Transformation risk and its determinants

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, 87031 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Preliminary draft - Please do not quote without the permission of the authors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: +33-555-32-81-88, [email protected] (C. Roulet).
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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
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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
banks1. 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).
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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.
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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 monetised2. 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 ones3. 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
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 2000-2008 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 European4 publicly traded commercial banks from
2000 to 2008. 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.
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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.
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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 sheet6 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 - 0.5 * illiquid assets + 0.5 * liquid liabilities + 0 * semi liquid assets - 0.5 * 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.
3.2. 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.
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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)
+ 0.5 * (long term marketable assets + customer acceptances)
+ 0.85 * consumer loans
+ 1 * (commercial loans + other loans + other assets
required amount available amount
+ net fixed assets)
NSFD = of stable funding - of stable funding =
total assets
total assets
total assets
0.7 * (demand deposits + saving deposits)
+ 0 * (short term market debts + other short term liabilities)
+ 1 * (long term liabilities + equity)
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
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.
3.3. 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.
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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 2008
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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 assets7. 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 -52.25. 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.
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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 LC9 and of the stable versus unstable funding in NSFD10. 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 3.
10
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.
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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.
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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 24.55. 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
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.
3.4. 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): LC i , t = α + β * NSFD i ,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 effects11.
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 13.
N
11
Average cross section fixed effects are calculated as follows:
∑
i= 1
(α + α i ) / N .
16
[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.
4.1. 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
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
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_LOAN12). 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 = (loans that are potentially securitisable / total loans) 2 + (loans that are not securitisable / total loans) 2
We calculate normalised HHI _ LOAN as follows:
1
HHI _ L −
2
HHI _ LOAN =
)
1
1−
2
We calculate the other indicators of concentration by considering the same methodology.
19
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.
4.2. 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
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
of failure; or in order to obtain higher expected profits and improve its financial statements.
Thus, we can expect a negative relationship between bank profitability and transformation
risk. As a proxy of bank profitability, we consider the return on assets that corresponds to the
ratio of net income to total assets (ROA). The expected sign for the coefficient of this variable
is ambiguous.
We consider the impact of credit risk in the determination of transformation risk (Deep
and Schaefer, 2004; Rauch et al., 2008; Berger and Bouwman, 2009; Fungacova et al., 2010).
The lower is credit risk, the more a bank can enhance its credit activities by continuously
meeting the capital at-risk standards. Consequently, better quality of loans may improve the
ability of banks to create liquidity that leads to increase their exposure to transformation risk.
Consequently, we can expect a negative relationship between credit risk and transformation
risk. As a proxy of the quality of bank loans, we consider the ratio of total provisions for loan
losses to total loans (PLL_TLO). We can expect a negative sign for the coefficient of this
variable in the determination of transformation risk.
Berger and Bouwman (2009) shed light on the impact of bank market power in the
determination of bank liquidity creation and transformation risk as it may impact the
availability of funding (Petersen and Rajan, 1995) and the split of loan portfolio (Berger et al.
2005). Greater market power may enable banks to enhance their liquidity creation by making
more loans and by attracting more funds (i.e., deposits or market funding). Thus, we can
expect that the higher is bank market power, the higher may be liquidity creation and
exposure to transformation risk. As a proxy of bank market power, we consider the ratio of
total assets of bank i located in country j to total assets of the banking system in country j
(MKT_POW). Thus, we can expect a positive sign for the coefficient of this variable in the
determination of transformation risk.
We consider bank size to control for possible data distortions due to size heterogeneity
since small banks are likely to be more focused on traditional intermediation activities (Rauch
et al., 2008; Choi et al., 2009; Berger and Bouwman, 2009; Fungacova et al., 2010). Thus, we
can expect a negative relationship between bank size and transformation risk. However, bank
size accounts for possible “too big to fail” status of large banks that could lead to moral
hazard behaviour and excessive risk exposure. We can expect a positive relationship between
bank size and transformation risk. As a proxy of bank size, we consider the log of total assets
(LN_TA). The expected sign for the coefficient of this variable is ambiguous.
The existing empirical literature about liquidity creation outlines the relevance of
macroeconomic indicators concomitantly to microeconomic indicators (Rauch et al., 2008;
22
Pana et al., 2009; Chen et al., 2010). Indeed, macroeconomic context is likely to impact bank
activities and investment decisions. For example, the demand for differentiated financial
products is higher during economic boom and may improve bank ability to expand its loan
and securities portfolios at a higher rate. Similarly, economic downturns are exacerbated by
the reduction in bank credit supply. Based on these arguments, we can expect banks to
increase their liquidity creation and their exposure to transformation risk during economic
booms. To highlight the impact of macroeconomic context on transformation risk of banks,
we consider the annual growth rate of real GDP (GDP_GWT). Thus, we can expect a positive
sign for the coefficient of this variable in the determination of transformation risk.
We consider the impact of monetary policy on bank liquidity creation and
transformation risk (Rauch et al., 2008). The literature provides two opposite views on the
link between monetary policy and bank liquidity creation (Mishkin, 1996). First, when central
bank policy rate is relatively low, credit supply increases as banks’ liquidity creation. We can
expect a negative relationship between central bank policy rate and transformation risk.
Second, when interest rates are relatively high and because of adverse selection problems,
demands for risky investment projects with higher expected returns may supplant safe
investment projects that generate low profitability. Thus, banks are likely to face higher losses
through the lower quality and the higher illiquidity of their assets. We can expect a positive
relationship between central bank policy rate and transformation risk. In our study, we
consider the policy rate of the central banks that are located in the countries considered (CB).
Under the first view, we can expect a negative sign for the coefficient of this variable in the
determination of transformation risk. Under the second view, we can expect a positive sign.
Thus, the expected sign for the coefficient of this variable is ambiguous.
As liquidity shortages are likely to disturb the management of bank liquidity and may
lead to acute liquidity problems, we consider the impact of liquidity pressures on the
interbank market. As a proxy of liquidity pressures on the interbank market, we consider the
spread of the one month interbank rate and the policy rate of the central bank (IBK1M_CB).
The higher is the spread, the higher is the one month interbank rate compared with the policy
rate of the central bank. Thus, the interbank market is under liquidity pressures. In addition,
this variable is an indicator of banks’ mistrust towards their peers (Estrella and Mishkin,
1998; Estrella, 2005). Banks charge a high risk premium for lending to the other banks at
short notice compared with the policy rate of the lender on last resort. Consequently, the
higher cost of interbank funding may prevent banks to access to these sources of liquidity.
Thus, banks may face losses from having to sell some assets at fire sale prices to repay the
23
liabilities claimed on demand. Consequently, we can expect a positive sign for the coefficient
of this variable in the determination of transformation risk.
Finally, we consider supervisory regime (Laeven and Levine, 2008; Shehzad et al.,
2010) as it is likely to impact bank risk taking behaviour. We compute the index measuring
supervisory control from the World Bank’s 2007 Regulation and Supervisory Database (Barth
et al., 2007)13. As banking regulation is likely to vary across countries, this indicator enables
us to control for possible country effects. We can expect that under strong supervisory
oversight, banks are encouraged to control their risk exposure and manage their
transformation risk. Thus, we can expect a negative sign for the coefficient of this variable in
the determination of transformation risk.
In table 14, we present some descriptive statistics of our explanatory variables for US
and European publicly traded commercial banks over the 2000-2008 period.
[Insert Table 14]
4.3. Econometric model
To study the determinants of transformation risk, we consider as dependant variable
the net stable funding difference (NSFD). It is an indicator of the liquidity profile “at-risk” of
banks from their liquidity creation activities. In addition of including the liquidity unbalances
of both sides of bank balance sheet, it accounts for the impact of the liquidity of the financial
markets, on the valuation of assets and the availability of funding, to assess bank exposure to
transformation risk. A positive NSFD implies that banks are likely to face too many losses
13
To compute our proxy of supervisory regime (CONTROL), we combine two indicators. The first indicator
refers to supervisory agency control and is the total number of affirmative answers to the following questions: (i)
Is the minimum capital adequacy requirement greater than 8%? (ii) Can the supervisory authority ask banks to
increase minimum required capital in the face of higher credit risk? (iii) Can the supervisory authority ask banks
to increase minimum required capital in the face of higher market risk? (iv) Can the supervisory authority ask
banks to increase minimum required capital in the face of higher operational risk? (v) Is an external audit
compulsory obligation for banks? (vi) Can the supervisory authority force a bank to change its internal
organization structure? (vii) Can the supervisory authority legally declare that a bank is insolvent? (viii) Can the
supervisory authority intervene and suspend some or all ownership rights of a problem bank? (ix) Can the
supervisory authority supersede shareholders rights? (x) Can the supervisory authority remove and replace
managers? (xi) Can the supervisory authority remove and replace directors? The second indicator of the
supervisory regime measures deposit insurance agency control and is the total number of affirmative answers to
the following questions: (i) Can the deposit insurance agency legally declare that a bank is insolvent? (ii) Can the
deposit insurance agency intervene and suspend some or all ownership rights of a problem bank? (iii) Can the
deposit insurance agency remove and replace managers? (iv) Can the deposit insurance agency remove and
replace directors? (v) Can the deposit insurance agency supersede shareholders rights?
24
from having to sell some assets at fire sale prices to repay some liabilities that may be claimed
at short notice. These losses may prevent banks to repay this amount of debts as the cash
value of assets may be too weak.
Our empirical methodology is closed to Berger and Bouwman (2009). Based on the
fact that portfolio changes take time to occur and likely reflect decisions made on the basis of
historical experience, we consider the one year lagged value of all explanatory variables
(except for LN_TA and CONTROL following Rauch et al. (2008) and Fungacova et al.
(2010)). Like Berger and Bouwman (2009), we suppose that the future cannot cause the past.
In a risk management perspective, the purpose is to outline how previous factors influence
bank decisions to determine their current profile of liquidity creation and transformation risk.
Besides, as we consider two proxies of concentration on loans that are potentially
securitisable (i.e., HHI_LOAN and HHI_ILASSET) and because they are highly correlated, we
introduce them alternatively in our regressions (equations 2.a and 2.b). Our model is specified
as follows (subscripts i and t denoting bank and period respectively):
NSFD it = α
it
+ β 1 HHI _ LOAN i , t − 1 + β 2 HHI _ LOAN i, t − 1 * SECLO _ TLO i , t − 1
+ β 3 HHI _ STFUND i, t − 1 + β 4 HHI _ STFUND i ,t − 1 * STMDBT _ STDBTi , t − 1
+
11
∑
p= 5
β p TD i , t − 1 +
13
∑
p = 12
(2.a)
β p TD i, t + ε i ,t
NSFD it = α it + β 1 HHI _ ILASSETi , t − 1 + β 2 HHI _ ILASSETi , t − 1 * SECLO _ IA i , t − 1
+ β 3 HHI _ STFUND i , t − 1 + β 4 HHI _ STFUND i , t − 1 * STMDBT _ STDBTi , t − 1
+
11
∑
p= 5
β p TD i , t − 1 +
13
∑
p = 12
(2.b)
β p TD i , t + ε i , t
Where HHI_LOAN and HHI_ILASSET are respectively normalised Herfindalh
Hirschman indexes that proxy the level of concentration on loans that are potentially
securitisable versus on loans that cannot be securitised or alternatively on illiquid assets (i.e.,
including all loans that cannot be securitised and other illiquid assets). HHI_STFUND is a
normalised Herfindalh Hirschman index that proxy the concentration on short term deposits
25
versus on short term market debts. SECLO_TLO corresponds to the ratio of loans that are
potentially securitisable to total loans. SECLO_IA is the ratio of loans that are potentially
securitisable to total loans and other illiquid assets. STMDBT_STDBT corresponds to the ratio
of short term market debts to total short term debts. TD corresponds to the determinants of
transformation risk from previous literature. After testing the presence of cross section and /
or time fixed versus random effects and possible heteroskedasticity of error, we introduce
cross section and time fixed effects in our regressions. To avoid colinearity problems, we
orthogonalise the correlated variables if their introduction disturbs the results of our
regressions14. To deal with heteroskedasticity problem, we use the Huber-White robust
covariance method.
5. Regression results
We test for the contribution of the concentrations on loans that are potentially
securitisable and on short term, potentially unstable market funding to explain transformation
risk beyond the factors documented in the literature. Equations 2.a and 2.b correspond to the
estimation of equation (2) by considering alternatively two proxies of the concentration on
loans that are potentially securitisable. Table 15 presents our regression results.
[Insert Table 15]
All proxies of concentration come out significant in the baseline of our estimations.
The coefficients of HHI_LOAN and HHI_ILASSET are significantly positive. The coefficient
of HHI_STFUND is significantly negative. Consequently, the higher is the concentration on
loans that cannot be securitised, the higher is transformation risk. In addition, the higher is the
concentration on short term, stable deposits, the lower is transformation risk. Besides, the
sums of coefficients of HHI_LOAN (respectively, HHI_ILASSET) and of its interaction with
SECLO_TLO (respectively, SECLO_IA) tends to become more and more negative as
SECLO_TLO (respectively, SECLO_IA) tends to increase. Furthermore, the sum of
coefficients of HHI_STFUND and of its interaction with STMDBT_TDBT tends to become
more and more positive as STMDBT_TDBT tends to increase. Thus, the more banks are
concentrated on loans that are potentially securitisable, the lower is transformation risk.
Besides, the higher is the concentration on short term, potentially unstable market funding, the
14
We orthogonalise LN_TA with MKT_POW.
26
higher is transformation risk. These findings highlight the benefits of the concentration on
loans that are potentially securitisable and on short term deposits to mitigate transformation
risk. These results outline the advantages for banks to develop the securitisation of loans in
order to benefit from the liquidity of securitisation markets to manage their transformation
risk. However, the advantages provided by the securitisation of loans depend on the liquidity
of securitisation markets that is likely to be impugned following a market collapse (i.e., like
during the Subprime crisis). Thus, holding such loans is likely to be inefficient to manage
transformation risk when the liquidity of securitisation markets is tightening. Besides, our
findings emphasize the benefit, as ever pointed out by the Basel Committee (2009), of the
stability of funding to mitigate transformation risk. These results outline the negative impact
of the liquidity shortages on funding markets as the higher instability of short term market
funding is likely to increase transformation risk.
Concerning the other determinants of transformation risk documented in previous
literature, most variables are significant except bank profitability (ROA). The coefficient of
the ratio of Tier 1 and 2 capital to total assets (T12_TA) is significantly negative.
Consequently, it is the “financial fragility hypothesis” and “the debt crowding hypothesis”
that seem to prevail. Thus, higher capital ratio may hamper liquidity creation and mitigate
transformation risk, banks benefiting from the stability of their liabilities. The coefficient of
the ratio of total provisions for loan losses to total loans (PLL_TLO) is significantly negative.
Consequently, under reduced credit risk, the ability of banks to create liquidity may be
improved, their exposure to transformation risk tending to be higher. Moreover, the
coefficients of the log of total assets (LN_TA) and the coefficient of our proxy of bank market
power (MKT_POW) are significantly negative. Thus, small banks tend to face higher
transformation risk. This result may be explained as they are likely to be focused on
traditional intermediation activities because of their restricted access to financial markets
compared with large banks. We can conjecture similar conclusions for banks with weak
market power as they are likely to be the smaller ones. Besides, the coefficient of our index of
supervisory regime (CONTROL) is significantly negative. Thus, the stronger is supervisory
oversight, the more banks are encouraged to control their risk exposure and face lower
exposure to transformation risk. Furthermore, our findings point out the importance to
consider macroeconomic indicators in the analysis of transformation risk. The coefficient of
the annual growth rate of real GDP (GDP_GWT) is significantly positive. Consequently,
during economic booms, banks may expand their loan and securities portfolios and increase
their transformation risk. In addition, the coefficient of the central bank policy rate (CB) is
27
significantly positive. This result implies that when interest rates are relatively high and
because of adverse selection problems, banks are likely to face higher risk exposure through
the lower quality and the higher illiquidity of their assets. Thus, higher interest rates
contribute to increase transformation risk. Finally, the coefficient of our proxy of the liquidity
pressures on the interbank market (IBK1M_CB) is significantly positive. The higher are
liquidity pressures on the interbank market, the higher is the cost of interbank funding. This
may prevent banks to access to these sources of liquidity and increase their transformation
risk. This finding highlights the importance of considering the state of the interbank market
for the analysis of transformation risk. Furthermore, it emphasizes the importance of the
effective transmission of monetary policy to the interbank market.
6. Robustness checks
We examine the robustness of our findings considering the impact of bank location on
the relationship between transformation risk and the concentrations on loans that are
potentially securitisable and on short term, potentially unstable market funding. We estimate
equation (2) separately for US and European banks (see table A2.1) in order to examine
whether the results are driven by US banks alone as they account for a large share of our
sample. The conclusions for all indicators of concentration are consistent with those
previously obtained whatever the location of banks.
We now check the stability of our results considering bank size. Depending on the size
of the bank, the ability to access external funding may differ. Large banks may benefit from
their “too big to fail status” and from their larger access to financial markets. By contrast,
small banks have a more restricted access to financial markets. This is likely to impact the
link between transformation risk and the concentrations on loans that are potentially
securitisable and on short term, potentially unstable market funding. 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. Besides, as our sample of European banks mainly includes large
banks (170 large banks in 207 European banks), we also consider the location of banks.
Consequently, we consider bank size but we also check the stability of our results for banks
located in Europe versus in the US. Thus, we consider a dummy variable that takes the value
of 1 for US banks and 0 otherwise (DUM_LOC). We estimate equation (2) separately for
large and small banks and we introduce all the interactions of each explanatory variable with
this dummy variable and the dummy variable alone (see table A2.2). The conclusions for all
28
indicators of concentration are consistent with those previously obtained whatever the size
and the location of banks, except in one case. For small banks whatever their location, the
concentration on short term, potentially unstable market funding becomes not significant in
the determination of transformation risk. Consequently, transformation risk of small banks is
not impacted by the potential instability of their short term funding. However, small banks are
largely funded by deposits15. Our results emphasize the benefit of the stability of their large
deposit base, and especially from the stability of their short term deposits, to mitigate their
transformation risk.
7. Concluding remarks
Liquidity creation is an essential role of banks but a major source of their vulnerability
to shocks. Thus, it is not liquidity creation that may be damaging for banks but excessive
liquidity creation. In this perspective, we consider a measure of bank liquidity profile “at-risk”
as defined in the Basel III accords (called the “net stable funding difference”) that enables us
to assess a level of liquidity creation beyond which a bank may not be able to meet its
liquidity requirements without borrowing money or fire selling its assets (called the
“transformation risk neutral of liquidity creation”). Besides, although through their function
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. Consequently, we study how transformation risk is decreased by the concentration
on loans that are potentially securitisable and impacted by the concentration on short term,
potentially unstable market funding.
Our findings confirm the similarity of the liquidity creation indicator of Berger and
Bouwman (2009) and the net stable funding difference and exhibit a strong linear and positive
relationship between these two indicators. We find that on average, the transformation risk
neutral of liquidity creation of US and European banks is 37.5% of total assets over the
2000-2008 period. More precisely, the average transformation risk neutral of liquidity
creation of European banks (respectively, large US banks) is lower than this of US banks
(respectively, small US banks). US banks (respectively, small US banks) benefit from the
stability of their large deposit base, European banks (respectively, large US banks) being
more funded by volatile market debts. Consequently, European banks (respectively, large US
15
The average share of total deposits in total assets is 80% for small US banks and of 68.7% for small European
banks.
29
banks) can create less liquidity than US banks (respectively, small US banks) to be
continuously able to face transformation risk. Furthermore, our results show that the more
banks are concentrated on loans that are potentially securitisable and on short term deposits,
the lower is their transformation risk. These results outline the advantages for banks to
develop the securitisation of loans in order to benefit from the liquidity of securitisation
markets to manage their transformation risk. However, the benefits provided by loan
securitisation depend on the liquidity of securitisation markets that is likely to be impugned
following a market collapse. Consequently, holding such loans is likely to be inefficient to
manage transformation risk when securitisation markets become more illiquid. Besides, our
findings emphasize the benefit, as ever pointed out by the Basel Committee (2009), of the
stability of funding to mitigate transformation risk. These results shed light on the negative
impact of the liquidity shortages on funding markets that increase the instability of bank
funding and transformation risk.
Finally, the transformation risk neutral of liquidity creation may be useful to add to the
debate on liquidity assessment in banking. In a prudential approach, this level of liquidity
creation could be considered to appreciate the ability of banks to face transformation risk
when they create liquidity. In addition, by better understanding what factors significantly
impact bank exposure to transformation risk, it can help banks to improve their risk
management framework.
30
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33
Table 1: Distribution of US and European commercial banks
United States
Europe
Austria
Belgium
Cyprus
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Liechtenstein
Malta
Netherlands
Norway
Portugal
Spain
Sweden
Switzerland
United Kingdom
Banks
available in
Bloomberg
Banks
included in
our final
sample
645
225
8
4
4
44
2
22
15
12
2
3
24
2
4
2
23
6
15
4
22
7
574
207
8
3
4
38
2
22
14
12
2
3
22
2
4
2
20
6
15
4
18
6
Total assets of
banks in final
sample / total assets
of the banking
system
66.4
60.4
57.3
80.3
69.7
60.6
71.2
62.1
40.1
80.6
66.3
31.3
59.6
50.1
32.5
47.6
70.3
55.3
64.4
72.6
74.8
61.5
Source: Bloomberg, European Central Bank, Bank of England, National Bank of Switzerland, Sveriges Riskbank, Danmarks
Nationalbank, Central Bank of Iceland, FDIC and Finance Norway (2000-2008). To deal with the issue of sample
representativeness, we compare aggregate total assets of banks included in our final sample (i.e., US and European publicly
traded commercial banks) to aggregate total assets of the whole banking system.
Table 2: General statistical description of our data set of US and European commercial
banks, on average from 2000 to 2008
Total assets
Total
Total loans /
in billion
deposits /
total assets
USD
total assets
Provisions
Tier 1
for loan
capital /
losses /
total assets
total loans
Tier 1 & 2
capital /
RWA
ROA
Total
interest
income /
total income
Mean
48.9
66.4
70.2
0.5
8.2
13.2
0.9
72.3
Median
1.1
68.3
75.4
0.3
7.7
12.5
0.9
75.9
Max
3768.2
95.1
96.0
7.2
35.2
34.0
6.9
100.0
Min
0.02
3.7
4.1
-1.2
0.1
4.5
-13.3
4.7
Std. Dev.
222.5
14.2
17.0
0.6
3.4
3.3
0.9
15.6
Source: Bloomberg (2000-2008). All variables are expressed in percentage (except Total assets). Total assets in billion USD;
Total loans / total assets: (commercial loans + consumer loans + other loans) / total assets; Total deposits / total assets:
(demand deposits + saving deposits + time deposits + other time deposits) / total assets; Total provisions for loan losses /
total loans: total provisions for loan losses / (commercial loans + consumer loans + other loans); Tier 1 capital / total assets:
Tier 1 capital / total assets; Tier 1 & 2 capital / RWA: (tier 1 capital + tier 2 capital) / total risk weighted assets; ROA: net
income / total assets; Total interest income / total income: (interest income from loans + resale agreements + interbank
investments + other interest income or losses) / total income.
34
Table 3: Balance sheet weighting used to calculate the liquidity creation
Assets
Liquidity level
Weights
Liquid
-0.5
Semi Liquid
0
Short term marketable assets
Liquid
-0.5
Commercial loans
Illiquid
0.5
Consumer loans
Semi Liquid
0
Other loans
Semi Liquid
0
Long term marketable assets
Semi Liquid
0
Net fixed assets
Illiquid
0.5
Other assets
Illiquid
0.5
Semi Liquid
0
Liquid
0.5
Cash and near cash items
Interbank assets
Custumer acceptances
Liabilities
Demand deposits
Liquid
0.5
Time deposits
Semi Liquid
0
Other term deposits
Semi Liquid
0
Short term borrowings
Liquid
0.5
Other short term liabilities
Liquid
0.5
Long term borrowings
Semi Liquid
0
Other long term liabilities
Semi Liquid
0
Subordinated debentures
Illiquid
-0.5
Prefered equity
Illiquid
-0.5
Minority interests
Illiquid
-0.5
Shareholder common capital
Illiquid
-0.5
Retained earnings
Illiquid
-0.5
Saving deposits
35
Table 4: Balance sheet weighting used to calculate the net stable funding difference
Required amount of stable funding
Assets
Corresponding definition from the BIS
Weights
Cash and near cash items
Cash
0
Interbank assets
Non renewable loans to financials
with remaining maturity < 1 yr
0
Marketable securities and other
short term investments
Short term unsecured actively traded
instruments (with remaining maturity
< 1 yr)
0
Commercial loans
All other assets
1
Consumer loans
Loans to retail clients (with remaining
maturity < 1 yr)
Other loans
All other assets
Long term investment
Unemcumbered listed equity or non
financial senior unsecured corporate
bonds rated at least A- (with
remaining maturity < 1 yr)
Net fixed assets
All other assets
1
Other assets
All other assets
1
Custumer acceptances
Unemcumbered listed equity or non
financial senior unsecured corporate
bonds rated at least A- (with
remaining maturity < 1 yr)
0.85
1
0.5
0.5
Available amount of stable funding
Liabilities
Demand deposits
Saving deposits
Corresponding definition from the BIS
70% of deposits of retail and small
business customers (non-maturity or
residual maturity < 1yr)
Weights
0.7
0.7
Time deposits
Other liabilities with an effective
maturity > 1 yr
1
Other term deposits
Other liabilities with an effective
maturity > 1 yr
1
Short term borrowings
All other liabilities or equity not
included above
0
Other short term liabilities
All other liabilities or equity not
included above
0
Long term borrowings
Other liabilities with an effective
maturity > 1 yr
1
Other long term liabilities
Other liabilities with an effective
maturity > 1 yr
1
Subordinated debentures
Prefered equity
Minority interests
Shareholder common capital
Retained earnings
1
Tier 1 & 2 capital instruments, other
preferred shares and capital
instruments in excess of Tier 2
allowable amount having an effective
maturity > 1 yr
1
1
1
1
Note: The net stable funding difference (NSFD) is the difference of the required amount of stable funding and the available
amount of stable funding, scaled by total assets. It is based on the net stable funding ratio as defined in the Basel III accords
(BIS, 2009). For further details about the weighting of bank balance sheet as suggested by the BIS (2009) to compute this
ratio, see appendix 1.
36
Table 5: Statistical analysis of the liquidity creation (LC) and the net stable funding
difference (NSFD), for US and European banks from 2000 to 2008
LC
Pearson
coefficient of
correlation
NSFD
Mean
Std Dev
Mean
Std Dev
All banks
31.6
12.7
-7.9
14.0
0.72 ***
(82.81)
US banks
31.3
13.2
-10.8
11.3
0.82 ***
(98.45)
European banks
32.4
11.5
-0.2
17.1
0.69 ***
(39.12)
3.08 ***
(0.00)
1.31 ***
(0.00)
28.77 ***
(0.00)
2.28 ***
(0.00)
-
Test statistic &
%level
Note: All variables are expressed in percentage. LC: liquidity creation / total assets, for further details about the computation of
LC, see table 3; NSFD: (required amount of stable funding - available amount of stable funding) / total assets, for further
details about the computation of NSFD, see table 4. T-statistics test for null hypothesis of identical means or null Pearson’s
coefficient of correlation; *, **, *** indicate statistical significance respectively at the 10%, 5%, and 1% level, for bilateral
test.
Table 6: Average comparison of the components of the liquidity creation (LC) and the net
stable funding difference (NSFD), for US and European banks from 2000 to 2008
LC
US banks
European banks
Test statistic &
%level
NSFD
IA_TA
61.7
IL_TA
30.4
RSF_TA
70.1
ASF_TA
80.9
59.5
27.0
68.2
68.4
6.87 ***
(0.00)
-16.41 ***
(0.00)
-5.49 ***
(0.00)
-52.25 ***
(0.00)
Note: All variables are expressed in percentage. IA_TA corresponds to all illiquid assets, i.e., to totally illiquid assets and to the
semi liquid assets that are illiquid. To calculate illiquid assets, we assign a weight of 1 to all illiquid assets and a weight of 0.5
to all semi liquid assets; IA_TA: (0*liquid assets + 0.5*semi liquid assets + 1*illiquid assets) / total assets. IL_TA corresponds
to all illiquid liabilities, i.e., to totally illiquid liabilities and to the semi liquid liabilities that are illiquid. To calculate illiquid
liabilities, we assign a weight of 1 to all illiquid liabilities and a weight of 0.5 to all semi liquid liabilities; IL_TA: (0*liquid
liabilities + 0.5*semi liquid liabilities + 1*illiquid liabilities) / total assets. For further details about the breakdown of assets
and liabilities by liquidity categories, see table 3. RSF corresponds to the required amount of stable funding, i.e., the amount of
assets that cannot be readily monetised. It is the sum of all assets weighted by their corresponding required stable funding
factor; RSF: (0*cash and near cash items + 0*interbank assets + 0*marketable assets and other short term investments +
1*commercial loans + 0.85*consumer loans + 1*other loans + 0.5*long term investments + 1*net fixed assets + 1*other assets
+ 0.5*customer acceptances) / total assets. ASF corresponds to the available amount of stable funding, i.e., 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; ASF: (0.7*demand and saving deposits + 1*time and other time deposits + 0*short term
borrowings and other short term liabilities + 1*long term borrowings and other long term liabilities + 1*subordinated debts +
1*equity) / total assets. For further details about the breakdown of assets and liabilities according to the importance of their
stability, see table 4. T-statistics test for null hypothesis of identical means; *, **, *** indicate statistical significance
respectively at the 10%, 5%, and 1% level, for bilateral test.
37
Table 7: Average comparison of liquid versus illiquid liabilities in the liquidity creation
(LC), for US and European banks from 2000 to 2008
US banks
European banks
Test statistic &
%level
DEPO_L
60.2
DEPO_IL
17.1
STMD_L
6.2
LTMD_L
3.3
LTMD_IL
3.3
K_IL
10.0
42.3
9.0
21.6
9.1
9.1
9.0
-55.06 ***
(0.00)
-44.53 ***
(0.00)
60.05 ***
(0.00)
47.75 ***
(0.00)
47.75 ***
(0.00)
-10.31 ***
(0.00)
Note: All variables are expressed in percentage. Liquid liabilities correspond to all liquid liabilities and to the semi liquid
assets that are liquid. To calculate liquid liabilities, 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. To
calculate illiquid liabilities, 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 3. DEPO_L: (1*demand and saving deposits
+ 0.5*time and other time deposits) / total assets; DEPO_IL: 0.5*time and other time deposits / total assets; STMD_L: 1*short
term borrowings and other short term liabilities / total assets; LTMD_L: 0.5*long term borrowings and other long term
liabilities / total assets; LTMD_IL: 0.5*long term borrowings and other long term liabilities / total assets; K_IL: 1*equity / total
assets. T-statistics test for null hypothesis of identical means; *, **, *** indicate statistical significance respectively at the
10%, 5%, and 1% level, for bilateral test.
Table 8: Average comparison of stable versus unstable liabilities in the net stable funding
difference (NSFD), for US and European banks from 2000 to 2008
US banks
European banks
Test statistic &
%level
DEPO_STB
64.4
DEPO_USTB
12.9
STMD_USTB
6.2
LTMD_STB
6.5
K_STB
10.0
41.3
10.0
21.6
18.2
9.0
-77 ***
(0.00)
-24.51 ***
(0.00)
60.05 ***
(0.00)
47.75 ***
(0.00)
-10.31 ***
(0.00)
Note: All variables are expressed in percentage. 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. DEPO_STB: (0.7*demand and saving deposits + 1*time and other
time deposits) / total assets; DEPO_USTB: 0.3* demand and saving deposits / total assets; STMD_USTB: 1*short term
borrowings and other short term liabilities / total assets; LTMD_STB: 1*long term borrowings and other long term liabilities /
total assets; K_STB: 1*equity / total assets. T-statistics test for null hypothesis of identical means; *, **, *** indicate statistical
significance respectively at the 10%, 5%, and 1% level, for bilateral test.
Table 9: Statistical analysis of the liquidity creation (LC) and the net stable funding
difference (NSFD), for US banks by size from 2000 to 2008
LC
NSFD
Mean
Std Dev
Mean
Std Dev
Large banks
32.1
12.2
-9.0
11.5
Small banks
30.8
13.8
-12.1
11.0
-3.26 ***
(0.00)
1.29 ***
(0.00)
-9.21 ***
(0.00)
1.08 ***
(0.04)
Test statistic &
%level
Pearson
coefficient of
correlation
0.81 ***
(60.40)
0.84 ***
(80.37)
-
Note: All variables are expressed in percentage. A bank is considered as large if its total assets is greater than one billion USD.
LC: liquidity creation / total assets, for further details about the computation of LC, see table 3; NSFD: (required amount of
stable funding - available amount of stable funding) / total assets, for further details about the computation of NSFD, see table
4. T-statistics test for null hypothesis of identical means or null Pearson’s coefficient of correlation; *, **, *** indicate
statistical significance respectively at the 10%, 5%, and 1% level, for bilateral test.
38
Table 10: Average comparison of the components of the liquidity creation (LC) and the
net stable funding difference (NSFD), for US banks by size from 2000 to 2008
LC
Large banks
Small banks
IA_TA
61.4
62.0
IL_TA
29.3
31.1
Test statistic &
%level
1.62
(0.11)
9.02 ***
(0.00)
NSFD
RSF_TA
ASF_TA
69.4
78.4
70.7
82.7
3.89 ***
(0.00)
24.55 ***
(0.00)
Note: All variables are expressed in percentage. A bank is considered as large if its total assets is greater than one billion USD.
IA_TA corresponds to all illiquid assets, i.e., to totally illiquid assets and to the semi liquid assets that are illiquid. To calculate
illiquid assets, we assign a weight of 1 to all illiquid assets and a weight of 0.5 to all semi liquid assets; IA_TA: (0*liquid assets
+ 0.5*semi liquid assets + 1*illiquid assets) / total assets. IL_TA corresponds to all illiquid liabilities, i.e., to totally illiquid
liabilities and to the semi liquid liabilities that are illiquid. To calculate illiquid liabilities, we assign a weight of 1 to all illiquid
liabilities and a weight of 0.5 to all semi liquid liabilities; IL_TA: (0*liquid liabilities + 0.5*semi liquid liabilities + 1*illiquid
liabilities) / total assets. For further details about the breakdown of assets and liabilities by liquidity categories, see table 3.
RSF corresponds to the required amount of stable funding, i.e., the amount of assets that cannot be readily monetised. It is the
sum of all assets weighted by their corresponding required stable funding factor; RSF: (0*cash and near cash items +
0*interbank assets + 0*marketable assets and other short term investments + 1*commercial loans + 0.85*consumer loans +
1*other loans + 0.5*long term investments + 1*net fixed assets + 1*other assets + 0.5*customer acceptances) / total assets.
ASF corresponds to the available amount of stable funding, i.e., 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; ASF: (0.7*demand and
saving deposits + 1*time and other time deposits + 0*short term borrowings and other short term liabilities + 1*long term
borrowings and other long term liabilities + 1*subordinated debts + 1*equity) / total assets. For further details about the
breakdown of assets and liabilities according to the importance of their stability, see table 4. T-statistics test for null hypothesis
of identical means; *, **, *** indicate statistical significance respectively at the 10%, 5%, and 1% level, for bilateral test.
Table 11: Average comparison of liquid versus illiquid liabilities in the liquidity creation
(LC), for US banks by size from 2000 to 2008
Large banks
Small banks
DEPO_L
58.0
61.8
DEPO_IL
15.6
18.2
STMD_L
8.9
4.2
LTMD_L
3.8
2.9
LTMD_IL
3.8
2.9
K_IL
9.9
10.1
Test statistic &
%level
13.40 ***
(0.00)
14.32 ***
(0.00)
-28.17 ***
(0.00)
-10.93 ***
(0.00)
-10.93 ***
(0.00)
2.05 **
(0.04)
Note: All variables are expressed in percentage. A bank is considered as large if its total assets is greater than one billion USD.
Liquid liabilities correspond to all liquid liabilities and to the semi liquid assets that are liquid. To calculate liquid liabilities,
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. To calculate illiquid liabilities, 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 3. DEPO_L: (1*demand and saving deposits + 0.5*time and other time deposits) / total assets;
DEPO_IL: 0.5*time and other time deposits / total assets; STMD_L: 1*short term borrowings and other short term liabilities /
total assets; LTMD_L: 0.5*long term borrowings and other long term liabilities / total assets; LTMD_IL: 0.5*long term
borrowings and other long term liabilities / total assets; K_IL: 1*equity / total assets. T-statistics test for null hypothesis of
identical means; *, **, *** indicate statistical significance respectively at the 10%, 5%, and 1% level, for bilateral test.
Table 12: Average comparison of stable versus unstable liabilities in the net stable
funding difference (NSFD), for US and European banks from 2000 to 2008
Large banks
Small banks
Test statistic &
%level
DEPO_STB
61.0
66.9
DEPO_USTB
12.7
13.1
STMD_USTB
8.9
4.2
LTMD_STB
7.6
5.7
K_STB
9.9
10.1
21.16 ***
(0.00)
2.95 ***
(0.00)
-28.18 ***
(0.00)
-10.93 ***
(0.00)
2.04 ***
(0.04)
Note: All variables are expressed in percentage. A bank is considered as large if its total assets is greater than one billion USD.
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.
DEPO_STB: (0.7*demand and saving deposits + 1*time and other time deposits) / total assets; DEPO_USTB: 0.3* demand
and saving deposits / total assets; STMD_USTB: 1*short term borrowings and other short term liabilities / total assets;
LTMD_STB: 1*long term borrowings and other long term liabilities / total assets; K_STB: 1*equity / total assets. T-statistics
test for null hypothesis of identical means; *, **, *** indicate statistical significance respectively at the 10%, 5%, and 1%
level, for bilateral test.
39
Table 13: The transformation risk neutral level of liquidity creation (TRNLC), for US
and European banks over the 2000-2008 period
β
α
TRNLC
All banks
0.74 ***
(75.11)
37.50 ***
(422.39)
37.5
European banks
0.60 ***
(46.51)
32.56 ***
(377.97)
32.6
US banks
0.81 ***
(60.72)
40.11 ***
(270.22)
40.3
US banks - Large
0.78 ***
(41.03)
39.13 ***
(218.13)
39.2
US banks - Small
0.82 ***
(43.26)
40.67 ***
(174.77)
40.9
Note: All variables are expressed in percentage. A bank is considered as large if its total assets is greater than one billion
USD. LC: liquidity creation / total assets, for further details about the computation of LC, see table 3; NSFD: (required
amount of stable funding - available amount of stable funding) / total assets, for further details about the computation of
NSFD, see table 4. Coefficients α and β are obtained by estimating equation (1): LC i,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. *, **, ***
indicate statistical significance respectively at the 10%, 5%, and 1% level. TRNLC is the transformation risk level of
N
liquidity creation. It corresponds to the average cross section fixed effects (i.e.,
∑
i= 1
(α + α i ) / N , subscript i denoting bank).
Table 14: Descriptive statistics of our main explanatory variables, for US and European
commercial banks, on average from 2000 to 2008
Variables
T12_TA
ROA
PLL_TLO
MKT_POW
GDP_GWT
CB
IBK1M_CB
LN_TA
CONTROL
HHI_LOAN
HHI_ILASSET
HHI_STFUND
SECLO_TLO
SECLO_IA
STMDBT_STDBT
Mean
9.6
0.8
0.5
1.7
2.3
3.0
0.2
7.6
10.5
0.2
0.2
0.5
42.2
37.0
20.1
Median
9.0
0.9
0.3
0.0
2.5
2.3
0.1
7.0
11.0
0.1
0.1
0.5
41.0
35.6
13.9
Max
60.2
6.9
7.2
74.5
9.5
22.0
3.5
15.1
12.0
1.0
1.0
1.0
99.4
97.7
100.0
Min
0.8
-15.1
-1.2
0.0
-3.5
0.3
-0.4
2.8
4.0
0.0
0.0
0.0
0.0
0.0
0.0
Std. Dev.
3.8
1.0
0.6
6.3
1.3
1.9
0.2
2.1
1.3
0.2
0.2
0.3
20.9
19.0
19.8
Obs.
6414
6440
6289
6414
7029
7029
7029
6414
7029
6414
6414
6414
6414
6414
6414
Source: Bloomberg (2000-2008). All variables are expressed in percentage (except LN_TA, all HHI and CONTROL).
T12_TA: (tier 1 capital + tier 2 capital) / total assets; ROA: net income / total assets; PLL_TLO: total provisions for loan
losses / total loans; MKT_POW: total assets of bank i in country j / total assets of the banking system in country j;
GDP_GWT: annual growth rate of real GDP; CB: central bank policy rate; IBK1M_CB: spread of one month interbank rate
and central bank policy rate; LN_TA: log of total assets; CONTROL: index of supervisory regime; HHI_LOAN: normalised
Herfindalh Hirschman index for concentration on loans that are potentially securitisable versus on loans that cannot be
securitised; HHI_ILASSET: : normalised Herfindalh Hirschman index for concentration on loans that are potentially
securitisable versus on illiquid assets (i.e., including the loans that cannot be securitised and the other illiquid assets);
HHI_STFUND: normalised Herfindalh Hirschman index for concentration on short term deposits versus on short term
market debts; SECLO_TLO: consumer loans / total loans; SECLO_IA: consumer loans / (total loans + long term investments
+ customer acceptances + fixed assets + other assets); STMDBT_STDBT: short term market debts / (demand and saving
deposits + short term market debts).
40
Table 15: The determinants of transformation risk
This table shows the result of estimating equation (2) for US and European publicly traded commercial banks,
over the 2000-2008 period. The dependent variable is NSFD. Equations 2.a and 2.b correspond to the estimation
of equation (2) by considering alternatively two proxies of the concentration on loans that are potentially
securitisable. All explanatory variables are one year lagged (except LN_TA and CONTROL). See table 14 for the
definition of the explanatory variables. To deal with colinearity problems in all regressions, we orthogonalise
LN_TA with MKT_POW. We include cross section and time fixed effects and we use the Huber-White robust
covariance method. *, **, *** indicate statistical significance respectively at the 10%, 5%, and 1% level.
2. a
2. b
HHI_LOAN
0.07 ***
(16.66)
-
HHI_LOAN *
SECLO_TLO
-0.15 ***
(-18.33)
-
HHI_ILASSET
-
0.08 ***
(20.31)
HHI_ILASSET *
SECLO_IA
-
-0.17 ***
(-13.15)
HHI_STFUND
-0.03 ***
(-8.78)
-0.03 ***
(-8.77)
HHI_STFUND *
STMDBT_STDBT
0.27 ***
(13.87)
0.27 ***
(13.90)
T12_TA
-0.05 **
(-2.13)
-0.06 ***
(-2.39)
ROA
-0.07
(-0.56)
-0.06
(-0.45)
PLL_TLO
-1.73 ***
(-8.33)
-1.72 ***
(-8.30)
MKT_POW
-0.45 ***
(-16.50)
-0.47 ***
(-16.99)
GDP_GWT
1.16 ***
(8.07)
1.15 ***
(8.05)
CB
1.57 ***
(11.37)
1.54 ***
(11.23)
IBK1M_CB
2.01 ***
(3.77)
2.03 ***
(3.86)
LN_TA
-0.01 ***
(-19.88)
-0.01 ***
(-20.24)
CONTROL
-0.03 ***
(-39.88)
-0.03 ***
(-39.77)
C
0.20 ***
(18.47)
0.20 ***
(18.05)
0.82
1174
0.00
5567
0.82
1176
0.00
5567
R²
Fisher Stat
P-Value F
Total Obs.
41
APPENDIX 1
Table A.1: Summary of the balance sheet weighting used to calculate “net stable funding
ratio” as defined in the Basel III accords
Available funding source
Availability factor
Tier 1 & 2 Capital Instruments
Other preferred shares and capital instruments
in excess of Tier 2 allowable amount having an
effective maturity of one year or greater
100%
Other liabilities with an effective maturity of 1
year or greater
Less stable deposits of retail and small
business customers (non-maturity or residual
maturity < 1yr)
85%
Less stable deposits of retail and small
business customers that are not covered by
effective deposit insurance, high-value
deposits, internet deposits and foreign
currency deposits (non-maturity or residual
maturity < 1yr)
70%
Wholesale funding provided by nonfinancial
corporate customers (non-maturity or residual
maturity < 1yr)
50%
All other liabilities and equity not included
above
0%
Required funding source
Required factor
Cash
Short-term unsecured actively traded
instruments (< 1 yr)
Securities with exactly offsetting reverse repo
0%
Securities with remaining maturity < 1 yr
Non-renewable loans to financials with
remaining maturity < 1 yr
Debt issued or guaranteed by sovereigns,
central banks, BIS, IMF, EC, non-central
government, multilateral development banks
5%
Unencumbered non-financial senior unsecured
corporate bonds (or covered bonds) rated at
least AA, maturity ≥ 1 yr
20%
Unencumbered listed equity securities or nonfinancial senior unsecured corporate bonds (or
covered bonds) rated at least A-, maturity ≥ 1 yr
50%
Gold
Loans to non-financial corporate clients having
a maturity < 1 yr
Loans to retail clients having a maturity < 1 yr
85%
All other assets
100%
Source: “International framework for liquidity risk, measurement and monitoring”, 2009, Basel Committee of Banking
Regulation and Supervision, Consultative Document.
42
APPENDIX 2
Table A2.1: The determinants of transformation risk, for US and European banks
separately
This table shows the result of estimating equation (2) separately for US and European publicly traded commercial banks,
over the 2000-2008 period. The dependent variable is NSFD. Equations 2.a and 2.b correspond to the estimation of
equation (2) by considering alternatively two proxies of the concentration on loans that are potentially securitisable. All
explanatory variables are one year lagged (except LN_TA and CONTROL). See table 14 for the definition of the explanatory
variables. To deal with colinearity problems in all regressions, we orthogonalise LN_TA with MKT_POW. We include cross
section and time fixed effects and we use the Huber-White robust covariance method. *, **, *** indicate statistical
significance respectively at the 10%, 5%, and 1% level.
US banks
2. a
European banks
2. b
2. a
2. b
HHI_LOAN
0.11 ***
(9.89)
-
-0.004
(-0.28)
-
HHI_LOAN *
SECLO_TLO
-0.22 ***
(-10.61)
-
-0.07 ***
(-3.33)
-
HHI_ILASSET
-
0.12 ***
(11.13)
-
0.03 ***
(2.34)
HHI_ILASSET *
SECLO_IA
-
-0.22 ***
(-6.75)
-
-0.14 ***
(-5.71)
HHI_STFUND
-0.07 *
(-1.79)
-0.07 *
(-1.77)
-0.11 ***
(-11.31)
-0.11 ***
(-11.24)
HHI_STFUND *
STMDBT_STDBT
0.11 **
(2.18)
0.11 **
(2.20)
0.40 ***
(15.97)
0.40 ***
(16.07)
-0.13 ***
(-3.03)
-0.15 ***
(-3.41)
0.04
(0.61)
0.05
(0.80)
-0.28
(-1.48)
-0.26
(-1.39)
-0.94 ***
(-2.70)
-0.93 ***
(-2.67)
-2.52 ***
(-8.65)
-2.52 ***
(-8.65)
-2.88 ***
(-8.19)
-2.84 ***
(-8.11)
MKT_POW
0.03
(0.09)
0.04
(0.11)
-0.22 ***
(-7.79)
-0.22 ***
(-7.91)
GDP_GWT
0.52 ***
(3.91)
0.52 ***
(3.90)
-0.26 *
(-1.76)
-0.25 *
(-1.74)
CB
0.19 ***
(3.25)
0.19 ***
(3.12)
1.46 ***
(8.25)
1.44 ***
(8.06)
IBK1M_CB
7.24 ***
(6.33)
7.26 ***
(6.35)
0.01
(0.02)
0.11
(0.20)
LN_TA
0.03 ***
(8.50)
0.03 ***
(8.14)
0.01 ***
(6.46)
0.01 ***
(7.22)
-
-
0.0004
(0.36)
0.001
(1.27)
-0.12 ***
(-13.57)
-0.13 ***
(-14.05)
-0.02
(-1.18)
-0.03 **
(-2.11)
0.78
21
0.00
4036
0.78
21
0.00
4036
0.80
426
0.00
1531
0.80
426
0.00
1531
T12_TA
ROA
PLL_TLO
CONTROL
C
R²
Fisher Stat
P-Value F
Total Obs.
43
Table A2.2: The determinants of transformation risk, for large and small banks
separately
This table shows the result of estimating equation (2) separately for large and small banks, over the 2000-2008 period. A
bank is considered as large if its total assets is greater than one billion USD. The dependent variable is NSFD. Equations 2.a
and 2.b correspond to the estimation of equation (2) by considering alternatively two proxies of the concentration on loans
that are potentially securitisable. All explanatory variables are one year lagged (except LN_TA and CONTROL). See table 14
for the definition of the explanatory variables. Besides, US banks account for a large share of our sample (574 US banks
against 207 European banks) and small US banks account for a large share of the sample of small banks (170 US banks
against 37 European banks). Consequently, we consider bank location to check the stability of our results according to bank
size. Thus, we add in equation (2), all the interactions of our explanatory variables with a dummy variable that takes the
value of 1 for US banks and 0 otherwise (DUM_LOC). To deal with colinearity problems in all regressions, we
orthogonalise LN_TA with MKT_POW. We include cross section and time fixed effects and we use the Huber-White robust
covariance method. *, **, *** indicate statistical significance respectively at the 10%, 5%, and 1% level.
2. a
Coefficient
C(1)
HHI_LOAN
C(2)
HHI_LOAN * DUM_LOC
C(3)
HHI_LOAN *
SECLO_TLO
C(4)
HHI_LOAN *
SECLO_TLO * DUM_LOC
C(5)
HHI_STFUND
C(6)
HHI_STFUND * DUM_LOC
C(7)
C(8)
HHI_STFUND *
STMDBT_STDBT
HHI_STFUND *
STMDBT_STDBT * DUM_LOC
C(9)
T12_TA
C(10)
T12_TA * DUM_LOC
C(11)
ROA
C(12)
ROA * DUM_LOC
C(13)
PLL_TLO
C(14)
PLL_TLO * DUM_LOC
C(15)
MKT_POW
C(16)
MKT_POW * DUM_LOC
C(17)
GDP_GWT
C(18)
GDP_GWT * DUM_LOC
C(19)
CB
C(20)
CB * DUM_LOC
C(21)
IBK1M_CB
C(22)
IBK1M_CB * DUM_LOC
C(23)
LN_TA
C(24)
LN_TA * DUM_LOC
C(25)
CONTROL
C(26)
CONTROL * DUM_LOC
C(27)
C
Wald stat
and % level
to reject:
R²
Fisher Stat
P-Value F
Total Obs.
c(1)+c(2)=0
c(1)+c(2)+c(3)+c(4)=0
c(5)+c(6)=0
c(5)+c(6)+c(7)+c(8)=0
2. b
Large banks
0.04 ***
(2.63)
0.03 *
(1.63)
-0.14 ***
(-6.35)
Small banks
0.20 **
(2.15)
-0.07
(-0.80)
-0.30 ***
(-3.13)
-0.05 *
(-1.78)
0.08
(0.79)
-0.05 ***
(-3.84)
0.03 ***
(2.45)
0.33 ***
(10.86)
-0.11 *
(-1.75)
-0.03
(-0.48)
-0.07
(-0.87)
-0.91 **
(-2.08)
0.59
(1.21)
-1.17 ***
(-2.81)
-1.18 **
(-1.99)
-0.25 ***
(-9.08)
-0.02
(-0.14)
-0.36 **
(-2.13)
0.54 **
(2.21)
1.33 ***
(6.35)
-0.83 ***
(-3.69)
-0.08
(-0.17)
6.17 ***
(3.74)
0.002
(1.08)
0.005 **
(2.15)
0.001
(1.02)
-0.001
(1.04)
-0.01
(-0.78)
0.82
520
0.00
3013
0.07 ***
-0.12 ***
-0.01 ***
0.20 ***
-0.04 ***
(-2.73)
0.02
(0.90)
0.03
(0.64)
0.03
(0.35)
0.24 **
(1.97)
-0.26 **
(-2.03)
-0.96 *
(-1.67)
0.65
(1.07)
-2.44 ***
(-3.75)
0.62
(0.82)
-3.25
(-1.35)
-353.46 ***
(-3.77)
0.74 ***
(2.37)
-0.22
(-0.63)
0.77 ***
(2.82)
-0.70 ***
(-2.45)
2.48
(0.74)
3.06
(0.83)
-0.001
(-0.12)
-0.001
(-0.12)
0.01 **
(1.92)
-0.01 **
(1.93)
-0.19 ***
(-3.10)
0.79
356
0.00
2554
0.12 ***
-0.10 ***
-0.02 ***
0.04
HHI_ILASSET
HHI_ILASSET * DUM_LOC
HHI_ILASSET * SECLO_IA
HHI_ILASSET * SECLO_IA *
DUM_LOC
HHI_STFUND
HHI_STFUND * DUM_LOC
HHI_STFUND *
STMDBT_STDBT
HHI_STFUND *
STMDBT_STDBT * DUM_LOC
T12_TA
T12_TA * DUM_LOC
ROA
ROA * DUM_LOC
PLL_TLO
PLL_TLO * DUM_LOC
MKT_POW
MKT_POW * DUM_LOC
GDP_GWT
GDP_GWT * DUM_LOC
CB
CB * DUM_LOC
IBK1M_CB
IBK1M_CB * DUM_LOC
LN_TA
LN_TA * DUM_LOC
CONTROL
CONTROL * DUM_LOC
C
R²
Fisher Stat
P-Value F
Total Obs.
c(1)+c(2)=0
c(1)+c(2)+c(3)+c(4)=0
c(5)+c(6)=0
c(5)+c(6)+c(7)+c(8)=0
Large banks
0.07 ***
(5.82)
0.02
(1.40)
-0.18 ***
(-6.46)
Small banks
0.18 ***
(2.62)
-0.05
(-0.71)
-0.28 ***
(-3.72)
-0.005
(-0.13)
0.03
(0.37)
-0.05 ***
(-3.73)
0.03 **
(2.30)
0.33 ***
(10.97)
-0.11 *
(-1.70)
-0.03
(-0.46)
-0.07
(-0.88)
-0.87 **
(-2.00)
0.51
(1.06)
-1.15 ***
(-2.74)
-1.23 **
(-2.06)
-0.25 ***
(-9.30)
-0.01
(-0.06)
-0.38 **
(-2.28)
0.56 **
(2.29)
1.30 ***
(6.17)
-0.81 ***
(-3.58)
0.03
(0.07)
5.85 ***
(3.55)
0.004 **
(2.03)
0.004 **
(2.03)
0.002 *
(1.84)
-0.01 *
(1.87)
-0.03 *
(-1.81)
0.82
520
0.00
3013
0.09 ***
-0.09 ***
-0.02 ***
0.20 ***
-0.04 ***
(-2.84)
0.02
(1.05)
0.04
(0.74)
0.04
(0.43)
0.23 **
(1.95)
-0.26 **
(-2.13)
-0.89
(-1.57)
0.64
(1.05)
-2.39 ***
(-3.62)
0.58
(0.75)
-2.08
(-0.84)
-356.98 ***
(-3.81)
0.76 ***
(2.43)
-0.24
(-0.69)
0.77 ***
(2.70)
-0.70 ***
(-2.37)
2.77
(0.83)
2.84
(0.78)
-0.0001
(-0.01)
-0.0001
(-0.01)
0.01 *
(1.75)
-0.01 *
(1.78)
-0.18 ***
(-3.08)
0.79
355
0.00
2554
0.13 ***
-0.13 ***
-0.02 ***
0.05
44