1. finance
2. bank

Why bank liquidity needs to be regulated?
This version: June 2012
Sonia Ondo Ndong* and Peixin Zhang†
Abstract:
Our paper focuses on banking regulation, especially on the necessity of liquidity regulation as
complement to traditional capital-based banking regulation. During the 2007/2008 financial crisis,
some studies on troubled banks prove that the principal problem of these banks in trouble was
illiquidity rather than insolvency. In fact, most of troubled banks were dropped down by the lack
of stable funding sources which are less sensitive to market fluctuations instead of the credit risk
raised by their toxic assets. In this paper, by implementing the traditional econometric analysis
coupled with the neural-network method on a panel data of European banks during 2000 and
2008, we argue that liquidity regulation should be conceived as a both quantitative and
qualitative requirement that is complementary to capital-based regulation rather than a simple
separate regulatory ratio to restrict banks’ maturity transformation.
Keywords: banking strategies; liquidity regulation
JEL: G21, G28, G32
*
University of Paris West Nanterre La Défense, EconomiX, address: Bureau G 301, 200 Avenue de la République,
Nanterre 92001, France, tel: 01 40 97 59 07, e-mail: [email protected]
†
University of Paris West Nanterre La Défense, EconomiX, address: Bureau G 301, 200 Avenue de la République,
Nanterre 92001, France, tel: 01 40 97 59 07, e-mail: [email protected]
1
1. Introduction
The lesson of the global financial crisis beginning in the summer of 2007 shows that the actual
banking regulation on the basis of the Basel II agreement is not sufficient to handle the problem
in the banking sector due to banks’ excessive risk-taking through diverse financial innovations.
Many studies have pointed to the main flaw of the present regulatory regime: a high dependence
on the capital-based regulation, especially risk-based capital regulation, which has been criticized
by some authors before the 2007/2008 financial crisis (Calem and Rob, 1999; Blum, 1999; Rime,
2001; Heid, 2007).
Financial regulators and academics start to realize that regulatory reforms in the financial system
are necessary (e.g. see FSA, 2009; De Larosière Group, 2009; Goodhart, 2008; Dewatripont,
Rochet and Tirole, 2010). Some authors have proposed several paths for future regulatory
reforms from the macro-prudential point of view. For instance, Adrian and Brunnermeier (2008)
put forward a CoVar measurement for systemically important institutions; Kashyap, Rajan, and
Stein (2008) suggest a counter-cyclical remedy for regulatory capital requirements; Krimminger
(2008) concentrates on the resolution of cross-border banks and the cooperation between
regulators; Allen and Carletti (2008) propose an additional balance sheet based on mark-tofunding evaluation for banks in order to reduce bank's liquidity risk. All these proposals are
aimed at capturing the systemic risk in order to achieve the financial stability objective of
regulators.
As recognized by many authors (e.g. Bunnermeier et al., 2009), this financial systemic crisis is a
liquidity crisis rather than a credit risk, and one of its main reasons is banks’ liquidity
vulnerability due to their strong maturity mismatch. This phenomenon justifies the argument of
Diamond and Rajan (2001; 2005) about the relationship between liquidity shortages and banking
crises. Therefore, some studies have focused on the improvement of liquidity regulation in
banking industry. For instance, Farhi, Golosov and Tsyvinski (2009) propose a simple
implementation of the optimum that imposes a constraint on the portfolio share that financial
intermediaries invest in short-term assets. Perotti and Suarez (2011) suggest that if the return to
the lending activities undertaken by the banks using this funding is heterogeneously distributed
across banks (or, similarly, over time), a Pigovian tax on short-term funding will dominate a net
2
stable funding ratio or a liquidity coverage ratio. Farhi and Tirole (2009) characterize the optimal
regulation, which takes the form of a minimum liquidity requirement coupled with monitoring of
the quality of liquid assets. Brunnermeier and Oehmke (2012) develop a model which shows that
excessive maturity mismatch may arise even in the absence of an anticipated ex-post intervention
by the central bank in order to promote to limit maturity in the financial institution.
In the present paper, we also focus on regulatory reforms in the banking system. Especially, we
are interested in liquidity regulation, more precisely in the justification and the modality for
implementing liquidity regulation in the banking system. We use panel data analysis to find
which type of risky banking strategies can significantly cause bank losses and thus destabilizing
the bank. Therefore, the most important concern for future regulatory reforms is to reduce the
risk-level of these significant banking strategies by imposing more appropriate regulation on
banks. An outstanding result of our paper, concluded from empirical evidence on a panel data set
of 25 banks from 14 European countries during the period between 2000 and 2008, is that the
interaction between risky (funding) liquidity strategy and risky leverage (capitalization) strategy
has a more detrimental effect on bank results.
Furthermore, by making an analysis on bank sponsored Special-Purpose Entities (SPEs)
domiciled in UK, we obtain a supplementary result that is the insignificant relationship between
the creation of SPEs and the decline in parent banks’ funding structure. This result contradicts the
argument of financial regulators and supervisors about the responsibility of SPEs for banks’
liquidity difficulties in the crisis (Loutskina, 2011). However, as our crude analysis is based on a
period without regulatory requirement on prudential information disclosure, lack of detailed
information about bank sponsored SPEs determine that our result is biased. Nevertheless, our
analysis is not meaningless. In fact, our result affirms that the lack of sufficient detailed
information has always been a main reason for the regulatory negligence on shadow banking
system. Therefore, we suggest both the necessity of a qualitative aspect in liquidity regulation
and the need for transparency in the banking system.
As regards the methodological aspect of this paper, we use nonlinear neural-network approach
with other usual estimators to perform our panel data analysis to enhance the robustness of our
estimation.
3
The rest of the paper proceeds as follows. Section 2 describes the data and the variables used.
Section 3 presents the empirical model and the results of the econometric analysis. Section 4
investigates the SPEs contribution to banks’ funding liquidity deterioration. Section 5 concludes
and discusses policy implications.
2. Data and Variables
We collect annual banking balance sheet and income-statement data from Bankscope database to
construct our sample. We use the data for the period between 2000 and 2008, a whole cycle
between two crises.1 At the start, information of our sample is gathered on commercial banks
(commercial and cooperative)2 of 14 European countries of which the banking sector is more
developed in comparison with other countries in the European Union.3 Then according to two
criteria, we trim our sample by ruling out some unqualified banks. As we are constructing a panel
data sample, continuous financial reporting is the first relevant criterion to construct our sample.
Besides, some banks retained in the first step did not communicate information on certain
variables used in our econometric analysis. Therefore, we also have to eliminate these banks.
Then the final dataset for testing includes 25 banks over 9 years (225 observations). In addition,
the data for our control variables is collected from IMF and ECB database. The data sample is
described in Table 1. Variables description is presented in Table 2.
2.1. Dependent variable
In this paper, we use the variation of banks' credit supply as a proxy for bank results. This
relationship is especially justified in the case where commercial banks encounter losses. When
1
The IT bubble burst in the U.S. in 2000, following by a euphoric phase in which the Fed’s low-interest rate policy
in favor of economy recovery. However, this behavior was one of the main reasons to explain the formation of the
real estate bubble. When the Fed changed its opinion by raising interest rates from 2005 (the BCE implemented the
same policy from 2006, an enormous amount of risk was stocked in the financial system. Finally, in the summer of
2007, the property bubble burst and another financial crisis broke out.
2
We exclude investment banks for the reason that the dependant variable used in our econometric analysis is the
variation of credit supply which is considered as proxy for banking results of commercial banks rather than
investment banks (see Section 2.2 for details).
3
We included Switzerland in our sample. Countries in our sample are countries are as follows: Belgium, Denmark,
Finland, France, Germany, Luxembourg, Netherlands, Norway, Portugal, Republic of Ireland, Spain, Sweden,
Switzerland, and United Kingdom. Since there are a large number of small Italian cooperative banks in Bankscope
database, we excluded them from our sample in order to minimize the selection bias.
4
banks make losses and need to meet regulatory requirements, they have two ways to absorb
losses: decreasing the volume of assets (contracting the supply of credit) or increasing the equity
capital (issuing new equity shares). From the point of view of regulators, the second is more
favorable to financial stability. However, the existing shareholders in reality prefer the first one.
The rationale for this is raising equity will dilute existing shareholders’ control power in the bank.
Moreover, risk-based capital regulation reinforces this behavior (Berger and Udell, 1994; Peek
and Rosengren, 1995).
In this paper, we use banking strategy variables to measure their impact on bank results. More
precisely, we focus on following categories of banking strategies: capitalization strategy,
liquidity strategy, portfolio strategy, and management strategy. To complete our empirical model,
we also introduce some control variables.
2.2. Explanatory Variables
2.2.1. Capitalization strategy
Here, we examine two types of capitalization strategies, leverage and regulatory capitalization.
Leverage. Because debt is relatively costless to capital equity, the bank has a great incentive to
use the former to finance their activities. Moreover, there is an increase in competition among
banks and in expected return by shareholders. This leads banks to implement a risky leverage
strategy by is using more costless funds (debt) rather than equity capital to finance its activities.4
However, the negative effect of a risky leverage strategy should not be neglected. A series of
authors (Brunnermeier et al., 2009; Shin, 2009) have shown that a bank experiencing a highleverage strategy is riskier during crisis. In particular, highly leveraged institutions have to
liquidate their assets at lower price or contract the supply of credit in order to reduce losses in the
downward spiral. According to them, leverage ratio reflects banks’ risk level, and the risk level of
banks’ leverage has an impact on bank results. Therefore, we use here banks' simple leverage
ratio (equity-to-asset ratio) as the first proxy for capitalization strategy.
4
According to Modigliani-Miller (1958) theorem, a higher leverage ratio (debt-to-equity) can increase firms' return
on equity. Therefore, risky leverage strategies of the bank allow maximizing shareholders' profit.
5
Regulatory capitalization. In last decades where competition in the banking industry was more
and more fierce, banks were forced to build a reputation of being riskless and profitable in order
to attract more customers.5 Since the capital requirement is qualified by regulators as a signal for
revealing banks' level of risk, banks can reassure investors about the bank's stability by holding a
high level of regulatory capital. It is usually an important instrument to conceal their real level of
risk for large banks that have more risky activities. Therefore, we are willing to examine whether
this type of banking strategy has an impact on bank results.
2.2.2. Liquidity strategy
We are interested in two types of bank liquidity. The first one is liability-side liquidity that is
measured in our empirical analysis by banks’ unstable funding ratio (short-term funding ratio)
and the second one is asset-side liability that is measured here by asset-side liquidity ratio.
Unstable funding ratio. The rationale for using this ratio is short-term funding is more costless
than long-term liabilities to finance their activities, although they are potentially detrimental to
banks’ stability. Nevertheless, if short-term funding is more prevailing in the bank's funding
structure, the bank has to confront the increasing cost of renewal in time of crisis.6 Besides, the
bank depending on short-term funding is more likely to be in the situation where creditors
demand higher repayment when the bank wants to renew these funds. Indeed this situation may
be aggravated by a systemic crisis in which the bank has already had a liquidity shortage. In this
case, the penalty rate (or high risk premium) demanded by financiers will increase the bank's
financing cost. Consequently, the bank has to choose between encountering losses (earns less)
and contracting the supply of credit, as showed by Prisman, Slovin, and Sushka (1986).
Asset-side liquidity ratio. This ratio measure how the bank handles maturity mismatches
between its assets and liabilities. Even though maturity transformation is the main activity of the
bank, a bank with large maturity mismatch will experience the fire sale of its assets, especially in
the crisis, as showed by Brunnermeier (2009). Therefore, a risky (smaller) asset-side liquidity
5
Keeley (1990) argue that banks having more market power hold more capital than required level. Berger et al.
(2008) show that large banks (Bank Holding Companies in U.S.) actively manage their capital ratios.
6
Morris and Shin (2004) show that traders who rely on short-term funding will encounter large losses by liquidating
their assets at lower price, and that their “fire sale” behavior contribute to the formation of liquidity black holes.
6
ratio (holding less liquid asset relative to liquid liabilities) may have impact on bank results.
2.2.3. Portfolio Strategy
Portfolio structure. Here we are interested in the composition of banks’ assets portfolio, namely
banking book activities and trading book activities. As banking book activities are more regulated
than trading book activities, the risk level of the former is usually lower.7 Therefore, holding
more banking book assets can reduce banks’ risk exposure, especially the exposure to market
risks. In contrast, since banks' trading book activities are highly related to financial markets,
banks are more sensitive to market fluctuations. Particularly in time of crisis, banks that hold
important trading book assets are prone to assuming losses. Thus we use banking book assets to
total assets ratio as the first proxy for portfolio strategy.
Size. Boyd and Runkle (1993) argue that there is a relation between bank size and the return on
assets and leverage and thus large banks are more profitable but riskier by being highly leveraged.
De Nicoló (2000) reports a positive and significant relationship between bank size and failure
probabilities for the United States, Japan, and several European countries. Then it is interesting to
examine whether the bank size has direct or indirect effect on the bank results. We use here the
size of sampled banks relative to total balance sheets of MFIs in their domestic country to
measure the impact of size on bank results.
Off-balance sheet activities. Today off-balance sheet activities play an important role in the
banking industry. It has became a new way for banks to maximize their profit by charging
commission for financial services (e.g. credit lines), since the profit margin of banks' traditional
activities decreased due to competition. In addition, banks create and sponsor SIVs to transfer
their credit risk, and then to reduce their capital requirement. It is in line with banks' profitmaximization objective. Nevertheless, we all know that this “originate to distribute” model of
banks played an important role in the subprime crisis. During the crisis, a bank that has large
exposure to off-balance-sheet activities has to reengage them into its own balance sheet by
reintermediation process. By doing so, the bank finally bear risks and encounter losses (Basel
7
Even if banking book activities are highly regulated, risk may appear in this type of assets. However, Holmström
and Tirole (2000) show that the banking book is usually jeopardized by the trading book at first, even though the
trading book is often used to hedge the banking book.
7
Committee on Banking Supervision, April 2008). We use here the ratio of off-balance-sheet
items to total assets.
Interbank activities. The bank's interbank claim can be used to measure the interconnection of
one bank with the others. Allen and Gale (2000) and Freixas, Parigi and Rochet (2000) establish
that contagion through interbank claims can make banks more fragile and cause bank losses. We
use the interbank ratio (bank loans to bank deposits) to test this type of portfolio strategy.
2.2.4. Management Strategy
As we know, managerial efficiency has an important effect on bank results. The role of an
efficient management strategy by bank managers is not only to reduce managerial cost to income
ratio, but also to release signals favorable to the bank in the market.8 In this section, we are
interested in three types of management strategies: managerial quality, loan loss provision and
ROE policy.
Managerial quality. Overhead expenses are related to personal expenses and non-interest
expenses in banking operations. Moreover, overheads are usually used to measure the bank’s
managerial quality. For instance, Levine (2001) shows that large overhead expenses reflect a less
efficient management of the bank. Therefore, we use overhead expenses to total assets ratio to
measure the managerial quality of our sampled banks.
Loan loss provision. Madura and McDaniel (1989) show that an increase in loan loss provision
has the potential to convey to the market a negative strong signal that is the poor management of
banks’ loan portfolio. Obviously, this bad news can weaken investors’ confidence so that the
bank is more likely to face the financing problem. From viewpoint of regulators, the more loan
loss provisions are held, the more the bank is risky. However, Madura and McDaniel (1989) also
recognize the possible positive stock market reaction to loan loss provision announcement. In
other words, an increase in loan loss provision may raise banks’ stock price and thus enhancing
capitalization.9 Moreover, holding more loan loss provision can be considered as one of active
8
See for example Diamond (1991) for the importance of signaling to build reputation.
9
These phenomena are also highlighted by Musumeci and Sinkey (1990), and reexamined by Docking, Hirschey and
Jones (1997).
8
risk management used by the bank to protect itself against credit risk. Therefore, which viewpoint
prevails? More loan loss provisions make the bank assume more loss or not? In this empirical
study, we use loan loss reserve ratio find out the answer.
ROE policy. Return on equity measures the rate of return on shareholder’s equity and thus a
bank’s (in general a firm) efficiency at generating profits from each unit of equity capital. This
ratio is also a sort of operational/management strategy that reflects the relation between banks’
equity capital and risk-taking.10 The lesson of the actual financial crisis is that banks established a
risky ROE policy (higher ROE) in favor of their shareholders, but their behavior generated a
potential social cost (privatizing gains and socializing losses). Therefore, we test in this paper
whether banks’ risky ROE policy is significantly detriment to their financial stability.
2.2.5. Interaction between liquidity and capitalization strategy
By focusing on the mechanism of the subprime crisis, some authors (Brunnermeier et al., 2009;
Calomiris, 2009; Hellwig, 2009) argue that highly leveraged institutions with large maturity
mismatches are prone to carry out a fire sale in time of distress and thus aggravating the impact
of the financial crisis. In other words, the joint implementation of risky capitalization strategy and
risky liquidity strategy is dangerous both to individual banks and to the financial system.
Therefore, in this paper we provide empirical evidence on European banks to test whether the
interaction between two types of banking strategies has a significant effect on banks’ stability.
The variable used in the econometric model is the unstable-funding to equity ratio, which is
calculated by the division of unstable-funding ratio (UFS/TA) to simple leverage ratio (E/TA), by
taking into account the effect of both two types of strategies.
2.2.6. Control variables
As central bank policy had an important contribution to the formation of this cycle, we also
introduce the real interest rate (RIR) of our sampled banks’ domestic country as a control
variable.11 Besides, we take into account two other control variables, the importance of financial
10
Higher operating returns mean higher risk level. In a sense, a higher ROE signifies that the bank manager use less
equity capital to take more risks in business operation.
11
See footnote 1. Real interest rate is calculated as the difference between nominal interest rate and inflation rate.
9
sector to national economy (FSI, measured as the ratio of aggregated balance sheet of all MFIs to
GDP) and output gap.
3. Empirical model and results
The econometric analysis proceeds in two steps. In the first step, we use first-difference estimator
and neural-network estimator to estimate the pooled sample. By use of a between comparison, we
try to find some generalities and to identify the significant effect of certain variables for sampled
banks. In the second step, we use panel-data techniques to better capture the serially related and
cross-sectionally related components of sampled banks. The econometric specification we used to
test is as follows:
𝑉𝐴𝑅 = 𝛼 ∗ 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛽! 𝐿𝐸𝑉 + 𝛽! 𝑅𝐵𝐶𝑅 + 𝛽! 𝑈𝐹𝑆 + 𝛽! 𝐴𝑆𝐿𝑅 + 𝛽! 𝐵𝐵 + 𝛽! 𝑆𝐼𝑍𝐸 + 𝛽! 𝑂𝐵𝑆
+ 𝛽! 𝐵𝐿𝐵𝐷 + 𝛽! 𝐿𝐿𝑅𝑅 + 𝛽!" 𝑀𝑄 + 𝛽!! 𝑅𝑂𝐸 + 𝛽!"
𝑈𝐹𝑆
+ 𝛽!" 𝐺𝐴𝑃 + 𝛽!" 𝑅𝐼𝑅
𝐸
+ 𝛽!" 𝐹𝑆𝐼 + 𝜀
3.1. Pooled sample analysis
In this step, we also use the neural-network estimator in addition to the traditional linear firstdifference estimator to estimate the pooled sample. The rationale for using the neural-network
approach is that banking strategies are usually interdependent in banks’ business operation.
Especially in terms of riskiness, this interdependency can be interpreted as the amplification
effect of one risky banking strategy on another strategy or both has the similar effect on each
other. This peculiarity of banking strategies has been highlighted by many authors: for instance,
the relation between ROE and leverage (Galai and Masulis, 1976; Saunders, Strock and Travlos,
1990), the relation between leverage and liquidity (Adrian and Shin, 2010; Brunnermeier et al,
2009), the relation between the bank size and ROE and leverage (Boyd and Runkle, 1993), the
fact that banks' off-balance-sheet activities are usually supported by short-term funding strategies
(Borio 2008). Obviously, this interdependency brings nonlinearity to the econometric modeling.
Therefore, we are motivated to take into account the interdependency between banking strategies
by using a nonlinear approach as a complement to the linear approach.
10
The nonlinear method we used here is the neural-network approach, a nonlinear method coming
from the brain science of cognitive theory and neurophysiology, to this empirical study with the
purpose of finding which strategies of the bank can cause banks' losses.12 Indeed, economists
started to adopt neural network methods after a great technical improvement in these methods,
namely the application of back-propagation to neural network learning (Rumelhart et al. 1986).13
Without loss of generality, we use in this step the traditional (one hidden layer with three neurons)
Multi-layer-perceptron (MLP) neural network to conduct our estimation. 14 In addition, we
introduce a linear ''perceptron'' (jump connections) in addition to the nonlinear structure in order
to capture the linear relation. As a result, this structure allows us to capture both nonlinear
relations and potential linear relations. The hybrid optimization coupling gradient-descent and
genetic algorithm methods15 with the help of MATLAB programming allows us to perform the
estimation.
Table 4 and 5 show the result of pooled-sample estimation by use of linear first-difference
estimator and nonlinear neural-network approach.
3.2. Panel data estimation
In this step, we perform the estimation on our panel-data sample of 225 observations (25 banks
during 9 years from 2000 to 2008). Table 5 also provides econometric results with two types of
estimators we used in this panel data estimation, random-effect and fixed-effect estimator.
12
See McNelis (2005) for details in neural-network methodology.
13
This tool has been used for analyzing economic problems by a series of authors, especially in two fields, timeseries prediction and classification of economic agents. For application of neural network in time-series prediction,
for instance, White (1988) is interested in the capital market and use neural network to test the efficient market
hypothesis, and Bosarge (1993) argue that neural network allowing for nonlinearities can improve the quality of
forecast, and also some articles invested in other areas, such as the one of Franses and Draisma (1997) and Swanson
and White (1997) for macroeconomic variables, Church and Curram (1996) for consumers’ expenditure, or Kohzadi
et al. (1995) for agricultural economics.
14
Several architectures of neural network have been applied to economic studies (McNelis, 2005). However, as
summarized by Hamzaçebi, Akay and Kutay (2009), the most widely used type is the Multi-Layer-Perceptron (MLP)
neural network, which belongs to feed forward networks. Besides, Hornik, Stinchcomb, and White (1989) argue that
one hidden layer with two or three neurons yields good results in nonlinear function approximation at any accuracy
level.
15
Quagliarella and Vicini (1998) who point out that hybridization coupling gradient-descent and genetic algorithm
methods may lead to better solutions than those obtainable using the two methods individually
11
3.3. Results
The first-step estimation by use of neural-network approach and first-difference estimator shows
that the between comparison among sampled banks without taking into account time serial
feature allows to identify the significance of following variables, UFS_E, LLRR, LEV, UFS,
RBCR, MQ, ROE, SIZE and two control variables, FSI and GAP. In the second step panel-data
techniques (random effect and fixed effect) validate MQ, UFS_E, SIZE, RBCR, LLRR, GAP.16
However, when we focus on the performance of two panel-data techniques we use in the second
step, we find that the random effect estimator is more similar to the pooled OLS estimator than to
the fixed effect estimator.17 Therefore, the result of fixed effect estimator that is less biased
prevails. More precisely, the only three explanatory variables that have a significant impact on
bank results are loan loss provision (management strategy), size and the interaction between
liquidity and capitalization strategy.
3.3.1. Management strategy
Loan loss provision and managerial quality are both negatively significant to bank results.18 It
means that an increase in loan loss reserves and managerial expenses will cause losses to the
bank. Therefore, a risky management strategy is detrimental to the bank’s stability.
The impact of managerial quality on bank results is intuitive. High managerial expenses signify a
less efficient management of the bank. As in a nonfinancial firm, a poor management is more
likely to generate unexpected costs and to lower the firm’s performance. Likewise, when a bank’s
managerial quality is low, it is prone to assuming more losses.
Concerning loan loss provision, its impact is composed of two aspects. Firstly, a high loan loss
16
Because LEV, UFS, ROE, FSI are validated by neither of two panel-data estimators, we exclude them from
significant variables.
17
Starting from the error-component model 𝑦!" = 𝛽! + 𝛽! 𝑥!" + 𝑣! + 𝜖!" , the random effect estimator is obtained by
applying pooled OLS to the date after the following transformation, 𝑦!" − 𝜃𝑦! = 𝛽! 1 − 𝜃 + 𝛽! 𝑥!" − 𝜃𝑥! +
{ 1 − 𝜃 𝑣! + 𝜖!" − 𝜃𝜖! }. By calculating 𝜃 = 1 − 𝜎! ! /(𝑇𝜎! ! + 𝜎! ! ) , if 𝜃 is close to 0, the random effect
estimator is similar to pooled OLS. On the contrary, the random effect estimator is similar to fixed effect estimator.
In our random effect estimation, 𝜃 = 0.08.
18
We explain the effect of managerial quality in this section, because it is still validated by three estimators.
12
provision means that bank managers have realized some potential risks in the bank’s loan
portfolio. Then they have to increase the loss provision to cover these risks. This behavior is the
result of a decrease in loan portfolio quality that will cause losses to the bank in the future.
Secondly, the increase in loan loss provision is also a signal to the market. When the bank
manager announces a high loan loss provision, it is equivalent to telling its customers and market
investors that the portfolio quality of the bank is lowering and it will be proved by loan losses in
following periods. As soon as customers and investors receive this message, they are willing to
protect themselves by reducing their exposure to this bank rather than to stick with the bank. As a
result, the bank’s difficulties are self-fulfilling and aggravated by loss of confidence.
3.3.2. Size
Amongst portfolio strategy variables, size is the only variable that is significant to bank results in
our econometric study. This result indicates the negative effect of bank size on bank results and
stability. In fact, this effect is obvious. The rationale for the fact that one bank decides to increase
its size by holding more assets is the presence of economies of scale and market power. Therefore,
increased bank size allows large banks to make more profits. However, these banks
correspondingly have to assume more risks. Even if they can reduce risk with the help of
portfolio diversification or risk transfer techniques, they are still exposed to systemic risk that is
not diversifiable and could cause important losses to these large banks.
3.3.3. Interaction between liquidity and capitalization strategy
The insignificance of liquidity strategy variables and the lack of robust significance of
capitalization strategy show that the negative contribution of the increase in the risk level of a
single strategy (liquidity or capitalization) to the bank’s stability is limited.19 Then, regulation on
one single strategy is less efficient. That’s why many authors criticize the risk-based capital
regulation (see for example, Blum, 1999; Rime, 2001; Ayuso, Pérez, and Saurina, 2004). Some
authors emphasize the necessity of the introduction of risk-independent leverage restriction (e.g.
Blum, 2008). Nevertheless, our result suggests that banking regulation that only relies on
19
We consider LEV and RBCR are less significant, because only two estimators validate them. Particularly, the
unbiased model, the fixed effect estimator rejects their significance.
13
capitalization regulation is not sufficient to limit the impact of banks’ risk taking on their stability.
The significance of the interaction between liquidity and capitalization strategy is the most
important result we obtain in this empirical study. It shows that an increase in unstable-funding
sources relative to equity capital will negatively affect bank results, namely bank losses. As this
variable measures the interaction between liquidity and capitalization strategy of the bank, a high
unstable-funding sources to equity ratio (UFS_E ratio) can be interpreted as follows. This ratio is
obtained by dividing unstable-funding ratio (UFS ratio) by simple leverage ratio (equity-to-assets
ratio). These two ratios are proxy for banks’ liquidity strategy and capitalization (leverage)
strategy, respectively. Therefore, a high UFS_E ratio corresponds to the joint implementation of
risky liquidity strategy (high UFS ratio) and risky capitalization strategy (low equity-to-assets
ratio). As a result, the bank can be destabilized by the implementation of both risky liquidity
strategy and risky capitalization strategy by banks.
Why this interaction between two strategies explains bank results better than a separate strategy?
The rationale is that banks always try to bypass the regulation by using risky strategies that are
currently less regulated or unregulated. As we know, from the Basel I to Basel II the main spirit
of banking regulation is capital adequacy requirements, whatever it is risk-based or not.
According to regulators, if the bank takes more risks, it has to hold more equity capital that is
costly to the bank. However, financial innovations help banks to easily decrease its regulatory
charges. Indeed, through various ways, banks have transferred their risk to other banks, to other
financial institutions, even to the whole financial system. Amongst these ways, the most popular
one is the use of more short-term (unstable) funding sources instead of equity capital with the
help of securitization or the creation of off-balance sheet entities. Then, the bank is able to use
economizing on equity capital to invest in more risky activities. Consequently, the bank’s
instability not only stems from the lack of equity capital, but also from the lack of stable-funding
sources.
4. Further analysis on SPE
The lesson of subprime crisis motivates regulators and academics to rethink the future regulatory
reform in the banking system, regulatory tools as well as regulatory perimeter. Many authors
14
establish that the increase in the importance of banks’ off-balance sheet activities in recent years
have a detrimental impact on the bank’s stability. Most of them point out that the main
destabilizing factor of banks’ off-balance sheet activities is the close link between banks and the
shadow banking system they sponsor (e.g. Adrian and Shin, 2009; Gorton et al., 2010).
Especially in subprime crisis, Special Purpose Entities (SPEs) created by banks to securitize their
risky assets and to get short-term funding sources instead of equity capital should be partly
responsible for the liquidity problem of the bank and the decrease in stability of the whole
financial system (e.g. Shin, 2009; Brunnermeier, 2009).
Nevertheless, in our econometric estimation, off-balance sheet activities are not a significant
variable to bank results. That is to say, the increase in off-balance sheet activities will not
significantly raise the bank’s risk level, or cause losses to the bank. Intuitively, it seems
inconsistent with the arguments we mentioned above. The reason is that the proxy for this
variable we used in our study cannot include all off-balance sheet activities and especially their
impact.
As we introduced above, our proxy is off-balance sheet items reported in banks’ annual report.
The main components of these items are contingent liabilities and loan commitments. Boot and
Thakor (1991) establish that loan commitments may reduce banks’ portfolio risk. According to
their argument, the insignificance of our proxy variable could be explained by the
counterbalancing effect of loan commitments and other risky off-balance sheet activities on the
bank’s stability.
Besides, the proxy we used here neglects the effect of shadow banking system, especially SPEs
created by banks. In fact, before 2008, banks are not forced to disclose information about their
off-balance sheet entities.20 For this reason, in our econometric model, it is impossible to capture
the important impact of bank-sponsored SPEs on the bank’s stability, so that the estimation of the
effect of off-balance sheet variable is biased.
20
The Pillar III of Basel II about prudential information disclosure began to be reported by individual banks from
2008, and in this report one can find more details about banks’ off-balance sheet activities. European Central Bank
also paid attention to the impact of banks’ SPEs (the term used by ECB is Financial Vehicle Corporations, FVC) on
financial stability, so ECB has forced banks to report FVC statistics since 2010.
15
Even though we do not have enough information on SPEs created by our sampled banks, through
our following crude analysis we still try to provide some inspiration for further researches on the
negative contribution of bank-sponsored SPEs to the bank’s stability. This supplementary study
on SPEs is based on the list provided by Bank of England, which quarterly reports general
information on SPEs of which parent banks (23 banks) are installed in UK. The period we choose
to analyze SPEs is from 2005 to 2008 as well as our econometric sample.21 By focusing on these
SPEs’ parent banks, we try to analyze the impact of the creations and the type of securitization of
these SPEs on banks’ funding structure, namely the interaction between risky capitalization and
liquidity strategies (UFS/E). The dataset we used in this section to test this relationship is also a
panel data that contains 92 observations.
Table 6 shows that neither the number of SPE creation nor the type of securitization can
significantly affect banks’ funding structure. This result establishes that the issuance of SPEs will
not increase the riskiness of the joint implementation of liquidity and capitalization strategies. As
we mentioned above, without enough detailed information on these SPEs we cannot conclude
that SPEs created by banks do not have any negative impact on the bank’s stability. On the
contrary, our result affirms that the lack of sufficient detailed information on shadow banking
system constrains the measurement of banks’ liquidity risk. Therefore, we suggest both the
necessity of a qualitative aspect in liquidity regulation and the need for transparency in the
banking system.
5. Conclusion and policy implications
One result of this paper that the risky leverage strategy and the risky liquidity strategy alone are
not significant to the bank's losses justifies the idea of Ondo Ndong, Scialom, and Zhang (2010).
They provide empirical evidence for the argument that focusing only on the solvency regulation,
such as what regulators did for a long time, cannot reduce the bank's risk taking. By using the
neural network approach and panel data techniques, we find as one of main results in this paper
that the joint implementation of risky liquidity and leverage strategies has a negative effect can
21
23 banks domiciled in UK created 123 SPEs during 2005 and 2008. Indeed, because many banks in this sample
did not report detailed financial information before 2005, data availability force us to concentrate on this short fouryear period.
16
cause more losses to the bank.
As a consequence, the future regulation has to limit the joint use of these two types of risky
strategies. Technically, capitalization regulation should not be considered as the only efficient
regulatory approach to limit banks’ risk taking, and regulators have to pay attention to liquidity
regulation that is proved in our study as an efficient complementary tool to capitalization
regulation.
In addition, the lesson from SPEs evidence shows that only the quantitative method is not
sufficient to limit banks’ liquidity risk. The future liquidity regulation requires a qualitative
aspect as well as the quantitative aspect that has already drawn regulators’ attention (e.g. BIS,
2010, Basel III agreements on Core Liquidity Ratio and Net Stable Funding Ratio). The future
research on the modality of the implementation of liquidity regulation should focus on both
appropriate liquidity ratios of banks and qualitative liquidity requirements.
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Appendix
Table 1 Data sample description
Period: from 2000 to 2008
Number of banks: 25
Number of observations: 225
Code
Bank
Country
1
Bank of Scotland Plc
GB
2
ABN Amro Holding NV
NL
3
Banco Santander SA
ES
4
Lloyds TSB Bank Plc
GB
5
Dexia Crédit Local SA
FR
6
HSBC France
FR
7
Dexia Bank Belgium - Dexia Bank
BE
8
Crédit Industriel et Commercial - CIC
FR
9
Banco Bilbao Vizcaya Argentaria SA
ES
10
Allied Irish Banks plc
IE
11
Landesbank Berlin Holding AG - LBB Holding AG
DE
12
Nordea Bank Finland Plc
FI
13
Northern Rock (Asset Management) Plc
GB
14
Le Crédit Lyonnais (LCL)
FR
15
Anglo Irish Bank Corporation Limited
IE
16
Banco Espirito Santo SA
PT
17
Irish Life & Permanent Plc
IE
18
Bradford & Bingley Plc
GB
19
Crédit du Nord
FR
20
Caja Laboral Popular Coop. de Crédito
ES
21
Banco Cooperativo Espanol
ES
22
SkandiaBanken
SE
23
Banco Itau Europa S.A.
PT
24
Fionia Holding A/S
DK
25
Bankaktieselskabet Alm. Brand Bank
DK
22
Table 2 Variables description
Variable
Proxy
Code
Dependant: Bank results
Credit supply variation
VAR
Capitalization strategy
Leverage
Regulatory-capitalization
LEV
RBCR
Unstable funding ratio
UFS
Asset-side liquidity ratio
Portfolio structure
ASLR
BB
Size
SIZE
Off-balance sheet activities
Interbank activities
Asset quality
Management quality
ROE policy
OBS
BLBD
LLRR
MQ
ROE
Interaction between
liquidity and
capitalization strategy
Unstable funding to equity
ratio
UFS_E
Total short-term funding (customer deposits excluded)
to equity
Control variable
Output gap
Domestic interest rate
Financial sector importance
GAP
DIR
FSI
(Actual GDP - Potential GDP) / Potential GDP
Interest rate of domestic country
Aggregated MFI balance sheet to GDP
Liquidity strategy
Portfolio strategy
Management strategy
Description
Net loans variation from the last year to the present
year
Equity to total assets ratio
Total regulatory capital to risk-adjusted assets ratio
Total short-term funding (customer deposits excluded)
to total funding sources
Liquid assets to short-term funding sources
Banking book assets to total assets ratio
Total assets relative to total balance sheet of MFIs in
domestic country
Off-balance sheet items to total assets
Bank loans to bank deposits
Loan loss reserve ratio
Overheads to total assets
Returns on average equity
Table 3 Descriptive statistics
Variable
Obs
Mean
Std. Dev.
Min
Max
VAR
225
0.1348264
0.2012992
-0.7548397
1.184435
UFS
225
0.2682083
0.1814412
0.0025743
0.9368266
LEV
225
0.0490644
0.0286257
-0.0076543
0.1750997
RBCR
225
11.90089
2.984229
8.1
27.1
BB
225
0.8613856
0.1149114
0.3094596
0.9947096
OBS
225
0.2149386
1.182854
0
17.70003
LLRR
225
1.917156
1.857921
0
11.49
MQ
225
0.0154578
0.0096594
0.0017804
0.0683492
ROE
225
11.08236
18.19471
-178.93
45.54
BLBD
225
131.7282
144.5549
4.423764
923.1164
ASLR
225
41.3214
58.15818
1.268683
504.5263
SIZE
225
10.24307
18.26299
0.2479586
132.3618
UFSE
225
6.967914
8.99741
-59.59816
44.92315
GAP
225
2.255952
2.232136
-1.841209
9.003569
RIR
225
1.275949
1.456735
-1.526981
5.244417
FSI
225
3.519701
1.58301
0.9915413
9.420238
23
Table 4 Neural network results
Dependent variable: Credit supply variation (VAR)
Explanatory variables
UFS
LEV
RBCR
BB
OBS
LLRR
MQ
ROE
BLBD
ASLR
SIZE
UFS/E
GAP
RIR
FSI
DW
HQIF
RMSQ
R-squared
LWG TEST
NN model
0,0073
(0.4274)
0.1319
(1)
-0.2375***
(0)
0.0336
(0.9179)
-0.0232
(0.6042)
-0.1397***
(0)
-0.2478***
(0)
0.077
(0.8947)
0.0611
(0.3116)
-0.0009808
(0.8316)
0.0301
(0.5432)
-0.2579***
(0)
0.1161
(1)
-0.009
(0.88)
-0.0616***
(0)
1.947
550.2979
0.5384
0.2344
0.20%
The neural network model is bootstrapped 500 times to obtain the coefficient and the associated P-value (in
parenthesis) of each explanatory variable. *** denotes the statistical significance at the 1%. LWF test is the specific
test in neural network modeling in order to examine whether there exists a neglected nonlinearity. Our result, 0.003
for this test signifies that all nonlinearities are taken into account by our neural network.
24
Table 5 Pooled and panel data estimation
Dependent variable: DVAR (Pooled model); VAR (Random effect and fixed effect)
Explanatory variables
Pooled model
Random effect
Fixed effect
UFS
-0.420896*
0.1472384
-0.2141103
(-1.67)
(1.14)
(-1.2)
-5.674162**
1.101552
-0.5135375
(-3.01)
(1.59)
(-0.36)
0.0171186
-0.0225638***
-0.0112004
(1.4)
(-3.41)
(-1.3)
0.4774725
0.0911321
0.0975253
(1.61)
(0.74)
(0.51)
0.0048272
-0.0038071
0.0093081
(0.51)
(-0.35)
(0.81)
-0.0393883**
-0.0243568***
-0.0302584***
(-2.24)
(-2.79)
(-2.27)
-9.927754***
-6.228728***
-4.868252
(-2.67)
(-3.37)
(-1.64)
0.0023443*
0.000967
0.00063
(1.85)
(1.09)
(0.58)
-0.0000951
0.0001821
0.0002298
(-0.49)
(1.28)
(1.33)
0.0009762
-0.0001258
0.0003106
(1.53)
(-0.36)
(0.57)
0.008415***
0.000102
0.005432***
(2.84)
(0.14)
(2.64)
-0.0073298**
-0.0070943***
-0.0057592**
(-2.4)
(-2.93)
(-1.99)
0.0230974**
0.019799***
0.0179653*
(1.89)
(2.76)
(1.88)
0.0193723
-0.0012857
0.0011203
(1.41)
(-0.14)
(0.1)
LEV
RBCR
BB
OBS
LLRR
MQ
ROE
BLBD
ASLR
SIZE
UFS_E
GAP
RIR
25
FSI
-0.1082853***
-0.0148479
-0.0301881
(-2.66)
(-1.3)
(-1.59)
0.403229***
0.3966918*
(2.9)
(1.68)
CONSTANT
R-squared
0.29
0.2
0.13
Number of observations
200
225
225
Pooled model estimation proceeds with the first-difference estimator. This table reports the estimated coefficients
and associated T-statistics (in parenthesis, Z-statistics for random-effect model). *, **, ***, denote the statistical
significance at the 10%, 5% and 1%, respectively.
Table 6 Panel data estimation for SPEs
Dependent variable:
DUFS_E (Pooled model);
UFS_E (Ramdom effect and fixed effect)
Explanatory variables
Pooled model
Random effect
Fixed effect
NBI
0.0559794
(0.26)
0.0986963
(0.43)
0.0442004
(0.18)
TYPE
-0.1359412
(-0.16)
-1.823181*
(-1.8)
-1.40757
(-1.29)
5.549475***
(7.53)
5.441633***
(9.61)
CONSTANT
R-squared
0.001
0.03
0.03
Number of observations
69
92
92
NBI denotes the number of SPE issuance, and TYPE denotes the type of securitization of SPEs. Pooled model
estimation proceeds with the first-difference estimator. This table reports the estimated coefficients and associated Tstatistics (in parenthesis, Z-statistics for random-effect model). *, **, ***, denote the statistical significance at the
10%, 5% and 1%, respectively.
26