The impact on transformation risk of the concentration on short term, potentially unstable market funding is ambiguous. The purpose is to point out the main factors that significantly impact transformation risk in order to help banks to improve their risk management strategies. 2 As they are continuously traded on financial markets, it is possible to find a counterparty and sell these assets with no or relatively low discount. 3 Long term debts are repayable by contract at their residual maturity which must exceed one year. Short term debts are due within one year or may be claimed at short notice. 5

6 Our results confirm the similarity of the liquidity creation indicator of Berger and Bouwman (2009) and the net stable funding difference by considering US and European publicly traded commercial banks over the period. In addition, the net stable funding difference enables us to assess a level of liquidity creation beyond which a bank may not able to meet its liquidity requirements with its liquid assets. Moreover, our results show that transformation risk decreases under high levels of concentration on loans that are potentially securitisable. However, transformation risk increases when banks are more concentrated on short term market debts. The remainder of this paper is organised as follows. In section 2, we present the data. In section 3, we describe our indicator of liquidity creation, the net stable funding difference and we do a statistical analysis to assess the transformation risk neutral level of liquidity creation. In section 4, we detail the determinants of transformation risk and we discuss the regression framework. In section 5 and 6, we comment our regression results and perform some robustness checks. Section 7 concludes. 2. Presentation of the sample Our sample consists of US and European 4 publicly traded commercial banks from 2000 to We focus on US and European banks because the required data are available on standard databases to ensure an accurate representativeness of our sample of banks in each country. Furthermore, we focus on listed banks because their balance sheet data are more detailed which allows us to compute our indicators of liquidity that are our main variables of interest. Annual financial statements are extracted from Bloomberg. From 2000 to 2008, we identify 870 listed commercial banks (645 in the US and 225 in Europe). However, the breakdown for loans by category and the breakdown for deposits by maturity, which are necessary to compute our proxies of liquidity, are not detailed in Bloomberg or in annual reports for 71 US banks and 18 European banks. Thus, the final sample consists of 781 commercial banks (574 in the US and 207 in Europe). In table 1, we present the distribution of banks by country. To deal with the issue of sample representativeness, we verify that on 4 We use data for European banks from the 20 following countries: Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Liechtenstein, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom. 6

7 average from 2000 to 2008, the final sample constitutes over 66.4% of the banking assets of US commercial banks and over 60.4% of the banking assets of European commercial banks. [Insert Table 1] Table 2 presents some general descriptive statistics of our final sample. By considering several key accounting ratios, the data show that banks are on average focused on traditional intermediation activities. Indeed, loans and deposits account for a large share of total assets. The average share of total loans in total assets is 66.4% and the average share of total deposits in total assets is 70.2%. In addition, on average, interest income accounts for nearly three quarters of total income (72.3%). However, there is a high heterogeneity across banks as shown by the high standard deviation and the extreme values of each ratio 5. Regarding the quality of bank assets, the average share of total provisions for loan losses in total loans is 0.5%. In terms of profitability, the average return on assets is 0.9%. Lastly, considering capitalisation, the average total risk weighted capital ratio is higher than the minimum regulatory requirement at 13.2% and the average ratio of Tier 1 capital to total assets is 8.2%. [Insert Table 2] 3. Measuring bank liquidity and the transformation risk neutral level of liquidity creation 3.1. Indicator of liquidity creation Our indicator of liquidity creation is based on the liquidity creation measure in the steps of Berger and Bouwman (2009). In the first step, all bank assets and liabilities are 5 We notice that the average share of total loans to total assets is significantly higher for US banks than for European banks (respectively, 67% and 66%). In addition, the average share of total deposits to total assets is significantly higher for US banks than for European banks (respectively, 77% and 51%). Besides, the average share of interest income is significantly higher for US banks than for European banks (respectively, 77% and 59%). These specificities may be explained by the differences in regulation in the US from Europe. Indeed, in the US, banking groups are submitted to requirements in terms of segmentation of their activities into several subsidiaries. In addition, US banking groups are allowed to carry out activities closely related to banking, such as investment banking and insurance, only if they are considered as well capitalised by the Federal Reserve (i.e., if they meet the Fed s highest risk-based capital rating). It is the reason why most banking groups are focused on banking business, primarily issuing deposits and making loans. However, in Europe, banking groups are not submitted to such a regulation and can more easily develop their market activities. 7

8 classified as liquid, semi liquid or illiquid according to their maturity and their category. Indeed, some assets are considered as easier to sell than others (such as the loans are securitisable and the securities that are saleable on financial markets). Besides, some fundings are considered as more volatile than others (such as commercial paper and short term deposits). In the second step, both balance sheet sides are weighted in reference to the liquidity creation theory suggested by Berger and Bouwman (2009). Table 3 shows the weighting of bank balance sheet 6 based on Berger and Bouwman (2009). [Insert Table 3] Liquidity creation (LC) is then calculated as follows (where all components are scaled by total assets): LC = 0.5 * illiquid assets + 0 * semi liquid assets * illiquid assets * liquid liabilities + 0 * semi liquid assets * illiquid liabilities Total assets All else equal, a bank creates one dollar of liquidity by investing one dollar of liquid liabilities (such as transaction deposits) into one dollar of illiquid assets (such as business loans). Similarly, a bank destroys one dollar of liquidity by investing one dollar of illiquid liabilities or equity into one dollar of liquid assets such as treasury securities (i.e., the bank removes one dollar of liquidity from the non bank public by replacing liquid treasuries with illiquid liabilities or bank equity). The higher is the liquidity creation, the higher is bank illiquidity as it invests more liquid liabilities into illiquid assets. In this context, the bank is at risk if some debtholders claim their funds on demand when assets are saleable at fire sale prices The net stable funding difference Although liquidity creation increases bank illiquidity and transformation risk, the liquidity creation indicator suggested by Berger and Bouwman (2009) does not indicate to what extent liquidity creation may become damaging for a bank in terms of excessive liquidity creation and exposure to transformation risk. In this perspective and based on the Basel III guidelines for bank liquidity assessment (BIS, 2009), we consider an alternative 6 In their model, Berger and Bouwman (2009) consider that bank off balance sheet positions can contribute to liquidity creation. However, we cannot obtain precise breakdown for off-balance sheet. Thus, in our study we only consider the liquidity created from on balance sheet positions. 8

9 indicator that shows to what extent a bank is unable to meet its liquidity requirements without borrowing money or fire selling its assets. Thus, we compute the net stable funding difference by calculating the difference of the required amount of stable funding and the available amount of stable funding. Based on the definition of the BIS (2009), the required amount of stable funding corresponds to the amount of a particular asset that could not be monetised through the sale or the use as collateral in a secured borrowing. The available amount of stable funding corresponds to the total amount of an institution s: i) capital; ii) liabilities with effective maturities of one year or greater; and iii) a portion of stable non-maturity deposits and / or term deposits with maturities of less than one year that would be expected to stay within the institution. To calculate the net stable funding difference, a specific required stable funding factor is assigned to each particular type of asset and a specific available stable funding factor is assigned to each particular type of liability. In appendix 1 (see table A.1), we briefly summarize the composition of assets and liabilities categories and related stable funding factor as defined in the Basel III accords. Table 4 shows the breakdown of bank balance sheet as provided by Bloomberg and its weighting in accordance with the proposals made by the BIS (2009) to calculate the net stable funding difference. On the asset side, we consider the type and the maturity of bank assets in line with the definition of the BIS (2009) to put the corresponding weights. On the liability side, we consider the maturity of the several fundings to put the corresponding weights. However, as we have only the breakdown for deposits according to their maturity and not according to the type of depositors, we consider the intermediate weight of 0.7 for stable demand and saving deposits (including all deposits with a maturity of less than one year). [Insert Table 4] The net stable funding difference (NSFD) is then calculated as follows (where all components are scaled by total assets): 0 * (cash + interbank assets + short term marketable assets) * (long term marketable assets + customer acceptances) 0.7 * (demand deposits + saving deposits) * consumer loans + 0 * (short term market debts + other short term liabilities) required amount available amount + 1 * (commercial loans + other loans + other assets + 1 * (long term liabilities + equity) NSFD = of stable funding - of stable funding = + net fixed assets) - total assets total assets total assets total assets If the difference is positive, it means that the required amount of stable funding exceeds the available amount of stable funding. Thus, the bank faces transformation risk and may 9

10 experience liquidity problems to repay the funding exigible on demand with the assets that cannot be monetised or that are only saleable at fire sale prices Statistical analysis of the liquidity creation (LC) and the net stable funding difference (NSFD) As we assume that bank illiquidity and transformation risk increase with liquidity creation and that the net stable funding difference is a measure of the liquidity profile atrisk of banks, we do a statistical analysis to appreciate the similarity of our proxy of liquidity creation (LC) and of the net stable funding difference (NSFD). The aim is to emphasize the positive relationship that may exist between these two variables. First, we calculate Pearson s coefficient of correlation. We also present scatters to visualise the linear relationship that may exist between these two indicators. We do this statistical analysis by considering all banks in our sample. In addition, we separate US and European banks in order to examine whether the results are driven by US banks alone as they account for a large share of our sample. In table 5, we present descriptive statistics of our two indicators and Pearson s coefficients of correlation. [Insert Table 5] We notice that the average LC of all banks in our sample is 31.6% of total assets and the average NSFD is -7.9% of total assets (see table 5). Pearson s coefficient of correlation suggests a strong linear and positive relationship between LC and NSFD (i.e., this coefficient being at 0.72 and significant at 1% level). Figure 1 illustrates our findings by showing the linear and positive relationship between LC and NSFD. 10

11 Figure 1: Scatter of LC and NSFD (in percent of total assets), for US and European commercial banks from 2000 to 2008 Considering separately US and European banks, we notice that the average LC and the average NSFD of European banks (at respectively, 32.4% and -0.2% of total assets) are significantly higher than those of US banks (at respectively, 31.3% and -10.8% of total assets, see table 5). However, the difference in average NSFDs is higher (i.e., the mean test statistic being at 28.77) than the difference in average LCs (i.e., the mean test statistic being at 3.08). Besides, Pearson s coefficients of correlation emphasize the strong linear and positive relationship between LC and NSFD whatever the location of banks. Figures 2 and 3 illustrate our findings. Figure 2: Scatter of LC and NFSD (in percent of total assets), for US commercial banks from 2000 to

12 Figure 3: Scatter of LC and NFSD (in percent of total assets), for European commercial banks from 2000 to 2008 To deeper understand the higher difference in average NSFD than in average LC between US and European banks, we do an average comparison of the components of LC and NSFD. The liquidity creation of a bank is positive when its illiquid assets exceed its illiquid liabilities (i.e., some illiquid assets being funded by liquid liabilities). Based on the liquidity creation theory of Berger and Bouwman (2009), we calculate the amounts of illiquid assets (IA_TA) and of illiquid liabilities (IL_TA), scaled by total assets 7. In addition, as the net stable funding difference is the difference of two components (i.e., the required amount of stable funding and the available of stable funding), we calculate them separately (RSF_TA and ASF_TA, each component being scaled by total assets) 8. The average values of these ratios and the mean test statistics for the null hypothesis of identical means between US and Europeans banks are shown in table 6. [Insert Table 6] The average differences between US and European banks are significant whatever the ratio considered (see table 6). However, the higher average difference is for ASF_TA, the mean test statistic being the greatest at Thus, European banks hold on average significantly less 7 IA_TA corresponds to all illiquid assets, i.e., to totally illiquid assets and to the semi liquid assets that are illiquid. IA_TA is the weighted sum of all illiquid assets, scaled by total assets. The weights are defined in reference to the liquidity creation theory of Berger and Bouwman (2009). We assign a weight of 1 to all illiquid assets and a weight of 0.5 to all semi liquid assets. IL_TA corresponds to all illiquid liabilities, i.e., to totally illiquid liabilities and to the semi liquid liabilities that are illiquid. IL_TA is the weighted sum of all illiquid liabilities, scaled by total assets. We assign a weight of 1 to all illiquid liabilities and a weight of 0.5 to all semi liquid liabilities. For further details about the breakdown of assets and liabilities by liquidity categories, see table 3. 8 For further details about the computation of RSF_TA and ASF_TA, see table 4. 12

13 available stable funding than US banks. Consequently, this gap enables us to understand why European banks have on average significantly higher NSFD than US banks. The difference between US and European banks in NSFD arises from differences on the liability side of bank balance sheet. To further investigate the components that drive this difference in ASF_TA and to understand why the difference in IL_TA is not as important as the difference in ASF_TA between US and European banks, we do an average comparison of the liquid versus illiquid liabilities in LC 9 and of the stable versus unstable funding in NSFD 10. All components are scaled by total assets. The average values of these ratios and the mean test statistics for the null hypothesis of identical means between US and Europeans banks are shown in table 7 and 8. [Insert Tables 7 and 8] US banks are largely funded by deposits (77.4% of total assets) that contribute to a large share of their liquid liabilities in LC. Indeed, liquid deposits that account for 60.2% of total assets drive liquid liabilities that account for 69.6% of total assets. However, European banks are less funded by deposits (51.2% of total assets) but they are largely funded by market debts (39.8% of total assets) that contribute to a large share of their liquid liabilities in LC. Indeed, liquid market funding that accounts for 30.7% of total assets drives liquid liabilities that account for 73% of total assets (see table 7). In fact, the difference in average IL_TA is significant between US and European banks but it is not so large (at respectively, 30.4% and 27% of total assets), the liquid liabilities of US banks including mostly deposits instead the liquid liabilities of European banks that include both deposits and market debts. Besides, regarding the NSFD, the difference of this indicator from LC is that the majority of deposits that are qualified as liquid in LC are considered as stable in NSFD. Thus, although US banks are largely funded by deposits, the average share of their unstable deposits (12.9% of total 9 Liquid liabilities correspond to all liquid liabilities and to the semi liquid assets that are liquid. It is the weighted sum of all liquid liabilities scaled by total assets. The weights are defined in reference to the liquidity creation theory of Berger and Bouwman (2009). We assign a weight of 1 to all liquid liabilities and a weight of 0.5 to all semi liquid liabilities. Illiquid liabilities correspond to the semi liquid assets that are illiquid and to all illiquid liabilities. It is the weighted sum of all illiquid liabilities scaled by total assets. We assign a weight of 0.5 to all semi liquid liabilities and a weight of 1 to all illiquid liabilities. For further details about the breakdown of liabilities by liquidity categories, see table Stable liabilities correspond to the amount of liabilities that are likely to stay within the bank following a shock. It is the sum of all liabilities weighted by their corresponding stable funding factor. Unstable liabilities correspond to the amount of liabilities that are likely to be suddenly claimed on demand following a shock. It is the sum of all liabilities weighted by their unstable funding factor. For further details about the breakdown of liabilities according to the importance of their stability, see table 4. 13

14 assets) is weakly higher than this of European banks (10% of total assets). However, a large share of market debts contributes to increase unstable funding for European banks. Unstable market funding that accounts for 21.6% of total assets drives unstable funding that accounts for 31.6% of total assets. However, for US banks, unstable market funding accounts for only 6.2% of total assets, total unstable funding accounting for 19.1% of total assets (see table 8). Consequently, European banks hold higher average share of unstable funding driven by market debts compared with US banks. In fact, European banks hold weakly higher share of liquid liabilities in LC than US banks. However, they hold much more unstable funding in NSFD than US banks. Thus, US banks benefit from the stability of their large deposit base and face a highly negative average NSFD. European banks are more funded by volatile market funding and face a weakly negative average NSFD. Besides, depending on the size of the bank, the ability to access external funding may differ as large banks have a larger access to financial markets compared with small banks. Thus, our findings concerning the impact of market funding on the liquidity profile of banks are likely to differ according to the size of banks. Based on Berger and Bouwman (2009) and on IBCA criterion, a bank is considered as large if total assets are greater than one billion USD. We do the statistical analysis only for US banks (our sample including 233 large banks and 341 small banks in the US) as our sample of European banks mainly includes large banks (170 large banks in 207 European banks). In table 9, we present descriptive statistics of LC and NSFD and Pearson s coefficients of correlation for separately large and small US banks. [Insert Table 9] We notice that the average LC and the average NSFD of large banks (at respectively, 32.1% and -9% of total assets) are significantly higher than those of small banks (at respectively, 30.8% and -12.1% of total assets, see table 9). However, the difference in average NSFDs is higher (i.e., the mean test statistic being at -9.21) than the difference in average LCs (i.e., the mean test statistic being at -3.26). Besides, Pearson s coefficients of correlation outline the strong linear and positive relationship between LC and NSFD whatever the size of banks. Figures 4 and 5 illustrate our findings. 14

15 Figure 4: Scatter of LC and NFSD (in percent of total assets), for large US commercial banks from 2000 to 2008 Figure 5: Scatter of LC and NFSD (in percent of total assets), for small US commercial banks from 2000 to 2008 Like above, to deeper understand the higher difference in average NSFD than in average LC between large and small banks, we do an average comparison of the components of LC and NSFD. Statistics and mean tests according to the size of banks are shown in table 10. [Insert Table 10] The average differences between large and small banks are significant whatever the ratio considered (see table 10). However, the higher average difference is for ASF_TA, the mean test statistic being the greatest at Thus, small banks hold on average significantly more available stable funding than large banks. Consequently, this gap enables us to understand why large banks have on average significantly higher NSFD than small banks. To further investigate the components that drive this difference in ASF_TA and to understand why the 15

16 difference in IL_TA is not as important as the difference in ASF_TA between large and small banks, we do an average comparison of the liquid versus illiquid liabilities in LC and of the stable versus unstable funding in NSFD. Statistics and mean tests according to the size of banks are shown in table 11 and 12. [Insert Tables 11 and 12] We can do similar comments for large banks (respectively, small banks) as those ever done for Europeans banks (respectively, US banks). Finally, large banks hold weakly higher share of liquid liabilities in LC than small banks (see table 11). However, they hold much more unstable funding in NSFD than small banks (see table 12). Thus, small banks benefit from the stability of their large deposit base and face a highly negative average NSFD. Large banks are more funded by volatile market funding and face a weakly negative average NSFD The transformation risk neutral level of liquidity creation After emphasizing the strong linear and positive relationship between LC and NSFD whatever the location and the size of banks, we consider the following relationship (equation (1), subscripts i and t denoting bank and period respectively): LCi,t = α + β * NSFDi,t + ε i, t. After testing for cross section and time fixed versus random effects, we introduce cross section fixed effects in our regressions. We run regressions for all banks in our sample, for US and European banks separately and for large versus small US banks. From this equation, we can calculate a level of liquidity creation for a given level of net stable funding difference. Thus, we can calculate the level of liquidity creation for which a bank is continuously able to meet its liquidity requirements with its own liquid assets, i.e. when the net stable funding difference is null. This level of liquidity creation is the transformation risk neutral level of liquidity creation (TRNLC). It corresponds to average cross section fixed effects 11. Consequently, a given level of liquidity creation could be reached but the bank is able to meet its liquidity requirements without borrowing money or fire selling its assets (i.e., the value of assets that cannot be monetised equals the amount of available stable funding). Regression results and estimations of TRNLC are shown in table Average cross section fixed effects are calculated as follows: N i = 1 ( α + α ) / N. i 16

17 [Insert Table 13] Our results show that the TRNLC of all banks in our sample is 37.5% of total assets. More precisely, TRNLC of European banks is 32.6% and 40.3% for US banks. In addition among US banks, TRNLC of small banks is 40.9% and 39.2% for large banks. Consequently, the level of LC banks can perform by being continuously able to face transformation risk is lower for European banks (respectively, large US banks) than for US banks (respectively, small US banks). These findings can be explained in light of the conclusions of our statistical analysis. For weakly different levels of LC, NSFD of European banks (respectively, large US banks) is much higher than of US banks (respectively, small US banks). In other words, for weakly different levels of LC, European banks (respectively, large US banks) face much higher levels of transformation risk driven by the importance of their unstable market debts compared with US banks (respectively, small US banks) that benefit from the stability of their large deposit base. Consequently, European banks (respectively, large US banks) can create less liquidity than US banks (respectively, small US banks) to be continuously able to face transformation risk. This result confirms the necessity as ever pointed out by the Basel Committee (2009), especially for European banks, to strengthen the stability of their funding to mitigate their transformation risk. 4. The determinants of transformation risk and regression framework According to our empirical issue, we consider indicators of concentration on loans that are potentially securitisable and on short term, potentially unstable market funding in the determination of transformation risk. In addition, based on previous studies (Deep and Schaefer, 2004; Rauch et al., 2008; Berger and Bouwman, 2009; Pana et al., 2009; Choi et al., 2009; Chen et al., 2010; Fungacova et al., 2010), we consider a set of other explanatory variables. Finally, we discuss the regression framework Indicators of concentration on loans that are potentially securitisable and on short term, potentially unstable market funding Liquidity creation is an essential role of banks. However, through this function, banks face transformation risk and may become fragile. Nevertheless, financial innovation provides new asset - liability management (ALM) framework for banks to manage their liquidity and 17

18 mitigate liquidity pressures. Following financial globalisation and deregulation, banks have largely enhanced their market activities by increasing their market funding and by securitizing their loans (Shleifer and Vishny, 2009). The use of market funding reduces bank reliance on deposits (Mishkin, 2004). In addition, the securitisation of loans is a source of cash as it allows banks to convert some of their loans into liquid funds (Loutskina, 2011). In this perspective, we study how transformation risk is impacted by the concentrations on loans that are potentially securitisable and on short term, potentially unstable market funding. By holding totally illiquid assets, banks may experience acute liquidity problems. Nevertheless, although some assets are not totally liquid as they are not directly saleable on financial markets (i.e., in opposition to cash, near cash items and trading securities), they can be sold through OTC transactions such as the loans that are securitised. Based on this fact, we question whether the concentration on loans that are potentially securitisable rather than on totally illiquid assets is likely to mitigate liquidity pressures on banks and may decrease transformation risk. As a proxy of the loans that are potentially securitisable, we consider the consumer loans (such as loans to consumers, credit card loans, residential mortgage loans and instalment loans). Indeed, consumer loans are securitisable through the issuance of residential mortgage backed securities (RMBS). Commercial loans and other loans (such as loans to commercial and industrial entities, commercial real estate loans, construction loans, loans to agriculture and loans to money market funds) are not securitisable or only securitisable through the issuance of commercial mortgage backed securities (CMBS). However, central banks and prime brokers charge a higher discount on CMBS than on RMBS (IMF, 2008). Consequently, the securitisation of consumer loans provides larger amounts of cash than the securitisation of commercial loans and other loans. Thus, consumer loans are more liquid than commercial ones. Therefore all else equal, a bank increases its exposure to transformation risk by investing one dollar of liquid liabilities into one dollar of commercial loans rather than into one dollar of consumer loans. Thus, we can expect a negative relationship between the concentration on consumer loans that are potentially securitisable and transformation risk. Another important issue in bank liquidity analysis is the stability of funding. Short term debts are less stable than long term ones. Besides, short term deposits may be considered as more stable than short term market debts (BIS, 2009). Consequently, the more banks hold short term market debts, the greater is the potential instability of their funding. Thus, we can expect that the concentration on short term, potentially unstable market funding rather than on short term, stable deposits may increase liquidity pressures on banks and transformation risk. However, banks may consider possible liquidity shortages on funding markets (i.e., some 18

19 market debts may be rolled-off at short notice) to limit their liquidity creation. Thus, we can expect that the concentration on short term, potentially unstable market funding is likely to discourage banks for increasing their liquidity creation that leads to lower exposure to transformation risk. The impact on transformation risk of the concentration on short term, potentially unstable market funding is ambiguous. To measure such concentrations, we compute normalised Herfindalh Hirschman indexes (Stiroh, 2002; Acharya et al., 2002). We consider two proxies of the concentration on loans that are potentially securitisable. First, we consider the concentration on loans that are potentially securitisable rather than on loans that cannot be securitised. We test if the potential liquidity of the loan portfolio is likely to mitigate transformation risk. Thus, we compute a normalised Herfindalh Hirschman index to proxy the level of concentration on loans that are potentially securitisable versus on loans that cannot be securitised (HHI_LOAN 12 ). Second, we consider the concentration on loans that are potentially securitisable rather than on loans that cannot be securitised and on other illiquid assets. We test if the potential liquidity of the illiquid assets portfolio is likely to mitigate transformation risk. Consequently, we compute a normalised Herfindalh Hirschman index to proxy the level of concentration on loans that are potentially securitisable versus on totally illiquid assets (i.e., including all loans that cannot be securitised, others assets and fixed assets, HHI_ILASSET). In addition, we calculate a normalised Herfindalh Hirschman index to proxy the level of concentration on short term deposits versus on short term market debts (HHI_STFUND). Normalised Herfindalh Hirschman index varies between 0 and 1. The more the index is closed to 1, the higher is concentration. Besides, the higher is the ratio of loans that are potentially securitisable (respectively, the share of total short term market debts) to total loans or to total loans and other illiquid assets (respectively, to total short term debts), the higher is bank concentration on loans that are potentially securitisable (respectively, on short term market debts). To capture the concentration on loans that are potentially securitisable, we interact HHI_LOAN with the ratio of loans that are potentially securitisable to total loans (SECLO_TLO). 12 We split bank loan portfolio into loans that are potentially securitisable and loan that cannot be securitised. Herfindalh Hirschman index (HHI_L) is then computed as follows: HHI _ L = ( 2 loans that are potentially securitisable / total loans) + ( loans that are not securitisable / total loans) 2 We calculate normalised HHI _ LOAN as follows: 1 HHI _ L HHI _ LOAN = 2 1 ) 1 2 We calculate the other indicators of concentration by considering the same methodology. 19

20 Similarly, we interact HHI_ILASSET with the ratio of loans that are potentially securitisable to total loans and other illiquid assets (SECLO_IA). In addition, to capture the concentration on short term market debts, we interact HHI_STFUND with the ratio of short term market debts to total short term debts (SMDBT_STDBT). As we conjecture a positive relationship between transformation risk and the concentration on loans that cannot be securitised or on illiquid assets, we can expect a positive sign for the coefficients of HHI_LOAN and HHI_ILASSET in the determination of transformation risk. Then, as we conjecture a negative relationship between transformation risk and the concentration on loans that are potentially securitisable, we can expect that the sign of the sum of coefficients of HHI_LOAN (respectively, HHI_ILASSET) and of its interaction with SECLO_TLO (respectively, SECLO_IA) tends to be more and more negative as SECLO_TLO (respectively, SECLO_IA) tends to increase. However, the expected signs for the coefficient of HHI_STFUND and for the sum of coefficients of HHI_STFUND and of its interaction with STMDBT_TDBT are ambiguous Other variables impacting transformation risk Following the existing literature, we consider a large set of microeconomic and macroeconomic indicators that are likely to impact transformation risk. Based on Berger and Bouwman (2009), we consider the influence of bank capital in the determination of transformation risk. The authors point out two hypotheses that largely matter in the current debate of the relationship between bank capital and liquidity creation. The risk absorption hypothesis predicts a positive relationship between bank capital and liquidity creation. Liquidity creation increases the bank s exposure to transformation risk as its losses increase with the level of illiquid assets to meet the liquidity demands of customers (Allen and Gale, 2004), while capital allows the bank to absorb risk (Repullo, 2004). Thus, higher capital ratio may allow banks to increase their liquidity creation and their exposure to transformation risk. By contrast, the financial fragility hypothesis (Diamond and Rajan, 2000, 2001) and the deposit crowding-out hypothesis (Gorton and Winton, 2000) predict a negative relationship between capital and liquidity creation. In their model, Diamond and Rajan (2000, 2001) suggest that bank capital may impede liquidity creation by making the bank s capital structure less fragile. They model a relationship bank that raises funds from depositors and lends then to borrowers. By monitoring borrowers, the bank obtains private information that gives it an advantage in assessing the profitability of its borrowers. However, 20

21 this informational advantage may create an agency problem. Indeed, as the bank maximises its profitability, it may extort rents from its depositors by demanding a greater share of the loan income. As depositors know that the bank may abuse their trust, the bank has to win their confidence by adopting a fragile financial structure with a large share of liquid deposits. Nevertheless, a contract with depositors mitigates the bank s hold-up problem because depositors can run on the bank if they have doubts about bank efforts for monitoring borrowers and the fair reallocation of loan income. Consequently, financial fragility favours liquidity creation since it allows the bank to collect more deposits and grant more loans. By contrast, higher capital tends to mitigate the financial fragility and enhances the bargaining power of the bank that leads to hamper the credibility of its commitment to depositors. Consequently, higher capital tends to decrease liquidity creation and exposure to transformation risk. Besides, Gorton and Winton (2000) show that a higher capital ratio may reduce liquidity creation through the crowding-out of deposits. They argue that deposits are more effective liquidity hedges for investors than investments in bank equity capital. Indeed, deposits are totally or partially insured and withdrawable at par value. However, bank capital is not exigible and with a stochastic value that depends on the state of bank fundamentals and on the liquidity of the stock exchange. Thus, higher capital ratios shift investors funds from relatively liquid bank deposits to relatively illiquid bank capital. Consequently, the higher is bank capital ratio, the lower is liquidity creation and bank exposure to transformation risk. In our study, we consider the ratio of Tier 1 and 2 capital to total assets (T12_TA). We consider a broad definition of capital in line with some of the theoretical studies. For example, Diamond and Rajan (2001) indicate that capital in their analysis may be interpreted as equity and long term debts, the sources of funds that cannot run on the bank. Under the financial fragility hypothesis and the deposit crowding-out hypothesis, we can expect a negative sign for the coefficient of bank capital ratio in the determination of transformation risk. However under the risk absorption hypothesis, we can expect a positive sign. The expected sign for the coefficient of this variable is ambiguous. We consider bank profitability to account for the impact of better financial soundness on bank risk bearing capacity and on their ability to create liquidity (Rauch et al., 2008; Chen et al., 2010). By assuming that better financial soundness can enhance bank ability to take risk, we can expect a positive relationship between bank profitability and transformation risk. Nonetheless, it can also account for the too big to fail status of large banks and the problem of gamble for resurrection. A bank can create liquidity and take more risk even if it is currently in trouble in order to boost its profitability as it knows that it will be rescued in case 21