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Bank Liquidity Risk and Performance
Article in Review of Pacific Basin Financial Markets and Policies · March 2018
DOI: 10.1142/S0219091518500078
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Bank Liquidity Risk and Performance
Chung-Hua Shen
Department of Finance
National Taiwan University
TEL: (886) 2-33661087
FAX: (886) 2-83695817
E-mail: chshen01@ntu.edu.tw
Yi-Kai Chen *
Department of Finance
National University of Kaohsiung
TEL: (886) 7-5919501
FAX: (886) 7-5919329
E-mail: chen@nuk.edu.tw
Lan-Feng Kao
Department of Finance
National University of Kaohsiung
TEL: (886) 7-5919502
FAX: (886) 7-5919329
E-mail: lanfeng@nuk.edu.tw
Chuan-Yi Yeh
Department of Finance
National University of Kaohsiung
TEL: (886) 7-5919501
FAX: (886) 7-5919329
E-mail: james7449@yahoo.com.tw
June 2009
*
Corresponding Author
Bank Liquidity Risk and Performance
Abstract
This study is to employ alternative liquidity risk measures besides liquidity ratio, and
investigate the causes of liquidity risk (causes of liquidity risk model), using an unbalanced panel
dataset of 12 advanced economies commercial banks over the period 1994-2006. Thus, we apply
panel data instrumental variables regression, using two-stage least squares (2SLS) estimators to
estimate bank liquidity risk and performance model. We find that liquidity risk is the endogenous
determinant of bank performance. The causes of liquidity risk include components of liquid assets
and dependence on external funding, supervisory and regulatory factors and macroeconomic factors.
Besides, we also find that liquidity risk may lower bank profitability (return on average assets and
return on average equities) because of higher cost of fund, but increase bank’s net interest margins.
Besides, we classify countries as bank-based or market-based financial system. The result shows
that liquidity risk is negatively related to bank performance in market-based financial system.
However, it has no effect on bank performance in bank-based financial system.
Key Words: liquidity risk, fixed effects, performance, instrumental variables, financial system
1. Introduction
Since August 2007, the U.S subprime mortgage crisis has not only threatened to the U.S.
economy into a recession, but affected the global financial system. Furthermore, it brings a huge
challenge to short-term and long-term development for global banking industry. Because the crisis
has caused banks and other financial institutions became nervous about lending to other banks,
banks generally lack of liquidity following the subprime mortgage crisis.2 Especially, banks depend
heavily on the short-term money market or purchased funds market will be more likely to suffer
liquidity problem in the future, and the Northern Rock is an example.
After subprime mortgage crisis, Northern Rock was unable acquire funding from money
market because of credit freeze. 3 In September 2007, Northern Rock was influenced by magnitude
liquidity squeezes, and forced to a bailout from the Bank of England. It consequently suffered the
bank run crisis. From Northern Rock crisis we can realize the importance of bank liquidity and
diversified funding sources, though liquidity risk was rarely mentioned in the past. Swary (1986)
also provided an explanation of the failure of the Continental Illinois National Bank in the U.S,
which only had a small part of core deposit on its liability side. 4 Thus, it is worth of discussing for
bank liquidity risk.
According to the definition of the Basel Committee on Banking Supervision (1997), liquidity
risk arises from the inability of a bank to accommodate decreases in liabilities or to fund increases
in assets. When a bank has inadequate liquidity, it cannot obtain sufficient funds, either by
increasing liabilities or by converting assets promptly, at a reasonable cost, thereby affecting
profitability. Besides, Decker (2000) indicated that liquidity risk can be divided into funding
liquidity risk and market liquidity risk. 5
Comparing to credit risk, there are fewer literature to discuss with liquidity risk. Basel I
Accord (Basel Committee on Banking Supervision, 1988) set out regulatory standards for credit risk
2
Freixas et al (2000) showed that liquidity may dry up for a solvent bank in the interbank market if there is imperfect
information, or if there is market tension which reduces lending banks’ excess liquidity and reduces their scope to
diversify. The interbank market as a whole may face liquidity problems if each bank refuses to lend to others because it
cannot be confident of borrowing itself to meet its own liquidity shortages.
3
The funding sources of Northern Rock, one of the five largest British mortgage lenders, mostly relied on wholesale
money markets and securitization of mortgages instead of customer deposits. So it is extremely lack of stable funding
sources.
4
Continental Illinois National Bank relied heavily on large deposits from other domestic banks, foreign deposits, and on
interbank lines of credit for the funding of an undiversified loan asset portfolio. When Japanese banks became nervous
about the oil producing loan exposures of the bank, they withdrew their lines of credit. This caused other banks to do
the same and caused bank run. Consequently the bank was unable to fund its assets, and the bank eventually be taken
over by the Federal Deposit Insurance Corporation.
5
Funding liquidity risk is the risk that bank will be unable to meet its obligations as they come due because of inability
to liquidate assets or obtain adequate funding sources. However, market liquidity risk is that banks cannot easily unwind
or offset specific exposures without significantly lowering market prices because of inadequate market depth or market
disruptions.
3
and market risk. Besides, Basel II Accord (Basel Committee on Banking Supervision, 2004) even
takes operational risk into account. However, they seldom mention the liquidity risk. Landskroner
and Paroush (2008) also indicated that there has been extensive academic and regulatory discussion
of the different major banking risks: credit risk, market risk and even operation risk. However
relative little attention has been paid to liquidity risk that has become one of the major risks faced
by banks and other financial institutions in recent years.
Previously, the related literatures of liquidity risk mainly focus on bank run or failures. 6
Besides, previous empirical studies were mainly to investigate the determinants of bank profitability
or net interest margins. (e.g. Bourke, 1989; Molyneux and Thornton, 1992; Demirgüç-Kunt and
Huizinga, 1999; Shen et al., 2001; Barth et al., 2003; Demirgüç-Kunt et al., 2003; Kosmidou et al.,
2005; Athanasoglou et al., 2006; Pasiouras and Kosmidou, 2007; Athanasoglou et al., 2008;
Kosmidou, 2008; Naceur and Kandil, 2009 ). They usually use liquidity ratios to measure bank
liquidity, and regarded liquidity risk as exogenous variable. However, there are seldom studies to
discuss the causes of liquidity risk. Furthermore, previous empirical evidence showed that the effect
of liquidity risk on bank profitability is mixed. Some studies found out the positive effect (e.g.
Molyneux and Thornton, 1992; Barth et al., 2003); others found out the negative effect (e.g. Bourke,
1989; Demirgüç-Kunt and Huizinga, 1999; Kosmidou, 2005; Kosmidou, 2008). Besides, previous
studies found that banks with high liquidity have lower net interest margins. (e.g. Demirgüç-Kunt
and Huizinga, 1999; Shen et al., 2001; Demirgüç-Kunt et al., 2003; Naceur and Kandil, 2009).
Regulator have strict request to the bank in credit risk and operational risk in the past, but do
not focus on liquidity risk. However, we can found that liquidity risk will cause severe consequence
to banks following the subprime mortgage crisis. Besides, the credit crunch of 2007 reminded many
banks of the importance of liquidity risk (Matz, 2008). Thus, it is important for banks to strengthen
liquidity risk management, and liquidity risk will be an important issue in the future.
Generally, liquidity risk measures can be calculated from balance sheet positions. In the past,
better practices for liquidity risk measures focused on the use of liquidity ratios. However, Poorman
and Blake (2005) indicated that it was not enough to measure liquidity just using liquidity ratios and
it was not the solution. 7 Beyond mere liquidity ratios, banks must develop a new view of liquidity
measurement. Recently, there are many methods provided to assess bank liquidity risk besides
traditional liquidity ratios. 8 Therefore, the purpose of this study is to employ alternative liquidity
6
Diamond and Dybvig (1983) developed a model to explain why banks choose to issue deposits that are more liquid
than their assets .They specifically investigated bank liquidity and found out that lack of it may lead to a bank run. A
bank run is the sudden and unexpected increase in bank deposit withdrawals. Besides, the model has been widely used
to understand bank runs and other types of financial crises, as well as ways to prevent such crises.
7
A large regional bank, Southeast Bank, used over 30 liquidity ratios for liquidity measurement. However, it finally
failed due to liquidity risk.
8
Basel Committee on Banking Supervision (2000) proposed maturity laddering method for measuring liquidity risk.
Saunders and Cornett (2006) indicated that banks can use sources and uses of liquidity, peer group ratio comparisons,
liquidity index, financing gap and the financing requirement, and liquidity planning to measure their liquidity exposure.
Besides, Matz and Neu (2007) also indicated that banks can apply balance sheet liquidity analysis, cash capital position
and maturity mismatch approach to assess liquidity risk.
4
risk measures besides liquidity ratio. In our study, we use financing gap measures provided by
Saunders and Cornett (2006) to assess bank liquidity risk. In normal condition, banks seldom face
the liquidity crisis, and liquidity risk may vary with overall economic environment. Besides,
previous studies seldom focused on the causes of liquidity risk. Thus, another purpose of this study
is to investigate the causes of liquidity risk (causes of liquidity risk model), using an unbalanced
panel dataset of 12 advanced economies commercial banks over the period 1994-2006. We estimate
the causes of liquidity risk model through the fixed effects regression. In this model, we use each
bank’s financing gap ratio (FGAPR) as the dependent variable, and divide the causes of liquidity
risk into internal and external factors as independent variable.
The empirical results indicate that large banks have incentive to hold more loans thus have
larger financing gap ratio. However, over the limit point the effect of size becomes negative. Thus,
the effect of size on liquidity risk is non-linear. Banks with much less risky liquid assets and risky
liquid assets can reduce their liquidity risk. Besides, banks depend heavily on the external funding
face more severe liquidity problem. Thus banks should diversify their funding sources to reduce
liquidity risk. In regulation and supervision, we can find that countries with greater official power,
higher restrictiveness make their banks suffer less liquidity risk. However, we find no evidence that
regulatory empowerment of private monitoring of banks has significantly impact on liquidity risk.
Thus, we can find that direct government supervision and regulation of bank activities could reduce
bank liquidity risk. About macroeconomic environment, the results indicated that banks run down
their liquidity buffer in boom because they increase their loans but attract less customer deposits in
this period.
In addition, we further investigate the determinants of bank performance in terms of the
perspective of the bank liquidity risk (bank liquidity risk and performance model). Previous
studies regarded liquidity risk as exogenous determinant of bank performance. However, from the
causes of liquidity risk model, we can find that bank liquidity risk may affected by another factors.
In our study, we thus regard liquidity risk as an endogenous determinant of bank performance. We
apply panel data instrumental variables regression, using two stage least squares (2SLS) estimators
to estimate the determinants of bank performance model. In this model, we use return on average
assets (ROAA), return on average equities (ROAE) and net interest margins (NIM) as the
dependent variable. Besides, we divide the determinants of bank performance into internal and
external factors as independent variable.
We find that liquidity risk is the endogenous determinant of bank performance. The causes of
liquidity risk include components of liquid assets and dependence on external funding, supervisory
and regulatory factors and macroeconomic factors. Besides, we also find that liquidity risk may
lower bank profitability (ROAA and ROAE). Banks with larger gap lack stable and cheap fund, and
thus they have to use liquid assets or much external funding to meet the demand of fund, increase
bank’s cost of funding. It consequently decreases bank’s profitability. However, liquidity risk will
increase bank’s net interest margins. (NIM) It indicated that banks with high levels of illiquid assets
in loans may receive higher interest income.
5
The financing behavior is very different between bank-based and market-based financial
system. In our study, we classify countries as bank-based or market-based system, and investigate
the difference of causes of liquidity risk in different financial systems. The empirical results
indicated that the bank-specific variable has the same effect on bank liquidity risk in two financial
systems. About supervision and regulation, it provides that greater official power, higher activity
restrictiveness will diminish bank liquidity risk in market-based financial system. However, we find
that greater regulatory empowerment of private monitoring of banks will increase bank liquidity
risk in market-based financial system. Regarding macroeconomic environment, the results indicates
that economic boom make banks in market-based financial system run down their liquidity buffer,
but macroeconomic has no effect on bank liquidity risk in bank-based financial system. Besides, we
further investigate bank liquidity risk and performance in different financial systems. We find that
liquidity risk has different effects on bank performance in different financial systems. Liquidity risk
is negatively related to bank performance in market-based financial system; however, it has no
effect on bank performance in bank-based financial system. Finally, we check the robustness of our
results using alternative liquidity risk measures, net loans to customer and short term funding. We
find that the results are almost same as the model using financing gap to total assets ratio (FGAPR).
The contribution of this study is to use another liquidity risk measures besides to liquidity
ratio, and we are the first study to investigate the causes of liquidity risk. Besides, we find that
liquidity risk is an endogenous determinant of bank performance. In subsample analysis, we further
classify countries as bank-based or market-based system, and investigate the difference of causes of
liquidity risk in different financial systems. Besides, we further investigate the effect of liquidity
risk on bank performance in different financial systems.
The remainder of this study is organized as follows. Section 2 provides literature review.
Section 3 describes the sample selection and variable. Section 4 is econometric specification.
Section 5 presents the empirical results and Section 6 concludes the study.
2. Literature Review of Liquidity Risk Measures
In the past, better practices for liquidity risk measures focused on the use of liquidity ratios.
The ratios previous studies used include liquid assets to total assets ratio (e.g. Bourke, 1989;
Molyneux and Thornton, 1992; Barth et al., 2003; Demirgüç-Kunt et al., 2003), liquid assets to
deposits ratio (Shen et al., 2001) and liquid assets to customer and short term funding (Kosmidou et
al., 2005). The higher value of liquidity ratio makes bank more liquid and less vulnerable to failure.
Besides, some studies use loans to total assets ratio (e.g. Demirgüç-Kunt and Huizinga, 1999;
Athanasoglou et al., 2006), net loans to customer and short term funding ratio (e.g. Pasiouras and
Kosmidou, 2007; Kosmidou, 2008; Naceur and Kandil, 2009) to assess bank’s liquidity risk. The
higher the value of these ratios, the more liquidity risk the banks will suffer.
The liquidity ratios and the empirical results of the relationship between bank liquidity risk
6
and performance are shown in Table 1. We can find that the effect of liquidity risk on bank
profitability is mixed. Some studies found out the positive effect (e.g. Molyneux and Thornton,
1992; Barth et al., 2003); others found out the negative effect (e.g. Bourke, 1989; Demirgüç-Kunt
and Huizinga, 1999; Kosmidou, 2005; Kosmidou, 2008). Besides, previous studies found that
banks with high liquidity have lower net interest margins. (e.g. Demirgüç-Kunt and Huizinga, 1999;
Shen et al., 2001; Demirgüç-Kunt et al., 2003; Naceur and Kandil, 2009).
However, there are another ways can be used to assess bank liquidity risk besides traditional
liquidity ratios. Besides, they can be divided into quantitative measurement and qualitative
measurement. About quantitative measurement, Basel Committee on Banking Supervision (2000)
proposed maturity laddering method for measuring liquidity risk. Saunders and Cornett (2006)
indicated that banks can use sources and uses of liquidity, peer group ratio comparisons, liquidity
index, financing gap and the financing requirement, and liquidity planning to measure their liquidity
exposure. Besides, Matz and Neu (2007) also indicated that banks can apply balance sheet liquidity
analysis, cash capital position and maturity mismatch approach to assess liquidity risk. Moreover,
Matz and Neu (2007) indicated that qualitative assessment of liquidity risk is at least as important as
a quantitative measurement based on models. They provided some qualitative liquidity risk
measures besides quantitative measures.
3. Sample Selection and Variable Description
3.1. Sample Selection
In our study, we use annual bank level, market structure, supervisory and macroeconomic
data from 12 advanced economies (Australia, Canada, France, Germany, Italy, Japan, Luxembourg,
Netherlands, Switzerland, Taiwan, United Kingdom and United States) over the period 1994-2006. 9
The data was initially collected from 1993-2007, but was modified to include 1994-2006 due to
large amounts of missing data in 1993 and 2007. Besides, we focus on commercial bank and delete
the unavailable and incomplete observations. Finally, we yield an unbalanced panel data consisted
of 14360 observations. 10 Bank observations in each country and year are shown in Table 2. We can
find that the number of banks is decreasing because of merger and acquisition.
The data for calculation of bank-specific and market structure variables are available from
Bankscope database. 11 Besides, all variables available from Bankscope database are adjusted for
9
We choose advanced economies as our sample because of their transparent information, and well financial system.
According to the definition of World Economic Outlook published by IMF, there are 31 advanced economies. For data
completeness, we choose 12 countries.
10
The panel is unbalanced because it contains banks entering or dropping out of the market during the sample period
(e.g. due to mergers). However, unbalanced panels are more likely to be the norm in typical economic empirical settings
(Baltagi, 2005).
11
In our study, we use unconsolidated bank statements (Bankscope consolidation codes U1, U2) where such statements
are available. U* statements were used only if no other unconsolidated statements existed. If no unconsolidated
7
inflation. The data unit of each bank in a given year is million U.S dollars. Supervisory and
regulatory variables are available from Barth et al. (2004). 12 Macroeconomic variables are available
from International Monetary Fund’s (IMF) World Economic Outlook (WEO) Database.
3.2. Variable Description
3.2.1 Causes of Liquidity Risk
In this model, we consider liquidity risk measures, bank-specific variables, supervisory and
regulatory variables, and macroeconomic conditions. Table 3 provides a description of all variables
used in this study. Following Saunders and Cornett (2006), we measure liquidity risk by computing
bank’s financing gap. 13 In fact, extrapolating from recent liquidity events, Gatev and Strahan (2006)
indicated that retail liabilities are a more stable source of bank financing than wholesale funds. Thus,
financing gap is defined as the difference between a bank’s loans and customer deposits in our
study. 14 Besides, we divided financing gap by total assets to standardize, finally get the ratio of
financing gap to total assets (FGAPR). Banks with higher financing gap ratio must use its cash,
selling liquid assets and much external funding to fund this gap, and face larger liquidity risk.
3.2.1.1 Bank-specific Risk Causes
Bank-specific causes of liquidity risk include size, square of size, less risky liquid assets, risky
liquid assets, and external funding dependence. We use natural logarithm of bank’s total assets
(SIZE) to proxy size, and their square (SIZE2) to capture the non-linear relationship. Because of too
big to fail argument, large banks would benefit from an implicit guarantee, thus decrease their cost
of funding and allows them to invest in riskier assets (Iannotta et al., 2007). So we expect that large
banks usually hold more loans and thus have larger financing gap ratio. However, the largest banks
will face less liquidity risk because of too big to fail argument. Thus, the effect of size on bank
liquidity risk is non-linear.
statements were available, we used consolidated statements (C1, C2, C*). Banks with a consolidation status of A1 were
dropped.
12
Barth et al. (2004) conducted a survey of national regulatory agencies and obtained information on numerous bank
regulations and supervisory practices in 107 countries. However, they conduct pure cross-country regressions because
information on regulations and supervisory practices is available only for one point in time. They also indicated that
they were able to collect historical data for a few variables, however, and found very little change over time. Moreover,
controlling for any changes does not alter their findings. Besides, Barth et al. (2001) describe the survey questions and
data collection process in detail.
13
Saunders and Cornett (2006) indicated that banks can measure liquidity risk exposure by determining their financing
gap. Bank managers often regard the average core deposit as stable source of funds, thus it can permanently fund a
bank’s average loans. The financing gap is defined as the difference between a bank’s average loans and average core
deposits.
14
Saunders and Cornett (2007) indicated that core deposits are generally defined as demand deposits, negotiable order
of withdrawal (NOW) accounts, money market deposit accounts (MMDAs), other saving accounts, and retail
certificates of deposit (CDs). In our study, we use customer deposit to proxy core deposits.
8
The credit crunch of 2007 reminded many banks of the importance of liquidity risk
management. Although liquidity risk may cause bank failures, Davis (2008) indicates that banks
can protect against liquidity risk. On the asset side, it can be done by holding a significant
proportion of liquid assets. Cash can be used immediately to meet liquidity needs, while
government securities can be used readily as collateral. On the liability side, banks should ensure
enough diversified funding sources to reduce liquidity risk.
Because banks can sell or collateralize its liquid assets to obtain liquid funds, holding liquid
assets can reduce bank’s liquidity risk. However, banks may difficult to sell or collateralize their
liquid assets because of credit freeze. For this reason, we divided liquid assets into less risky liquid
assets and risky liquid assets. Besides, we divided less risky liquid assets and risky liquid assets by
total assets separately to standardize, finally get less risky liquid assets to total assets ratio (LRLA)
and risky liquid assets to total assets ratio (RLA). Banks can sell their less risky liquid assets such
as treasury bills with little price risk and low transaction cost, but they may difficult to collateralize
their risky liquid assets like trading securities because of credit freeze to get liquid funds. Thus we
expect that LRLA has negative and RLA has positive effect on the liquidity risk.
We use the ratio of external funding to total liabilities (EFD) to proxy the external funding
dependence. External funding are sum of money market funding and other funding. Banks rely on
the short-term money market rather than on core deposits to funds loans may face liquidity problem
in the future (Saunders and Cornett, 2006). The larger funds they need to borrow in the money
markets and the greater liquidity problems from such reliance they will face. Thus we expect that
EFD and bank’s liquidity risk have the positive relationship.
3.2.1.2 Supervisory and Regulatory Risk Causes
After subprime mortgage crisis we realized that government regulation and supervisory
practices are important for banking. We use official supervisory power index (OSP), private
monitoring index (PMI), and overall bank activities and ownership restrictiveness (BAR) to proxy
government regulation and supervisory practices.
The official supervisory power index aggregates information on whether bank supervisors can
take specific actions against bank management, bank owners, and bank auditors both in normal
times and times of distress, and larger number indicates greater power. Supervisory agencies can
use these powers to improve the governance of banks. The private monitoring index includes
information on the degree to which bank regulations force banks to disclose accurate information to
the public and induces private sector monitoring of banks. Besides, larger number indicates greater
regulatory empowerment of private monitoring of banks. The index of overall bank activities
restrictions measures the degree to which banks face regulatory restrictions on their activities in
securities markets, insurance, real-estate, and owning shares in non-financial firms. Besides, larger
number indicates higher restrictiveness.
9
In our study, we use interactive terms to examine the effects of supervisory and regulatory
variables. 15 The interactive terms include the interactions between annual percent change of GDP
and official supervisory power index (GDPC×OSP), interactions between annual percent change of
GDP and private monitoring index (GDPC×PMI), interactions between annual percent change of
GDP and overall bank activities and ownership restrictiveness (GDPC×BAR).
However, powerful government will ask their banks to increase liquidity hoard. Banks forced
to disclose accurate information to the public will increase their liquidity hoard. Francisco González
(2005) indicated that relaxing restrictions on banking activities may encourage bank risk-taking by
expanding a bank’s range of activities. In this situation, we expect that strict restrictiveness on bank
activities will make them decrease risk-taking and increase liquidity hoard. Yet relaxing restriction
may also increase opportunities for bank diversification, and thereby reduce risk-taking. In this
situation, strict restrictiveness on bank activities has the opposite effect.
3.2.1.3 Macroeconomic Risk Causes
In order to capture the effect of the macroeconomic environment, the two macroeconomic
variables used are annual percent change of GDP (GDPC) and annual percent change of inflation
(INF). Besides, we further add GDP annual percent change of last year (GDPCt-1) and inflation
annual percent change of last year (INFt-1) to capture the lagged effects. Aspachs et al. (2005)
indicated that banks hoard liquidity during periods of economic downturn, when lending
opportunities may not be as good and they run down liquidity buffers during economic expansions
when lending opportunities may have picked up. Thus we expect that higher economic growth
make banks run down their liquidity buffer and induce banks to lend more. However, banks will
attract less deposit during economic expansions, consequently increasing their financing gap.
3.2.2 Determinants of Bank Performance
In this model, we consider performance measures, liquidity risk measures, bank-specific
variables, market structure variables, supervisory and regulatory variables, and macroeconomic
conditions. This study use return on average assets (ROAA), return on average equities (ROAE)
and net interest margin (NIM) to evaluate bank performance. ROAA reflects the ability of a bank’s
management to generate profits from the bank’s assets. ROAE indicates the return to shareholders
on their equity. Average assets and equities are being used in order to capture any differences that
occurred in assets and equities during the fiscal year (or season effects). NIM measures the gap
between what the bank pays savers and what the bank receives from borrowers. Thus, NIM focuses
on the traditional borrowing and lending operations of the bank.
15
Barth et al. (2004) conducted pure cross-country regressions because information on regulations and supervisory
practices is available only for one point in time. In our study, we finish our model estimator by using interactive terms.
10
3.2.2.1 Bank-specific Performance Determinants
In our study, we use the ratio of financing gap to total assets (FGAPR) to proxy liquidity risk,
and we regard liquidity risk as an endogenous determinant of bank performance. Banks with higher
financing gap ratio must use its cash, selling liquid assets and much external funding to fund this
gap. It consequently increases their cost of funding and reduces profitability. However,
Demirgüç-Kunt et al. (2003) indicated that banks with high levels of liquid assets in cash and
government securities may receive lower interest income than banks with less liquid assets. If the
market for deposits is reasonably competitive, then greater liquidity will tend to be negatively
associated with interest margin. Thus, the proportion of liquid assets increases will decrease bank
liquidity risk, leading to a lower liquidity risk premium of the net interest margin (Angbazo, 1997;
Shen et al., 2001; Drakos, 2003). Thus, we expect that FGAPR has negative relationship with
ROAA and ROAE and positive relationship with NIM.
We also consider another factors affect bank performance besides liquidity risk. Besides, we
divide these factors into bank-specific factors, market structure factors, supervisory and regulatory
factors, and macroeconomic conditions. Bank-specific determinants of performance include size,
square of size, capital, and credit risk. Bank size is generally used to measure economies or
diseconomies of scale in the banking industry. The cost differences may cause a positive
relationship between size and bank performance, if there are significant economies of scale (Bourke,
1989; Molyneux and Thornton, 1992; Goddard et al., 2004). In addition, as Short (1979) argues,
size is closely related to the capital adequacy of a bank since relatively large banks tend to raise less
expensive capital and, hence, appear more profitable. In previous studies, some studies have found
scale economies for large banks (e.g. Berger and Humphrey, 1997; Altunbaş et al., 2001;
Athanasoglou et al., 2006; Kosmidou, 2008) while others have found diseconomies for larger banks
(e.g. Kosmidou et al., 2005; Pasiouras and Kosmidou, 2007). However, Eichengreen and Gibson
(2001) indicated that the effect of a growing bank’s size on profitability may be positive up to a
certain limit. Beyond this point the effect of size could be negative due to bureaucratic. Thus, the
relationship may be expected to be non-linear. As previous studies, we use natural logarithm of
bank’s total assets (SIZE) to proxy size, and their square (SIZE2) to capture the non-linear
relationship.
We use the ratio of equity to assets (ETA) to proxy the capital strength. Banks with high
capital-asset ratios are considered relatively safer in the event of loss or liquidation. Besides,
increase in capital may raise expected earnings by reducing the expected costs of financial distress
(Berger, 1995). The lower risk increases banks creditworthiness and consequently reduces the cost
of funding. Previous studies that use capital ratios as an explanatory variable of bank profitability
found a positive relationship (e.g. Demirgüç-Kunt and Huizinga, 1999; Barth et al., 2003;
Kosmidou et al., 2005). Thus, banks with higher equity to assets ratio will have lower needs of
external funding and therefore higher profitability.
The loan loss provisions to loans ratio (LLPL) is used to proxy the credit risk. Changes in
11
credit risk may reflect changes in the health of the bank’s loan portfolio (Cooper et al., 2003), which
may affect bank performance. Besides, Miller and Noulas (1997) indicated that the more financial
institutions are exposed to high-risk loans, the higher the accumulation of unpaid loans and the
lower the profitability. However, riskier loans should produce higher interest income. Maudos and
Fernández de Guevara (2004) indicated that the risk of non-repayment or default on a credit (credit
risk) requires the bank to apply a risk premium implicitly in the interest rates charged for the
operation. Banks that assume greater credit risk present higher interest margins. Doliente (2005)
also indicated that the impact of credit risk may reflect the additional risk premium charged by
banks for the financial costs of forgone interest revenue. Thus, we expect that LLPL has negative
relationship with ROAA and ROAE and positive relationship with NIM.
3.2.2.2 Market Structure Performance Determinants
Regarding market structure variables, we use three-bank concentration ratio (CON), CR3.
CON is calculated as the total assets held by the three largest commercial banks divided by the total
assets of all commercial banks in each country. Besieds, the higher the value is, the lesser
competition they have. According to the structure-conduct-performance (SCP) hypothesis, banks in
highly concentrated markets tend to collude and thus earn monopoly profits (Short, 1979; Gilbert,
1984; Molyneux et al., 1996). 16
3.2.2.3 Supervisory and Regulatory Performance Determinants
We use official supervisory power index (OSP), private monitoring index (PMI), and overall
bank activities and ownership restrictiveness (BAR) to proxy government regulation and
supervisory practices. In our study, we use interactive terms to examine the effects of supervisory
and regulatory variables. The interactive terms include the interactions between annual percent
change of GDP and official supervisory power index (GDPC×OSP), interactions between annual
percent change of GDP and private monitoring index (GDPC×PMI), interactions between annual
percent change of GDP and overall bank activities and ownership restrictiveness (GDPC×BAR).
Barth et al. (2004) indicated that strong supervision can help prevent banks from engaging in
excessive risk-taking behavior and thus improve bank development, performance and stability.
However, powerful supervisors may use their powers to benefit favored constituents, attract
campaign donations, and extract bribes (Djankov et al., 2002; Quintyn and Taylor, 2002). Under
these circumstances, powerful supervision will be positively related to corruption and will not
improve bank development, performance and stability.
Barth et al. (2003) indicated that it is possible that the wider the range of activities the greater
16
Previous studies indicated that collusion may cause higher interest rates spread (higher interest rates being charged on
loans and less interest rates being paid on deposits) and higher fees being charged (e.g. Goldberg and Rai, 1996 ;
Goddard et al., 2001).
12
will be profit opportunities for banks. However, banks may systematically fail to manage well a
diverse set of financial activities beyond traditional banking, and hence profitability would be
lower.
3.2.2.4 Macroeconomic Performance Determinants
In order to capture the effect of the macroeconomic environment, the two macroeconomic
variables used are annual percent change of GDP (GDPC) and annual percent change of inflation
(INF). Besides, we further add GDP annual percent change of last year (GDPCt-1) and inflation
annual percent change of last year (INFt-1) to capture the lagged effects. GDP is a measure of total
economic activity within an economy. Higher economic growth encourages banks to lend more and
permits them to charge higher margins, and improving the quality of their assets. Previous studies
found that economic growth has positive effect on bank’s performance (e.g. Kosmidou et al., 2005;
Pasiouras and Kosmidou, 2007; Athanasoglou et al., 2008; Kosmidou, 2008). Thus, GDPC and
GDPCt-1 are expected to have a positive impact on bank performance.
The relationship between inflation and performance is ambiguous. Perry (1992) indicated that
the relationship between inflation and performance depends on whether inflation expectations are
fully anticipated. An inflation rate fully anticipated by the bank’s management implies that banks
can appropriately adjust interest rates to increase their revenues faster than their costs and thus
acquire higher economic profits. If inflation is unanticipated, banks may be slow in adjusting their
interest rates. It results in a faster increase of bank costs than bank revenues that consequently has a
negative impact on bank profitability. Most studies found a positive relationship between inflation
and bank profitability (e.g. Bourke, 1989; Molyneux and Thornton, 1992; Kosmidou et al., 2005;
Athanasoglou et al., 2006; Pasiouras and Kosmidou, 2007; Athanasoglou et al., 2008). However,
Kosmidou (2008) found a negative relationship. Besides, Huybens and Smith (1999) develop a
theoretical model in which interest margins tend to rise in the presence of inflation. Empirical
studies found that inflation has positive effect on bank’s NIM (e.g. Demirgüç-Kunt and Huizinga,
1999; Kosmidou et al., 2005). The descriptive statistics of the variables we used are shown in Table
4.
4. Econometric Specification
4.1. Causes of Liquidity Risk Model
We use panel unit root tests to check the stationary of data before regression analysis. We test
the stationary using Maddala and Wu (1999) test. 17 The null hypothesis of nonstationary is rejected
at the 1% level for all variables.
17
Maddala and Wu (1999) suggest the use of the Fisher test, which is based on combining the p-values of the
test-statistic for a unit root in each bank. They state that not only does this test perform better than other tests for unit
roots in panel data, but it also has the advantage that it does not require a balanced panel, as most tests do.
13
This model provides an economic analysis of the causes of liquidity risk. Besides, we divide
the causes of liquidity risk into internal and external factors. In order to examine the relationship
between liquidity risk and the bank-specific, supervisory and macroeconomic variables, the panel
fixed effect regression model has been developed:
B
S
M
Lit = c i + ∑ λb Π + ∑ δ s Π + ∑ γ m Π mjt +ε it
b =1
b
it
s =1
s
jt
m =1
(1)
where Lit is liquidity risk of ith bank at time t, with i = 1,…, N, t = 1,…,T. In our study, it is the
financing gap ratio (FGAPR) and the ratio of net loans to customer and short term funding (NLCS).
Π bit , Π sjt , Π mjt are bank-specific, supervisory and macroeconomic variables with b = 1,…, B, s = 1,…,
S, m = 1,…, M, respectively. j refers to the country in which bank i operates; c is a constant term; εit
is the error term.
Extending equation (1) to reflect the variables, as described in Table 3, the model is
formulated as follows:
Lit = ci + λ1 SIZEit + λ2 SIZEit2 + λ3 LRLAit + λ4 RLAit + λ5 EFDit
+δ1GDPC jt × OSPjt + δ 2 GDPC jt × PMI jt + δ3 GDPC jt × BAR jt
+γ1GDPC jt + γ 2 GDPC jt -1 + γ3 INF jt + γ 4 INF jt -1 + ε it
(2)
Bank-specific variables include size (SIZE), square of size (SIZE2), less risky liquid assets
(LRLA), risky liquid assets (RLA) and external funding dependence (EFD). Supervisory and
regulatory variables include the interactions between change of GDP and official supervisory power
index (GDPC×OSP), interactions between change of GDP and private monitoring index
(GDPC×PMI), interactions between change of GDP and overall bank activities and ownership
restrictiveness (GDPC×BAR). Macroeconomic variables include change of GDP (GDPC), GDP
change of last year (GDPCt-1), change of inflation (INF) and inflation change of last year (INFt-1).
Equation (2) is estimated through fixed effects regression taking each bank’s FGAPR as the
dependent variable. We have been tested with the Hausman test and reject the null hypothesis of
random effects is suitable model. Thus, we use fixed effects rather than random effects model.
4.2. Determinants of Bank Performance Model
This model provides an economic analysis of the relationship between bank liquidity risk and
performance. Besides, from the causes of liquidity risk model, we can realize that there are many
factors may affect bank liquidity risk. In our study, we thus regard liquidity risk as an endogenous
determinant of bank performance, and apply panel data instrumental variables regression to
14
estimate this model. 18 In the previous studies, determinants of bank profitability were usually
divided into internal and external determinants. In order to examine the relationship between bank
liquidity risk and performance, the panel instrumental variables regression model has been
developed:
B
Pit = c + β l Lit +
K
S
M
∑
∑
∑
∑η Χ +ε
b =1
j =1
s =1
m =1
θ b Χ bit +
ω k Χ kjt +
ν s Χ sjt +
m
m
jt
it
(3)
the reduced form equation for Lit is
B
Lit = c +
∑
b =1
S
λb Π bit +
M
∑
∑ γ Π +ε
s =1
m =1
δ s Π sjt +
m
m
jt
(4)
it
where Pit is bank performance of ith bank at time t, with i = 1,…, N, t = 1,…,T. In our study, it is
return on average assets (ROAA), return on average equities (ROAE) and net interest margins
(NIM). Χ bit , Χ itk , Χ sjt , Χ mjt are bank-specific, market structure, supervisory and macroeconomic
variables with b = 1,…, B, k = 1,…, K, s = 1,…, S, m = 1,…, M, respectively. j refers to the country
in which bank i operates. Lit is liquidity risk, and regarded as endogenous variable. Π bit , Π sjt , Π mjt
are causes of liquidity risk and called instrumental variables. c is a constant term; εit is the error
term.
Extending equation (3) and (4) to reflect the variables, the model is formulated as follows:
Pit = c + β l Lit + θ1 SIZE it + θ 2 SIZE it2 + θ 3 ETAit + θ 4 LLPL it + ω1CON jt
+ν1GDPC jt × OSP jt + ν 2 GDPC jt × PMI jt + ν 3GDPC jt × BAR jt
+ η GDPC jt + η2 GDPC jt -1 + η3 INF jt + η4 INF jt -1 + ε it
the reduced1 form equation
for Lit is
(5)
Lit = c + λ1 SIZEit + λ2 SIZEit2 + λ3 LRLAit + λ4 RLAit + λ5 EFDit
+δ1GDPC jt × OSPjt + δ2 GDPC jt × PMI jt + δ3GDPC jt × BAR jt
+γ1GDPC jt + γ 2 GDPC jt -1 + γ3 INF jt + γ 4 INF jt -1 + ε it
(6)
Bank-specific variables include liquidity risk (LRGAP), size (SIZE), square of size (SIZE2),
capital (ETA) and credit risk (LLPL). Besides, liquidity risk is an endogenous variable. Market
18
The ordinary least squares estimator will cause biased. However, instrumental variables regression provides a way to
obtain consistent parameter estimates (Dunning, 2008).
15
structure variable is three-bank concentration ratio (CON). Supervisory and regulatory variables
include the interactions between change of GDP and official supervisory power index
(GDPC×OSP), interactions between change of GDP and private monitoring index (GDPC×PMI),
interactions between change of GDP and overall bank activities and ownership restrictiveness
(GDPC×BAR). Macroeconomic variables include change of GDP (GDPC), GDP change of last
year (GDPCt-1), change of inflation (INF) and inflation change of last year (INFt-1).
We have more instrumental variables than endogenous variables. Therefore, the endogenous
variables are over-identified. Equation (5) and (6) are estimated through two stage least squares
(2SLS) estimator taking each bank’s ROAA, ROAE and NIM as the dependent variable.
4.3. Subsample Analysis
There are large differences in financial systems across countries. Demirgüç-Kunt and Levine
(1999) constructed conglomerate index of financial structure, producing two categories of countries:
bank-based and market-based. 19 It expresses the relative development of bank and stock markets in
an economy, and is captured by the relative reliance on bank and stock market finance in the
economy. Thus, the financing behavior is very different between bank-based and market-based
financial system. Besides, most studies mainly analyze the impact of financial structure on
firm-financing behavior (e.g. Demirgüç-Kunt and Maksimovic, 2002; Schmukler and Vesperoni,
2004) or economic growth (e.g. Beck et al., 2000; Levine, 2002). However, Demirgüç-Kunt and
Huizinga (2000) focus on the performance of the banking sector itself across different systems.
Besides, their results indicate that once control for the level of financial development, there is no
significant difference in bank profits or margins between bank-based and market-based systems.
In subsample analysis, we classify countries as bank-based or market-based system, and
investigate the causes of liquidity risk in different financial systems. 20 Besides, we use dummy
variable (MB), and MB takes the value of one if the countries are classified as market-based system
and takes the value of zero if they are classified as bank-based system to examine the relationship
between financial system and bank performance. Finally, we investigate the relationship between
bank liquidity risk and performance in different financial systems.
19
In bank-based financial systems, it is greater reliance on bank finance, and banks play a leading role in mobilizing
savings, allocating capital, overseeing the investment decisions of corporate managers, and in providing risk
management vehicles. In market-based financial, it is greater reliance on stock market finance, and securities markets
share center stage with banks in terms of getting society’s savings to firms, exerting corporate control, and easing risk
management.
20
Bank-based countries include France, Germany and Italy. Market-based countries include Australia, Canada, Japan,
Luxembourg, Netherlands, Switzerland, Taiwan, United Kingdom and United States.
16
5. Empirical Results
5.1. Regression Results
Table 5 reports the empirical results of the causes of liquidity risk model using FGAPR to
measure liquidity risk. About bank-specific variable, the relationship between size (SIZE) and
liquidity risk is significantly positive, while the square of size (SIZE2) and liquidity risk is
significantly negative. This provides that large banks believe too big to fail argument. Thus they
have incentive to increase risk-taking and hold more loans and consequently have larger financing
gap ratio. However, over limit point the effect of size becomes negative. Thus, the effect of size on
liquidity risk is non-linear. We find that both the less risky liquid assets to total assets ratio (LRLA)
and risky liquid assets to total assets ratio (RLA) are significantly negative related to liquidity risk.
The results indicated that banks can reduce their liquidity risk by holding much liquid assets.
However, external funding dependence (EFD) has the positive effect on bank’s liquidity risk. This
provides that banks heavily depend on the external funding face larger liquidity problem. Thus
banks can diversify their funding sources to reduce liquidity risk.
Turning to supervision and regulation, we find that the interactions between annual percent
change of GDP and official supervisory power index (GDPC×OSP), interactions between annual
percent change of GDP and overall bank activities and ownership restrictiveness (GDPC×BAR)
have significantly negative effect on bank’s liquidity risk. The results indicate that greater official
power, higher restrictiveness will diminish the positive effect of GDPC. It provides that powerful
government will ask their banks to increase liquidity hoard. Strict restrictiveness on bank activities
will make them decrease risk-taking and increase liquidity hoard. However, the interactions
between annual percent change of GDP and private monitoring index (GDPC×PMI) have no effect
on bank’s liquidity risk significantly. Thus, we can find that direct government supervision and
regulation of bank activities could reduce bank liquidity risk.
Regarding macroeconomic environment, we find that both annual percent change of GDP
(GDPC) and GDP annual percent change of last year (GDPCt-1) have positive effect on bank’s
liquidity risk. This provides that higher economic growth of current year and last year make banks
run down their liquidity buffer and induce them to lend more. However, higher economic growth of
current year and last year make banks attract less customer deposits, thus increasing their financing
gap. Besides, annual percent change of inflation (INF) and inflation annual percent change of last
year (INFt-1) have significantly positive correlation with bank’s liquidity risk.
Table 6 reports the empirical results of bank liquidity risk and performance model using
FGAPR to measure liquidity risk. In panel A of Table 6, we use ROAA to evaluate bank
performance. We find that liquidity risk (FGAPR) is negatively and significantly related to bank
performance. It indicated that banks with larger gap lack stable and cheap funds, and thus they have
to use liquid assets or much external funding to meet the demand of fund. As borrowings rise,
lenders in money market may be concerned about bank’s creditworthiness. They may impose higher
17
risk premiums on borrowed funds, and thus increase bank’s cost of funding. It consequently
decreases bank performance.
About bank-specific variables, we can find that the relationship between size (SIZE) and bank
performance is significantly positive, while the square of size (SIZE2) and bank performance is
significantly negative. This provides evidence for the economies of scale theory. It is consistent
with previous study (e.g. Berger and Humphrey, 1997; Altunbaş et al., 2001; Athanasoglou et al.,
2006; Kosmidou, 2008). However, over the optimum point the effect of size becomes negative due
to bureaucratic. Thus, the effect of size on bank performance is non-linear. We also find that capital
(ETA) has the positive effect on bank performance. Banks with sound capital position have more
time and flexibility to deal with problems because of unexpected losses. Besides, well capitalized
banks face lower costs of going bankrupt, thus reduced cost of funding or less need for external
funding, and therefore increase their performance. Our finding is consistent with previous study (e.g.
Demirgüç-Kunt and Huizinga, 1999; Barth et al., 2003; Kosmidou et al., 2005; Athanasoglou et al.,
2006; Pasiouras and Kosmidou, 2007; Iannotta et al., 2007; Athanasoglou et al., 2008; Kosmidou,
2008). However, credit risk (LLPL) has the negative effect on bank performance, showing that
banks should focus on credit risk management. This finding is consistent with previous study (e.g.
Athanasoglou et al., 2006; Athanasoglou et al., 2008).
About market structure, concentration ratio (CON) shows a significantly positive correlation
with bank performance, which is consistent with the structure-conduct-performance (SCP)
hypothesis. This finding is consistent with previous study (e.g. Bourke, 1989; Molyneux and
Thornton, 1992; Lloyd-Williams et al., 1994; Demirgüç-Kunt and Huizinga, 1999; Kosmidou et al.,
2005; Pasiouras and Kosmidou, 2007).
Turning to supervision and regulation, we find that the interactions between annual percent
change of GDP and official supervisory power index (GDPC×OSP), interactions between annual
percent change of GDP and private monitoring index (GDPC×PMI), interactions between annual
percent change of GDP and overall bank activities and ownership restrictiveness (GDPC×BAR)
have significantly positive effect on bank performance. The results indicate that greater official
power, greater regulatory empowerment of private monitoring of banks, higher restrictiveness can
increase the positive effect of GDPC.
Regarding macroeconomic environment, we find that both annual percent change of GDP
(GDPC) and GDP annual percent change of last year (GDPCt-1) have positive effect on bank
performance. It indicates that higher economic growth of current year and last year have
significantly positive effect on bank performance. The results provide evidence that higher
economic growth encourages banks to lend more and permits them to charge higher margins, and
improving the quality of their assets, consequently increasing their profitability. Previous studies
also find that economic boom has positive effect on bank profitability (e.g. Kosmidou et al., 2005;
Pasiouras and Kosmidou, 2007; Athanasoglou et al., 2008; Kosmidou, 2008). Besides, inflation
annual percent change of last year (INFt-1) has positive correlation with bank performance. The
18
positive relationship indicated that inflation is anticipated by the inflation change of last year, thus
give banks the opportunity to adjust interest rates accordingly, and consequently increase their
performance. However, this positive effect is weak.
In panel B of Table 6, we use ROAE to evaluate bank performance. We find that almost all of
the results are same as ROAA model, except for INFt-1. Inflation annual percent change of last year
(INFt-1) has positive correlation with bank performance. Besides, this positive effect is strong
significantly.
In panel C of Table 6, we use NIM to evaluate bank performance. We find that most of the
results are same as ROAA model, but some are not. About bank-specific variables, we find that
liquidity risk (FGAPR) is positively and significantly related to NIM. It indicated that banks with
high levels of illiquid assets in loans may receive higher interest income than banks with less
illiquid assets. This finding is consistent with previous study (Naceur and Kandil, 2009). However,
we can’t find the evidence for the economies of scale theory. Besides, credit risk (LLPL) has the
positive effect on NIM. It provides that credit risk requires banks to apply a risk premium implicitly
in the interest rates charge. This is consistent with previous study (Maudos and Fernández de
Guevara, 2004; Iannotta et al., 2007; Santiago Carbó Valverde and Francisco Rodríguez Fernández,
2007; Maudos and Solís, 2009).
About market structure, concentration (CON) shows a significantly negative correlation with
bank performance, thus we can’t find the evidence to support the structure-conduct-performance
(SCP) hypothesis. We infer that banks operate in high concentration environment will decrease their
NIM because of high competition. Besides, interest rate decreases in recent years. Maybe it leads
the interest rate spread to decreases and thus decreases their NIM.
Regarding macroeconomic environment, we find that both annual percent change of inflation
(INF) and inflation annual percent change of last year (INFt-1) have positive effect on NIM. The
positive relationship indicated that inflation is anticipated, thus give banks the opportunity to adjust
interest rates accordingly, and consequently increase their NIM. This finding is consistent with
previous study (Huybens and Smith, 1999).
5.2. Regression Results in Different Financial Systems
The financing behavior is very different between bank-based and market-based financial
system. In subsample analysis, we classify countries as bank-based or market-based system, and
investigate the causes of liquidity risk in different financial systems. Table 7 reports the results of
causes of liquidity risk in different financial systems. Panel A of Table 7 shows the results of
market-based financial system, and Panel B shows the results of bank-based financial system.
Compared the results of two financial systems, the bank-specific variable has the same effect on
bank liquidity risk in two financial systems.
About supervision and regulation, it provides that greater official power, higher activity
19
restrictiveness will diminish bank liquidity risk in market-based financial system. However, we find
that greater regulatory empowerment of private monitoring of banks will increase bank liquidity
risk in bank-based financial system. Regarding macroeconomic environment, the results indicates
that economic boom of current year and last year make banks in market-based financial system run
down their liquidity buffer. However, we find that the macroeconomic condition has no effect on
bank liquidity risk in bank-based financial system. Because banks play key role in financing, they
don’t need to raise their funds on financial market, which was deeply affected by macroeconomic
condition. Thus, macroeconomic condition has no effect on bank liquidity risk in bank-based
financial system.
We also investigate the effect of financial system on bank performance. Table 8 reports the
results of the relationship between financial system and bank performance using FGAPR to
measure liquidity risk. Panel A shows the results using ROAA to evaluate bank performance. Panel
B shows the results using ROAE to evaluate bank performance. Panel C shows the results using
NIM to evaluate bank performance.
The empirical results show that market-based system has the positive effect on bank
performance even we use different performance measures (ROAA, ROAE and NIM). This
indicated that stock market development may improve bank performance, for example, as stock
markets generate information about firms that is also useful to banks. Besieds, stock market
development allows firms to be better-capitalized, thus reducing risks of loan default, consequently
increasing bank performance. Besides, at a higher level of stock market development, much
information about publicly traded firms is available that also enables banks to better evaluate credit
risk.
Besides, we further investigate liquidity risk effect on bank performance in different financial
systems. Table 9 shows the results of bank liquidity risk and performance in different financial
systems using ROAA as dependent variable. Panel A of Table 9 shows the results of market-based
financial system, and Panel B shows the results of bank-based financial system. In Panel A of Table
9, we find that liquidity risk is negatively related to bank performance in market-based financial
system. It indicated that banks in market-based financial system have to use liquid assets or much
external funding to meet the demand of fund. They need to raise funds in financial market, and thus
increase their cost of funding. It consequently decreases their performance. In Panel B of Table 9,
the results show that liquidity risk has no effect on bank performance in bank-based financial
system. In bank-based financial system, banks play key role in financing and thus they don’t
affected by liquidity risk. We find weak evidence to support the structure-conduct-performance
(SCP) hypothesis. Besides, supervision and regulation has no effect on bank performance.
Table 10 shows the results of bank liquidity risk and performance in different financial
systems using ROAE as dependent variable. Panel A of Table 10 shows the results of market-based
financial system, and Panel B shows the results of bank-based financial system. From Table 10, we
find that almost all results are same as the Table 12 using ROAA as dependent variable. Liquidity
20
risk is negatively related to bank performance in market-based financial system, and has no effect
on bank performance in bank-based financial system.
Table 11 shows the results of bank liquidity risk and performance in different financial
systems using NIM as dependent variable. Panel A of Table 11 shows the results of market-based
financial system, and Panel B shows the results of bank-based financial system. From Table 11, we
find that liquidity risk is positively related to NIM in two financial systems. It indicated that banks
with high levels of illiquid assets in loans may receive higher interest income in two financial
systems.
5.3. Robust Test
We check the robustness of our results using alternative liquidity risk measures. In this section,
we use net loans to customer and short term funding ratio (NLCS) to reexamine two models (causes
of liquidity risk and bank liquidity risk and performance model). The results indicate that most
results are same as the model using financing gap ratio (FGAPR) to measure liquidity risk.
6. Conclusions
This study investigates the causes of liquidity risk and the relationship between bank liquidity
risk and performance for 12 advanced economies over the period 1994-2006. In the causes of
liquidity risk model, we divide the causes of liquidity risk into bank-specific, supervisory and
macroeconomic factors. Besides, the model is estimated through fixed effects regression. In the
bank liquidity risk and performance model, we regard liquidity risk as an endogenous determinant
of bank performance, and apply panel data instrumental variables regression to estimate this model.
We also consider another factors affect bank performance besides liquidity risk. Besides, we divide
these factors into bank-specific factors, market structure factors, supervisory factors, and
macroeconomic conditions.
We find that liquidity risk is the endogenous determinant of bank performance. The causes of
liquidity risk include components of liquid assets and dependence on external funding, supervisory
and regulatory factors and macroeconomic factors. Besides, we also find that liquidity risk may
lower bank profitability (ROAA and ROAE). Banks with larger gap lack stable and cheap fund, and
thus they have to use liquid assets or much external funding to meet the demand of fund, increase
bank’s cost of funding. It consequently decreases bank’s profitability. However, liquidity risk will
increase bank’s net interest margins. (NIM) It indicated that banks with high levels of illiquid assets
in loans may receive higher interest income.
The financing behavior is very different between bank-based and market-based financial
system. In our study, we classify countries as bank-based or market-based system, and investigate
the difference of causes of liquidity risk in different financial systems. The empirical results
indicated that the bank-specific variable has the same effect on bank liquidity risk in two financial
21
systems. About supervision and regulation, it provides that greater official power, higher activity
restrictiveness will diminish bank liquidity risk in market-based financial system. However, we find
that greater regulatory empowerment of private monitoring of banks will increase bank liquidity
risk in market-based financial system. Regarding macroeconomic environment, the results indicates
that economic boom make banks in market-based financial system run down their liquidity buffer,
but macroeconomic has no effect on bank liquidity risk in bank-based financial system. Besides, we
further investigate bank liquidity risk and performance in different financial systems. We find that
liquidity risk has different effects on bank performance in different financial systems. Liquidity risk
is negatively related to bank performance in market-based financial system; however, it has no
effect on bank performance in bank-based financial system. Finally, we check the robustness of our
results using alternative liquidity risk measures, net loans to customer and short term funding. We
find that the results are almost same as the model using financing gap to total assets ratio (FGAPR).
The contribution of this study is to use another liquidity risk measures besides to liquidity
ratio, and we are the first study to investigate the causes of liquidity risk. Besides, we find that
liquidity risk is an endogenous determinant of bank performance. In subsample analysis, we further
classify countries as bank-based or market-based system, and investigate the difference of causes of
liquidity risk in different financial systems. Besides, we further investigate the effect of liquidity
risk on bank performance in different financial systems.
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26
Table 1 Empirical Results of the Relationship between Bank Liquidity Risk and Performance
Previous Studies
Bourke (1989)
Molyneux and Thornton (1992)
Demirgüç-Kunt and Huizinga (1999)
Liquidity Risk Measures
The ratio of liquid assets to total assets
The ratio of liquid assets to total assets
The ratio of loans to total assets
Shen, Kuo and Chen ( 2001)
Barth, Nolle, Phumiwasana and Yago (2003)
Demirgüç-Kunt, Laeven and Levine ( 2003)
The ratio of liquid assets to deposits
The ratio of liquid assets to total assets
The ratio of liquid assets to total assets
Kosmidou, Tanna and Pasiouras (2005)
The ratio of liquid assets to customer
and short term funding
Athanasoglou, Delis, and Staikouras (2006)
The ratio of loans to total assets
Pasiouras and Kosmidou (2007)
The ratio of net loans to customer and
short term funding
Kosmidou (2008)
The ratio of net loans to customer and
short term funding
The ratio of net loans to customer and
short term funding
Naceur and Kandil (2009)
27
Empirical Results
The liquidity ratio is positively related to return on assets (ROA).
The liquidity ratio is negatively related to return on assets (ROA).
The ratio of loans to total assets is negatively related to return on assets (ROA)
and positively related to net interest margins (NIM).
Banks with high fraction of liquid assets have lower net interest margins (NIM).
The liquidity ratio is negatively related to return on assets (ROA).
Banks that hold a high fraction of liquid assets have lower net interest margins
(NIM). And it is consistent with banks receiving lower returns on holding cash
or securities, but facing a competitive market for deposits.
The ratio of liquid assets to customer and short term funding has positive effect
on return on average assets (ROAA). It has negative effect on net interest
margins (NIM) but is only significant in the presence of external factors.
The ratio of loans to total assets has no effect on return on assets (ROA) and
return on equity (ROE).
The ratio of net loans to customer and short term funding is positively related to
return on average assets (ROAA) of domestic banks operating in the 15
European Union countries. And it is negatively related to ROAA of foreign
banks.
The ratio of net loans to customer and short term funding is negatively related to
return on average assets (ROAA).
The ratio of net loans to customer and short term funding is positively and
significantly related to net interest margins (NIM) of domestic banks, indicating
a negative relationship between net interest margins and the level of liquid
assets held by the bank. However, banks’ liquidity risk does not determine
returns on assets or equity (ROA or ROE) significantly.
Table 2 Bank Observations in Each Country and Year
Year/Country Australia
Canada
France
Germany
Italy
Japan
Luxembourg
Netherlands Switzerland Taiwan United Kingdom United States
Total
1994
17
21
151
124
76
144
51
26
144
27
41
337
1159
1995
19
24
159
135
91
144
67
22
130
28
45
363
1227
1996
20
25
153
135
91
144
61
24
140
27
51
366
1237
1997
17
29
143
147
94
137
73
24
131
31
52
388
1266
1998
16
30
132
138
102
131
68
24
117
33
46
368
1205
1999
13
29
124
121
100
127
70
20
117
38
43
358
1160
2000
14
27
113
128
99
127
68
13
110
37
48
386
1170
2001
15
24
104
129
97
125
55
20
90
34
47
373
1113
2002
14
26
98
128
94
123
35
16
89
36
51
362
1072
2003
13
25
96
113
99
122
32
19
89
38
50
321
1017
2004
12
18
87
107
103
123
26
18
92
37
45
279
947
2005
12
24
78
98
98
122
32
14
78
37
46
265
904
2006
15
23
71
102
88
121
33
15
83
32
40
260
883
Total
197
325
1509
1605
1232
1690
671
255
1410
435
605
4426
14360
28
Table 3 Variable Description
Category
Liquidity Risk
Variables Description/Calculation
FGAPR The ratio of financing gap to total assets. Financing gap defined as the difference between a bank's loans and customer deposit.
NLCS
The ratio of net loans to customer and short term funding.
Profitability
ROAA Net profit after tax divided by average total assets.
ROAE
Net profit after tax divided by average total equities.
NIM
Interest income minus interest expense over earning assets.
Bank-specific
SIZE
Natural logarithm of total assets
2
SIZE
Natural logarithm of total assets squared
LRLA
The ratio of less risky liquid assets to total assets. Less risky liquid assets is sum of the cash, due from central banks, treasury bills, government
securities.
RLA
The ratio of risky liquid assets to total assets. Risky liquid assets is sum of the deposits with banks, due from other banks, due from other credit
institutions, other bills and trading securities.
EFD
The ratio of external funding to total liabilities. We add money market funding and other funding as external funding.
ETA
The ratio of equity to total assets.
LLPL
The ratio of loan loss provision to loans.
Market structure CON
The ratio of total assets of the three largest commercial banks to total assets of all commercial banks in each country.
Supervisory
OSP
It is official supervisory power and used to measure of legal power of the supervisory agency. The value is principal component indicator of
fourteen variables.
PMI
It is private monitoring index and used to measures regulations that empower private monitoring of banks. Principal component indicator of nine
variables.
BAR
Indicator of bank's ability to engage in business of securities underwriting, insurance underwriting and selling, and in real estate investment,
management, and development.
Macroeconomic GDPC
Annual percent change of GDP
GDPCt-1 GDP annual percent change of last year
INF
Annual percent change of inflation
INF t-1
Inflation annual percent change of last year
Dummy variable MB
Market-based countries = 1, otherwise = 0
Source: Bank-specific data and market structure variables are available from BankScope database. And the data unit of each bank in a given year is million U.S dollars.
Supervisory variables are available from Barth, Caprio, and Levine (2004). And macroeconomic variables are available from World Economic Outlook Database (IMF).
29
Table 4 Descriptive Statistics
Variable
Mean
S.D.
Min
Max
FGAPR
-0.0244
0.3090
-0.9403316
0.9967066
NLCS
73.6351
41.0074
0.03
908.06
ROAA
0.7540
1.4016
-18.44
40.26
ROAE
8.8241
15.4351
-99.6
536.85
NIM
3.0135
2.2128
-3.29
66.94
SIZE
7.9856
2.0053
2.714695
14.57717
SIZE2
67.7904
33.6525
7.369567
212.494
LRLA
0.0246
0.0288
1.07E-06
0.7407508
RLA
0.1426
0.2005
1.63E-06
0.9757636
EFD
0.1324
0.1607
0
0.9891557
ETA
0.0806
0.0466
0.0033
0.3
LLPL
0.0103
0.0259
0
0.9166667
CON
0.3447
0.1741
0.1599831
0.9260695
OSP
0.1922
1.1167
-2.15
1.14
PMI
0.9110
0.2528
0.29
1.46
BAR
2.2490
0.8170
1.25
3.25
GDPC
2.5156
1.6111
-2.171
8.443
GDPCt-1
2.3836
1.7119
-2.171
8.443
INF
1.7797
1.1022
-0.887
5.393
INF t-1
1.8637
1.1431
-0.887
5.393
Obs
14360
For the notation of the variables see Table 3.
30
Table 5 Causes of Liquidity Risk Results Using FGAPR to Measure Liquidity
Risk
The model is estimated using fixed effects regression. Dependent variable is the financing gap ratio
(FGAPR) defined as the ratio of financing gap to total assets. And financing gap is the difference
between a bank's loans and customer deposit.
Pre. Sign
(1)
(2)
(3)
(4)
CONSTANT
?
-0.3377***
-0.3379***
-0.3380***
-0.3334***
SIZE
+
0.0717***
0.0718***
0.0717***
0.0709***
SIZE2
-
-0.0039***
-0.0040***
-0.0039***
-0.0039***
LRLA
-
-0.8144***
-0.8125***
-0.8144***
-0.8141***
RLA
+
-0.6231***
-0.6241***
-0.6232***
-0.6233***
EFD
+
0.6420***
0.6424***
0.6420***
0.6423***
GDPC×OSP
-
GDPC×PMI
-
GDPC×BAR
-
GDPC
+
0.0015**
0.0023***
0.0011
0.0059***
GDPCt-1
+
0.0027***
0.0029***
0.0027***
0.0026***
INF
+
0.0053***
0.0052***
0.0053***
0.0056***
INF t-1
+
0.0068***
0.0064***
0.0068***
0.0066***
Obs
14360
14360
14360
14360
R2
0.2049
0.2167
0.2048
0.2123
-0.0029***
0.0004
-0.0019**
Notes: All variables in these regressions have been defined in Table 3. ***, ** and * denote
significance at the 1%, 5% and 10% levels.
31
Table 6 Bank Liquidity Risk and Performance Results Using FGAPR to Measure
Liquidity Risk
The model is estimated by instrumental variables regression, using two stage least squares (2SLS)
estimators. Dependent variables are return on average assets (ROAA) defined as net profit after tax
divided by average total assets, return on average equities (ROAE) defined as net profit after tax
divided by average total equities, net interest margin (NIM) defined as interest income minus interest
expense over earning assets, and using financing gap ratio (FGAPR) to assess bank liquidity risk.
Pre. Sign
CONSTANT
FGAPR
SIZE
SIZE2
ETA
LLPL
CON
GDPC×OSP
GDPC×PMI
GDPC×BAR
GDPC
GDPCt-1
INF
INF t-1
Obs
R2
CONSTANT
FGAPR
SIZE
SIZE2
ETA
LLPL
CON
GDPC×OSP
GDPC×PMI
GDPC×BAR
GDPC
GDPCt-1
INF
INF t-1
Obs
R2
CONSTANT
FGAPR
SIZE
SIZE2
ETA
LLPL
CON
GDPC×OSP
GDPC×PMI
GDPC×BAR
GDPC
GDPCt-1
INF
INF t-1
Obs
R2
?
+
+
+
?
?
?
+
+
+
+
?
+
+
+
?
?
?
+
+
+
+
?
+
+
+
+
+
?
?
?
+
+
+
+
(1)
(2)
(3)
(4)
Panel A: ROAA As Dependent Variable
-1.4499***
-1.5426***
-1.4726***
-1.5675***
-0.4304***
-0.3556***
-0.2526***
-0.3654***
0.3754***
0.3379***
0.3225***
0.3400***
-0.0210***
-0.0184***
-0.0177***
-0.0185***
6.7337***
6.8674***
6.8642***
6.8372***
-8.8578***
-8.6854***
-8.6230***
-8.6494***
0.4317***
0.4183***
0.4353***
0.4296***
0.0240***
0.0403**
(5)
-1.5239***
-0.3412***
0.3330***
-0.0182***
6.8332***
-8.6511***
0.4560***
0.0268**
0.0236***
-0.0159
0.0185
14360
0.1226
0.0112*
0.0314*
0.0227**
-0.0174
0.0192*
14360
0.1211
Panel B: ROAE As Dependent Variable
-7.3998**
-10.0715*** -8.5036*** -10.3304***
-4.8768***
-3.5992***
-1.1088
-3.7688***
3.4143***
2.5645***
2.2698***
2.5891***
-0.1888***
-0.1315***
-0.1184***
-0.1330***
16.3567*** 17.0167*** 15.2060*** 16.3557***
-90.3370*** -86.2048*** -84.4944*** -85.6749***
3.4842***
4.8125***
5.1443***
4.9883***
0.4957***
0.4531**
-9.5045***
-3.2287***
2.4322***
-0.1258***
15.7693***
-85.2673***
5.6851***
14360
0.0998
0.0572***
0.0224***
-0.0145
0.0179
14360
0.1196
0.0498***
0.0203***
-0.0142
0.0207*
14360
0.1339
0.7456***
0.3879***
0.1291
0.5871***
14360
0.0811
0.2458***
0.5165***
0.3762***
0.0840
0.6047***
14360
0.0776
Panel C: NIM As Dependent Variable
3.9625 ***
3.4496 ***
3.5182 ***
3.2849 ***
0.8107 ***
0.8104 ***
0.9220 ***
0.8166 ***
0.0565
0.0302
0.0162
0.0428
-0.0188 *** -0.0156 *** -0.0149 *** -0.0158 ***
3.3076 ***
3.3948 ***
3.4209 ***
3.4198 ***
3.3562 ***
3.5696 ***
3.6128 ***
3.7416 ***
-1.7725 *** -1.7689 *** -1.7711 *** -1.7548 ***
0.0349 ***
0.2261 ***
3.4913 ***
0.9110 ***
0.0140
-0.0147 ***
3.3587 ***
3.7589 ***
-1.6046 ***
14360
0.024
14360
0.1208
1.0815***
0.3692***
0.1550
0.5794***
14360
0.0785
0.0541 ***
0.0323 ***
0.0725 ***
0.0787 ***
14360
0.1634
0.9261***
0.3210***
0.1607
0.6347***
14360
0.0959
0.0433 ***
0.0296 ***
0.0734 ***
0.0826 ***
14360
0.1757
-0.1176 ***
0.0380 ***
0.0703 ***
0.0822 ***
14360
0.2126
0.0705 ***
-0.1089 ***
0.0344 ***
0.0585 ***
0.0873 ***
14360
0.2007
Notes: All variables in these regressions have been defined in Table 3. ***, ** and * denote
significance at the 1%, 5% and 10% levels.
32
Table 7 Causes of Liquidity Risk Results in Different Financial System
(Dependent variable: FGAPR)
The model is estimated using fixed effects regression. And we use market-based system countries and
bank-based system countries as sample respectively. Dependent variable is the financing gap ratio
(FGAPR) defined as the ratio of financing gap to total assets. And financing gap is the difference
between a bank's loans and customer deposit.
Pre. Sign
(1)
(2)
(3)
(4)
Panel A: Market-Based Financial System
CONSTANT
?
-0.4005***
-0.3968***
-0.3997***
-0.3945***
SIZE
+
0.0753***
0.0748***
0.0754***
0.0742***
2
SIZE
-
-0.0043***
-0.0042***
-0.0043***
-0.0042***
LRLA
-
-0.8819***
-0.8784***
-0.8819***
-0.8813***
RLA
+
-0.6580***
-0.6578***
-0.6577***
-0.6576***
EFD
+
0.7270***
0.7265***
0.7270***
0.7280***
GDPC×OSP
-
GDPC×PMI
-
GDPC×BAR
-
GDPC
+
0.0016**
0.0037***
0.0024
0.0066***
GDPCt-1
+
0.0018**
0.0020***
0.0018**
0.0017**
INF
+
0.0078***
0.0075***
0.0078***
0.0082***
INF t-1
+
0.0098***
0.0092***
0.0097***
0.0097***
Obs
10014
10014
10014
10014
2
0.214
0.2264
0.2143
0.2232
R
-0.0034***
-0.0010
-0.0020**
Panel B: Bank-Based Financial System
CONSTANT
?
-0.1236
-0.1220
-0.1277
-0.1235
SIZE
+
0.0603***
0.0601***
0.0616***
0.0603***
2
SIZE
-
-0.0038***
-0.0038***
-0.0039***
-0.0038***
LRLA
-
-0.6039***
-0.6039***
-0.6014***
-0.6039***
RLA
+
-0.5783***
-0.5782***
-0.5784***
-0.5783***
EFD
+
0.4734***
0.4729***
0.4748***
0.4734***
GDPC×OSP
-
GDPC×PMI
-
GDPC×BAR
-
GDPC
N
0.0028
0.0055
-0.0117
0.0028
GDPCt-1
N
0.0018
0.0018
0.0021
0.0018
INF
N
0.0009
0.0011
-0.0006
0.0009
INF t-1
N
0.0014
0.0012
0.0018
0.0014
Obs
4346
4346
4346
4346
2
0.231
0.2308
0.2373
0.231
R
0.0022
0.0150*
0.0000
Notes: All variables in these regressions have been defined in Table 3. ***, ** and * denote
significance at the 1%, 5% and 10% levels.
33
Table 8 The Relationship Between Financial System and Bank Performance
Using FGAPR to Measure Liquidity Risk
The model is estimated by instrumental variables regression, using two stage least squares (2SLS)
estimators. Dependent variables are return on average assets (ROAA) defined as net profit after tax
divided by average total assets, return on average equities (ROAE) defined as net profit after tax
divided by average total equities, net interest margin (NIM) defined as interest income minus interest
expense over earning assets and using financing gap ratio (FGAPR) to assess bank liquidity risk.
Besides, we add a dummy variable (MB), and the value is 1 if the countries are classified as
market-based system.
Pre. Sign
CONSTANT
FGAPR
SIZE
SIZE2
ETA
LLPL
CON
GDPC×OSP
GDPC×PMI
GDPC×BAR
GDPC
GDPCt-1
INF
INF t-1
MB
Obs
R2
?
+
+
+
?
?
?
+
+
+
+
?
CONSTANT
FGAPR
SIZE
SIZE2
ETA
LLPL
CON
GDPC×OSP
GDPC×PMI
GDPC×BAR
GDPC
GDPCt-1
INF
INF t-1
MB
Obs
R2
?
+
+
+
?
?
?
+
+
+
+
?
CONSTANT
FGAPR
SIZE
SIZE2
ETA
LLPL
CON
GDPC×OSP
GDPC×PMI
GDPC×BAR
GDPC
GDPCt-1
INF
INF t-1
MB
Obs
R2
?
+
+
+
+
+
?
?
?
+
+
+
+
?
(1)
(2)
(3)
(4)
Panel A: ROAA As Dependent Variable
-1.5382***
-1.6071***
-1.5496***
-1.6311***
-0.3006***
-0.2687***
-0.2235**
-0.2794***
0.3508***
0.3221***
0.3150***
0.3245***
-0.0202***
-0.0180***
-0.0176***
-0.0181***
6.6174***
6.7598***
6.7814***
6.7280***
-8.7244***
-8.5915***
-8.5664***
-8.5578***
0.3907***
0.3910***
0.4094***
0.4021***
0.0161***
0.0394**
(5)
-1.5973***
-0.2655***
0.3205***
-0.0179***
6.7415***
-8.5792***
0.4098***
0.0054
0.0537***
0.0494***
0.0241*
0.0414**
0.0185***
0.0177***
0.0197***
0.0187***
-0.0104
-0.0108
-0.0117
-0.0119
0.0152
0.0176
0.0157
0.0158
0.3700***
0.2875***
0.2318***
0.2852***
0.2802***
14360
14360
14360
14360
14360
0.1082
0.1216
0.129
0.1243
0.1219
Panel B: ROAE As Dependent Variable
-7.5903 ** -10.0383 *** -8.3832 *** -10.2841 *** -9.5390 ***
-3.0654 ***
-2.6684 **
-1.1158
-2.8531 **
-2.5214 **
3.1657 ***
2.4622 ***
2.2721 ***
2.4895 ***
2.3676 ***
-0.1810 *** -0.1290 *** -0.1177 *** -0.1307 *** -0.1246 ***
14.6752 *** 16.0122 *** 15.4622 *** 15.3950 *** 15.1347 ***
-88.7818 *** -85.4606 *** -84.5534 *** -84.9772 *** -84.8004 ***
2.8062 **
4.4225 ***
5.2599 ***
4.5983 ***
5.2610 ***
0.5197 ***
0.4365 **
0.2147 ***
1.0567 ***
0.9292 ***
0.7337 ***
0.5687 ***
0.3420 ***
0.3300 ***
0.3607 ***
0.3543 ***
0.1877
0.1549
0.1612
0.1184
0.5587 ***
0.6485 ***
0.5662 ***
0.5852 ***
2.9495 ***
1.3848 **
-0.4418
1.3430 **
1.0833 *
14360
14360
14360
14360
14360
0.0293
0.0786
0.0979
0.0811
0.0776
Panel C: NIM As Dependent Variable
3.7810***
3.3173***
3.4186***
3.1589***
3.3953***
0.9074***
0.9000***
0.9598***
0.8986***
0.9635***
0.0382
0.0167
0.0087
0.0290
0.0042
-0.0182***
-0.0151***
-0.0147***
-0.0155***
-0.0145***
3.2358***
3.3588***
3.3784***
3.3585***
3.3059***
3.4308***
3.6386***
3.6498***
3.8057***
3.7960***
-1.7917***
-1.8002***
-1.7836***
-1.7747***
-1.6221***
0.0307***
0.2272***
0.0675***
0.0519***
0.0431***
-0.1206***
-0.1037***
0.0301***
0.0283***
0.0358***
0.0325***
0.0756***
0.0751***
0.0728***
0.0609***
0.0770***
0.0809***
0.0804***
0.0855***
0.4433***
0.3463***
0.2383***
0.3412***
0.2582***
14360
14360
14360
14360
14360
0.1216
0.1635
0.1724
0.2112
0.1961
Notes: All variables in these regressions have been defined in Table 3. ***, ** and * denote
significance at the 1%, 5% and 10% levels.
34
Table 9 Bank Liquidity Risk and Performance in Different Financial Systems
Using FGAPR to Measure Liquidity Risk (Dependent Variable: ROAA)
The model is estimated by instrumental variables regression, using two stage least squares (2SLS)
estimators. And we use market-based system countries and bank-based system countries as sample
respectively. Dependent variable is return on average assets (ROAA) defined as net profit after tax
divided by average total assets and using financing gap ratio (FGAPR) to assess bank liquidity risk.
Pre. Sign
CONSTANT
?
(1)
(2)
(3)
(4)
Panel A: Market-Based Financial System
-0.8124 *** -0.9914 *** -1.0062 *** -1.0310 ***
FGAPR
-
-0.4341 ***
-0.4222 ***
-0.3101 ***
-0.4429 ***
-0.4169 ***
SIZE
+
0.2671 ***
0.2267 ***
0.2207 ***
0.2285 ***
0.2229 ***
2
SIZE
-
-0.0162 ***
-0.0130 ***
-0.0127 ***
-0.0131 ***
-0.0129 ***
ETA
+
7.4036 ***
7.5794 ***
7.5908 ***
7.5308 ***
7.5324 ***
LLPL
-
-10.6581 *** -10.3236 *** -10.2521 *** -10.2440 *** -10.2661 ***
CON
+
0.3325 ***
GDPC×OSP
?
GDPC×PMI
?
GDPC×BAR
?
GDPC
+
0.0496 ***
0.0283 ***
0.0163
0.0183
GDPCt-1
+
0.0135
0.0126 *
0.0146 **
0.0146 **
INF
+
0.0099
0.0087
0.0092
0.0059
INF t-1
+
0.0312 ***
0.0380 ***
0.0325 ***
0.0322 ***
0.3722 ***
0.4325 ***
0.3919 ***
(5)
-0.9849 ***
0.4274 ***
0.0372 ***
0.0473 ***
0.0129 *
Obs
10014
10014
10014
10014
10014
2
0.1399
0.1677
0.191
0.1734
0.1687
R
Panel B: Bank-Based Financial System
-2.3196 *** -2.4337 *** -2.4454 *** -2.4571 ***
CONSTANT
?
-2.4502 ***
FGAPR
N
-0.0549
-0.0954
-0.0985
-0.1012
-0.0996
SIZE
+
0.5259 ***
0.5439 ***
0.5482 ***
0.5432 ***
0.5477 ***
2
SIZE
-
-0.0282 ***
-0.0286 ***
-0.0287 ***
-0.0285 ***
-0.0287 ***
ETA
+
5.3223 ***
5.5812 ***
5.6675 ***
5.6337 ***
5.6714 ***
LLPL
-
-6.6247 ***
-6.5659 ***
-6.5666 ***
-6.5923 ***
-6.5727 ***
CON
+
0.4313 *
0.2288
0.1595
0.2305
0.1668
GDPC×OSP
?
GDPC×PMI
?
GDPC×BAR
?
GDPC
+
0.0818 ***
0.1680 **
0.1585 **
0.1546 ***
GDPCt-1
+
0.0162
0.0158
0.0145
0.0154
INF
+
-0.0445
-0.0364
-0.0348
-0.0349
INF t-1
+
-0.0154
-0.0214
-0.0168
-0.0211
0.0721
-0.0798
-0.0435
Obs
4346
4346
4346
4346
4346
2
0.0334
0.0401
0.0397
0.0412
0.04
R
Notes: All variables in these regressions have been defined in Table 3. ***, ** and * denote
significance at the 1%, 5% and 10% levels.
35
Table 10 Bank Liquidity Risk and Performance in Different Financial Systems
Using FGAPR to Measure Liquidity Risk (Dependent Variable: ROAE)
The model is estimated by instrumental variables regression, using two stage least squares (2SLS)
estimators. And we use market-based system countries and bank-based system countries as sample
respectively. Dependent variable is return on average equities (ROAE) defined as net profit after tax
divided by average total equities and using financing gap ratio (FGAPR) to assess bank liquidity risk.
Pre. Sign
(1)
(2)
(3)
(4)
Panel A: Market-Based Financial System
-3.2886
-8.4868 *** -8.9957 *** -9.0135 ***
(5)
CONSTANT
?
-8.3712 ***
FGAPR
-
-3.0750 ***
-3.2422 ***
-1.2287
-3.6237 ***
-3.1837 ***
SIZE
+
3.3960 ***
2.4160 ***
2.3212 ***
2.4463 ***
2.3542 ***
2
SIZE
-
-0.2122 ***
-0.1351 ***
-0.1278 ***
-0.1366 ***
-0.1320 ***
ETA
+
9.6326 **
12.8136 ***
11.9211 ***
11.7461 ***
12.2231 ***
LLPL
-
CON
+
GDPC×OSP
?
GDPC×PMI
?
GDPC×BAR
?
GDPC
+
1.2362 ***
0.9007 ***
0.8123 ***
0.8928 ***
GDPCt-1
+
0.0680
0.0754
0.0899
0.0837
INF
+
0.4958 ***
0.4547 ***
0.4681 ***
0.4491 ***
INF t-1
+
1.0262 ***
1.1679 ***
1.0439 ***
1.0417 ***
-122.0364 *** -111.7293 *** -110.0775 *** -110.4951 *** -110.8957 ***
1.8137
4.1434 ***
5.4497 ***
4.5136 ***
4.7971 ***
0.6222 ***
0.6082 ***
0.1428 *
Obs
10014
10014
10014
10014
10014
2
0.0275
0.1397
0.1732
0.1466
0.1384
R
CONSTANT
?
Panel B: Bank-Based Financial System
-6.0904
-6.1449
-6.2930
-5.9175
-6.2901
FGAPR
N
0.6513
0.4029
0.4020
0.4719
0.3888
SIZE
+
1.6742
1.7744
1.8208
1.7747
1.8045
2
SIZE
-
-0.0562
-0.0588
-0.0601
-0.0591
-0.0596
ETA
+
19.1891 **
21.3397 **
22.1635 **
20.9143 **
21.9611 **
LLPL
-
-57.7052 *** -56.9908 *** -57.1035 *** -56.6921 *** -57.1109 ***
CON
+
GDPC×OSP
?
GDPC×PMI
?
GDPC×BAR
?
GDPC
+
0.5017 **
1.1640
-0.0016
0.8872
GDPCt-1
+
0.6233 ***
0.6244 ***
0.6353 ***
0.6214 ***
INF
+
-0.3164
-0.2373
-0.3898
-0.2532
INF t-1
+
-0.3212
-0.3646
-0.3181
-0.3487
6.3136 *
2.5468
1.8638
2.4369
2.1392
0.5549
0.5283
-0.2315
Obs
4346
4346
4346
4346
4346
2
0.0132
0.0202
0.0201
0.0195
0.0203
R
Notes: All variables in these regressions have been defined in Table 3. ***, ** and * denote
significance at the 1%, 5% and 10% levels.
36
Table 11 Bank Liquidity Risk and Performance in Different Financial Systems
Using FGAPR to Measure Liquidity Risk (Dependent Variable: NIM)
The model is estimated by instrumental variables regression, using two stage least squares (2SLS)
estimators. And we use market-based system countries and bank-based system countries as sample
respectively. Dependent variable is net interest margin (NIM) defined as interest income minus interest
expense over earning assets and using financing gap ratio (FGAPR) to assess bank liquidity risk.
Pre. Sign
CONSTANT
?
(1)
(2)
(3)
(4)
Panel A: Market-Based Financial System
2.7964***
2.3576***
2.3397***
2.1310***
(5)
FGAPR
+
0.7113***
0.7865***
0.9354***
0.7585***
0.8748***
SIZE
+
0.3298***
0.3093***
0.3034***
0.3179***
0.2977***
2
SIZE
-
-0.0315***
-0.0284***
-0.0282***
-0.0283***
-0.0278***
ETA
+
3.1089***
3.6809***
3.6930***
3.6828***
3.6944***
LLPL
+
3.5939***
4.1131***
4.1218***
4.4835***
4.5738***
CON
+
-1.7715***
-2.0162***
-1.9332***
-1.9878***
-1.7358***
GDPC×OSP
?
GDPC×PMI
?
GDPC×BAR
?
GDPC
+
0.0470***
0.0076
-0.1373***
-0.1625***
GDPCt-1
+
0.0390***
0.0359***
0.0427***
0.0445***
INF
+
0.0434***
0.0434***
0.0484***
0.0250
INF t-1
+
0.0590***
0.0695***
0.0664***
0.0665***
2.3348***
0.0659***
0.2595***
0.0857***
Obs
10014
10014
10014
10014
10014
2
0.1464
0.2031
0.2267
0.2933
0.2655
R
CONSTANT
?
Panel B: Bank-Based Financial System
7.4037***
6.4184***
6.4041***
6.4271***
6.4092***
FGAPR
+
0.8536***
1.0784***
1.1005***
1.0772***
1.0969***
SIZE
+
-0.7487***
-0.7411***
-0.7443***
-0.7335***
-0.7412***
2
SIZE
-
0.0187*
0.0185*
0.0183*
0.0180*
0.0181*
ETA
+
2.6204***
2.4666***
2.2721***
2.3993***
2.2783***
LLPL
+
2.9342***
2.9809***
2.9590***
3.0151***
2.9739***
CON
+
-1.5347***
-0.8163***
-0.6425**
-0.8028***
-0.6640**
GDPC×OSP
?
GDPC×PMI
?
GDPC×BAR
?
GDPC
+
0.0583***
-0.2148***
-0.1040
-0.1598***
GDPCt-1
+
0.0311*
0.0334**
0.0344**
0.0341**
INF
+
0.1728***
0.1513***
0.1540***
0.1482***
INF t-1
+
0.1070***
0.1270***
0.1115***
0.1255***
-0.2280***
0.1679**
0.1300***
Obs
4346
4346
4346
4346
4346
2
0.119
0.1389
0.1412
0.141
0.1416
R
Notes: All variables in these regressions have been defined in Table 3. ***, ** and * denote
significance at the 1%, 5% and 10% levels.
37
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