A Refereed Monthly International Journal of Management
Impact of Liquidity and Risk on Bank Capital: Empirical Analysis of Commercial Banks
Author
|
Faisal Abbas
PhD (Finance) scholar,
University of Lahore
Pakistan.
|
Shahid Iqbal
PhD (Finance) scholar,
The University of Lahore,
Pakistan
|
Dr. Bilal Aziz
Assistant Professor
University of Engineering and Technology Lahore,
In-charge Professional Development Centre (PD) Institute of Business Management (IBM)
Pakistan
|
Abstract
The purpose of this study is to explore the influence of bank liquidity and bank risk on bank capital among emerging economies of Asia in post crisis conditions. The data is collected for 379 banks from Bank scope database. The data for this paper includes post crisis period ranging from 2011 to 2016. Linear regression panel-corrected standard errors approach is used to find consistent estimators. The results confirm that liquidity is negatively influencing bank capital in medium size banks while small banks are positively impacted by liquidity. The overall risk has positive effect on bank capital in all banks which is consistent with regulatory hypothesis. The findings also prove that market funding, banks size and real gross domestic product have negative effect on bank capital.
Keyword: Bank Capital, Bank liquidity, Market Funding, GDP, Bank Size and Z-score
Introduction
The prime focus of this study is to explore the post crisis influence of bank liquidity and bank risk on bank capital among emerging economies of Asia. There are several questions to be asked with respect to bank capital, bank liquidity and bank risk. Does the bank liquidity influence the bank capital in current situation? Is this relationship similar for all banks? Does the risk of bank influence the bank capital if yes then which risk is more important? Does the economic situations influence the bank capital if yes then which economic factors are more critical to consider in managing the bank capital, liquidity and risk? In reaction to the last financial crisis, the Basel Committee recommends new regulations for banks regarding liquidity level and bank capital in Basel Accord III. The Basel III suggests new regulations to protect the banks from different types of risks like liquidity shortage risk, credit risk, operations risk and interest rate risk. The new standards require higher capital buffers and greater level of liquid assets as compared to Basel II. Due to these changes, bank capital and liquidity requirements have become a major concern to test the relationship among bank capital, liquidity and risk of banks. The level of bank capital is directly linked with bank liquidity and bank risk. Despite this fact, empirical evidence on this concept is rare especially in emerging economies of Asia in post crisis era.Barth, Caprio, and Levine (2008)argue that the behavior of Asian countries isnot similar regarding bank capital
Faisal requirements. According to their findings, some countries
increased their bank capital requirements while others
relaxed. The previous studies which are conducted to explore the relationship between bank risk and bank capital
provide mixed findings. Some studies found positive
relation between risk and capital which is consistent with
“regulatory hypothesis” (Shrieves & Dahl, 1992),
(Iannotta, Nocera, & Sironi, 2007) and (Demirgüç-Kunt &
Huizinga, 2000). In contrast, some researchers found
negative relationship between risk and bank capital which
refers to “moral hazard hypothesis” where banks normally
take more risk due to deposit insurance as concluded
by(Asli Demirgüç-Kunt & Kane, 2002),(Brewer & Lee,
1986)(Agusman, Monroe, Gasbarro, & Zumwalt, 2008)
They also conclude in their researches that risk and bank
capital both lead to influence the profitability of banks.
Theoretically, there are many studies conducted to incorporate the meaningful analysis of bank capital, liquidity and risk but some of the problems are not dispensed with appropriate manners. This research may fill the gap in literature. Firstly, this study will incorporate
external factors and internal factors simultaneouslywhereas the previous studies particularly focus determinants of bank capital, liquidity and risk considering found that with increase in bank size, level of liquidity
specific factors of firms at industry level ignoring external actors. Secondly, it is found in literature that econometric
techniques used by researchers do not incorporate
appropriate features of concerned questions makingestimates inconsistent and biased. Thirdly, this study helps to reconsider the formulation of regulations with to
overcome the issues in current situation of banking system in Asian region in specific. Fourthly, the overall findings of
this study would be helpful for the decision makers, analysts, policy makers and other stakeholders like governments and investors. Fifthly, this would be the study in Asian region to point out the factors which become
major concerns in further developments after the effect ofBasel Accord III regulations regarding bank liquidity, riskand bank capital. Sixthly, this study will provide resultswhich confirm the effect of currently implemented
regulations recommended by Basel Accord III in Asian region. To the best of my information, there is no study to address the above said questions especially in emerging explored that illiquidity affected smaller U.S.A banks and
economics of Asian region after the financial crisis of 2007 and 2008. The plenty of studies are conducted in developed countries like US and European countries where very less attention is given to the emerging economies of Asia which
is major and rapidly growing part of the whole financialsystem of the globe. The Second section of this studypresents review of relevant literature. The third section
(2017)and Cole and White (2012). Altunbas et al. (2017)
contains the data collection sources and econometricmodel. The fourth section is about the empirical findings.The fifth part of this paper consists of conclusions and
recommendations.
Literature Review
Raz (2018) explored evidence regarding bank capital and
bank risk using Indonesian banking sample and concluded
negative relationship. Haneef, Archer, and Karim
(2018)argued in their recently published paper that losses
covered from earnings of banks which included in tier-1
capital. The influence of loan loss provisions are also
absorbed by earnings which become cause of decrease in
tier-1 capital. Hugonnier and Morellec (2017) described in
their paper that bank leverage and liquidity as
recommended by Basel-III reduced probability of default.
Chiaramonte and Casu (2017) conducted study regarding
bank liquidity holding and bank capital as suggested by
regulatory authorities and they favor regulations recommended in Basel III. According to their findings, capital ratio found important proxy to manage in case of large banks and their model also favored holding of higher liquid assets to avoid failure. DeYoung and Jang (2016)found empirical evidences in their study regarding
bank liquidity holding and bank capital as suggested by regulatory authorities and they favor regulations recommended in Basel III. According to their findings,
capital ratio found important proxy to manage in case oflarge banks and their model also favored holding of higher liquid assets to avoid failure. DeYoung and Jang
empirical evidences in their study regardingloan to deposit rate and net stable fund ratio and also found these ratios influential for all categories of banks. They also found that with increase in bank size, level of liquidity
decreases. Diamond and Kashyap (2016)argued that liquidity standards as suggested by regulators in Basel-III influenced incentives of banks. These regulations
definitely decreased probability of run but on the otherhand higher liquidity cost more to banks. In corporations,capital and liquidity always remained basic instruments to
evaluate strengths and soundness. In USA the analysts and rating agencies applied different techniques to judge soundness of organizations like CAMELS methodology
approach Distinguin, Roulet, and Tarazi (2013) conducted a study to find relationship between bank liquidity and regulatory capital and they concluded that banks normally decrease their regulatory capital at the time when they
create liquidity and also at the time when banks face lower
liquidity funding risk. Beltratti and Stulz (2012) conducted study and concluded results that banks decreased their
capital in liquidity creations conditions and at the time oflower liquidity funding requirements. Carmona (2007) explored that illiquidity affected smaller U.S.A banks and suggested them to maintain higher liquidity to reduce their default risk. The following studies presented findings in
most recent years and concluded that aggressive lending, more reliance on short term funding, low quality assets, low capitalizations and low profitability are connected
with risk, Altunbas, Manganelli, and Marques-Ibanez(2017)and Cole and White (2012). Altunbas et al. (2017) argued that banks capital encourages banks to take risk in
both cases at lower level and higher level as well in
nonlinear. The above debate, encourage regulators to
motivate the riskier banks to create capital buffers. Delis
and Staikouras (2011) found direct relationship between
bank capital and risk taking behavior. Following are the
earliest studies conducted by renowned scholars in similar
domain Cole and Gunther (1995); Wheelock and Wilson
(2000), and DeYoung (2003). Pettway (1976) conducted a
study to find out the relationship between capital structure
and bank risk in U.S.A to cover the period of four years,
where they found direct relationship between risk and
equity ratio. The similar finding provided by Shrieves and
Dahl (1992) in similar sample means U.S.A. (Rime,
2001)and Iannotta et al. (2007) also found positive
relationship between bank capital ratio and risk in European banking sample. These findings are favoring the
regulatory hypothesis means banks increase their capital as
response to their risk. On the other hand, some studies like
Daesik Kim and Santomero (1988) Jahankhani and Lynge
(1979), (Agusman et al., 2008; Jacques & Nigro,
1997)conducted study in U.S.A banking industry and
found negative relationship between bank capital and bank
risk which is consistent with “Moral Hazard Hypothesis” in
such case banks normally take the advantage of deposits
insurance. Shim (2013) explored that capital buffers decrease the chances of default due to this banks hold greater capital buffers in order to manage more risky portfolio. This suggestion depicted that banks having higher level of capital buffer have risk assets in its balance sheets. Jeitschko and Jeung (2005) find the relationship between bank capital and bank risk which can be positive or negative as suggested in moral hazard hypothesis and regulatory hypothesis respectively. There is agency problem in such relationship like shareholder of well capitalized banks would like to invest in less risky assets due to their interest where the managers prefer to invest in more risky portfolio due to their interest where Repullo (2004) found the similar results in his study. The above detail examination of previous studies relating to developed economies (USA & EP) motivates to explore the empirical findings after post crisis in emerging economies especially in Asian region. This study contributes in existing literature regarding bank capital, liquidity and risk in Asia.
Data and Methodology
In this study the population is eight emerging economies of Asia which include Pakistan, India, Korea, China, Thailand, Malaysia, Philippines, and Indonesia. There were more than 400 banks in the list of bank scope database. The data were collected for six year ranging from 2011 to 2016 and banks which have missing data are
excluded from sample. The Sample data comprised 379
banks which were found fit for analysis. The banks were
divided into three categories of small, medium and large
banks as per their volume of assets. The data for economic
variables were collected from the World Bank website and
Central Banks of respective economy for the period of six
years from 2011 to 2016.The banks were divided into
different categories due to following reasons. Primary
reason is heterogeneity in banks data which may lead to
inappropriate findings. Secondly, literature explores the
difference in activities of banks according to their size as
reported byKashyap and Stein (2000) also reported
byCornett, McNutt, Strahan, and Tehranian (2011) and also
byBerger and Bouwman (2013).
Econometric Model
In this study, alternative model was used to manage
a u t o c o r r e l a t i o n , h e t e r o s c e d a s t i c i t y a n d
contemporaneously correlation among panels. In this
model, we developed linear regression panel corrected
standard errors instead of feasible generalized least
squares. The objective was to test the influence of bank
liquidity, and bank risk on bank capital after the crisis time
period. The desired equation is arranged as below:
!? ?,? = â1Liqi,t + â2Riski,t + â3FSVi,t + â4MSVi,t +å
Here “t” is defined to be as the bank time period till the day we have got data of the bank. (â1, â2, â3, â4,) are the structural parameters of the equation, “i” defined the bank where, “å” is the symbolic of error term. Every coefficient in reaction to a variation in the variable attains the shortterm influence on bank capital in the model of econometric. There are several problems in panel data which should be addressed properly otherwise the estimators should be biased. The problem includes heteroscedasticity, autocorrelation and cross-dependence. In order to deal with these issues, we used panel corrected standard errors approach to test the above equation. The results were found by using simple panel data ordinary least squares methods, robust standard errors options and panel corrected standard errors to confirm that findings were appropriate.
Dependent Variable
Bank Capital Ratio (BC) was used as dependent variable in above model and the researchers measured ratio of total equity of bank in present to total assets. Bank capital coefficient is expected positive because of the higher equity. Due to this higher equity, loss absorption capacity of banks will be higher as argued by following researchers: Bernanke, Lown, and Friedman (1991), Gambacorta and Mistrulli (2004); Francis and Osborne (2012); and Kapan and Minoiu (2013).
Explanatory Variables
by Boyd and Graham (1988); Laeven and Levine (2009);
As included in above model, liquidity and risk were primeconcerns of this study where the term “FSV” represented concerns of this study where the term “FSV” represented
other firm specific variables which included market funding, market share and size of firm. The variable liquidity is measured as liquid assets to total assets
second variable of major concern in above model is (Risk) expectations from Government. Market funding is also
taken as overall risk factor of banks by calculating Z-Score. used in this paper as a variable and measured as ratio of
The term “MSV” is used to test the influence of economic total liabilities less total deposits to total assets of bank. The
variables in this study which include growth of real gross sign for it may vary according to bank size like positive in
domestic product and interest rate. Risk in this study is case of larger banks while negative for smaller banks
calculated the sum of equity and return on assets and
divided by the standard deviation of return on assets as used by Boyd and Graham (1988); Laeven and Levine (2009); Barry, Lepetit, and Tarazi (2011).Bank size is another explanatory variable which is used as control proxy in this study taken as natural logarithm of banks total assets. The
Bank size influence is not definite. According to theory
Results and Discussion
Table#1Panel Data Regression Results Overall banks
Description: Bank capital as measure bank equity to total
assets, Bank Liquidity calculated as liquid assets to total
assets. Interest rate is defined as yearly change in interest rate, bank size is measured as log to total assets. Where
market funding measured as total liabilities less total
deposits divided by total assets, Bank risk is measured as Zscore calculated as equity ratio plus return on assets divided
by the variance of return on assets. In this model, we
developed linear regression panel corrected standard errors
instead of feasible generalized least squares
Table#2Panel Data Regression Results Large Banks
It contains results for overall banks. The model is
developed to examine the evidence regarding bank
liquidity, bank risk and bank capital in emerging economies
of Asia. The results indicate that liquidity has negative
relationship with bank capital in emerging economies of
Asia. This means as liquidity increases from the required level of bank, capital decreases. Banks normally adjust
their capital by adjusting bank loans with liquidity. Due to
this reason, bank capital buffer increases. Higher bank
capital buffer provides support to absorb higher level of
losses occurred due to economic shocks. These findings
were given by researchers in last financial crisis. The
variable Z-Score is used for banks risk which has positive
relationship with bank capital in overall data. These
findings are consistent with regulatory hypothesis. The
firm specific control variables also depicted their relevance
and importance in this study. The variable market funding
and bank size have negative relationship with bank capital.
The economic variable gross domestic product has
negative and statistically significant relationship with bank
capital while rate of interest has positive relationship with
bank capital.
Table#2Panel Data Regression Results Large Banks
Description: Bank capital as measure bank equity to total
assets, Bank Liquidity calculated as liquid assets to total
assets. Interest rate is define as yearly change in interest
rate, bank size is measured as log to total assets. Where
market funding measured as total liabilities less total
deposits divided by total assets, Bank risk is measured as Z-
score calculated as equity ratio plus return on assets divided
by the variance of return on assets. In this model, we
developed linear regression panel corrected standard errors
instead of feasible generalized least squares
Table#3Panel Data Regression Results Medium banks
It contains the results for large size banks. The similar Table#3Panel Data Regression Results Medium banks model is applied to test the influence of banks liquidity and
Description: Bank capital as measure bank equity to total
banks risk on bank capital in larger size banks. The findings assets, Bank Liquidity calculated as liquid assets to total
depict that management of liquidity as measured by liquid assets. Interest rate is define as yearly change in interest
assets divided by total assets is irrelevant to bank capital. rate, bank size is measured as log to total assets. Where
The findings show positive relationship between bank risk market funding measured as total liabilities less total
and bank capital. The findings also depict that bank size has deposits divided by total assets, Bank risk is measured as Z-
negative relationship with bank capital. The firm specific score calculated as equity ratio plus return on assets divided
variable market funding is not significant while bank size by the variance of return on assets. In this model, we
has negative and statistically significant influence on bank developed linear regression panel corrected standard errors
capital. The economic variables gross domestic product is instead of feasible generalized least squares. negatively influencing bank capital in emerging economies of Asia. The variable interest is positive and has statistically significant connection with bank capital.
It contains the results of medium size banks. The findings
provide that liquidity has negative relationship with bank
capital in medium size banks. Theoretically as banks
increases loans, the risk weighted assets also increase and
proportion of required capital also increases. The findings
explore that medium banks are more sensitive with respect
to their liquidity level and capital ratio. These banks use other sources to adjust their capital by reducing cost and
increasing their profitability and reserves. The worth
noting thing is that medium banks are aggressive in use of
their liquidity on the cost of retained earnings. When banks
are involved in liquidity creation, which is a basic function
of banks. It leads to decrease in capital level due to increase
in risk weighted assets. The findings confirm when
liquidity decreases, the bank required capital increases.
The variable risk Z-score has positive relationship with
bank capital which means as banks increase their risky
assets, they also increase their capital. These findings are consistent with regulatory hypothesis. The results of firm
specific control variables also depict their relevance and
importance. The variables market funding and bank size
have negative relationship with bank capital. The
economic variables gross domestic product and interest
rate have negative influence on capital and also statistically
significant influence on bank capital in medium banks.
Table#4Panel Data Regression Results Small banks
Description: Bank capital as measure bank equity to total
assets, Bank Liquidity calculated as liquid assets to total
assets. Interest rate is define as yearly change in interest
rate, bank size is measured as log to total assets. Where
market funding measured as total liabilities less total
deposits divided by total assets, Bank risk is measured as Z-
score calculated as equity ratio plus return on assets divided
by the variance of return on assets. In this model, we
developed linear regression panel corrected standard errors
instead of feasible generalized least squares
It contains the results of medium size banks. The findings
provide that liquidity has negative relationship with bank
capital in medium size banks. Theoretically as banks
increases loans, the risk weighted assets also increase and
proportion of required capital also increases. The findings
explore that medium banks are more sensitive with respect
to their liquidity level and capital ratio. These banks use other sources to adjust their capital by reducing cost and
increasing their profitability and reserves. The worth
noting thing is that medium banks are aggressive in use of
their liquidity on the cost of retained earnings. When banks
are involved in liquidity creation, which is a basic function
of banks. It leads to decrease in capital level due to increase
in risk weighted assets. The findings confirm when
liquidity decreases, the bank required capital increases.
The variable risk Z-score has positive relationship with
bank capital which means as banks increase their risky
assets, they also increase their capital. These findings are consistent with regulatory hypothesis. The results of firm
Table#4Panel Data Regression Results Small banks
Description: Bank capital as measure bank equity to total
assets, Bank Liquidity calculated as liquid assets to total
assets. Interest rate is define as yearly change in interest
rate, bank size is measured as log to total assets. Where
market funding measured as total liabilities less total
deposits divided by total assets, Bank risk is measured as Z-
score calculated as equity ratio plus return on assets divided
by the variance of return on assets. In this model, we
developed linear regression panel corrected standard errors
instead of feasible generalized least squares
It contains results of small size banks. Results depict that
liquidity has positive relationship with bank capital while
this relationship is negative in case of medium size banks and large size banks. These findings provide new insights
to managers regarding banks liquidity and banks capital
adjustment according to size. The findings indicate the
importance of bank liquidity in small size banks, large size
banks and medium size banks in emerging economies of
Asian region. These findings provide information to
compare the two extremes for appropriate regulations. The
overall risk variable Z-Score has positive relationship with
bank capital and these findings are consistent with
“regulatory hypothesis”. The firm specific control
variables, bank size and market funding have negative
impact on bank capital. In addition, macroeconomic
Conclusion
The main objective of this study is to explore the impact of
bank liquidity and bank risk on bank capital in commercial
banks of emerging economies of Asia after financial crisis
2007-2008. The study provides new insights about bank
capital in emerging economies of Asia after the
development of Base-III. The results confirm the negative
relationship between bank liquidity and bank capital in
medium size banks and large size banks while bank
liquidity and bank capital of small size bank have positive
relationship. These results indicate that banks behavior is
not similar toward the relationship of bank capital and bank
liquidity in emerging economies of Asia. The findings of
this study suggest that there must be different regulations for different categories of banks. The policy makers should consider the needs and constraints of all types of banks in order to develop new regulations. The findings explain that
larger banks have no need to manage their liquidity to
adjust capital. The medium size banks liquidity has
negative impact on bank capital which suggests that
medium size banks remain at equilibrium level of their capital and medium size banks use liquidity as a tool to adjust their capital. This means that banks convert their loans into liquid assets to manage their required capital. With the increase in liquidity due to decrease in banks loans
causes to reduce banks risk weighted assets that leads to
increase in banks capital buffer. The liquidity of small
banks has positive influence on bank capital. The findings
show the banks risk has positive relationship with bank capital in all banks categories including large size banks, medium size banks and small size banks. These finding are consistent with regulatory hypothesis. The regulatory hypothesis suggests that banks should increase their capital
as their risk increases. The interest rate has positive
relationship with bank capital while market funding and
real gross domestic product have negative relationship
with bank capital. These findings are particularly
generalizable for post crisis period data ranging from 2011 to 2016.
Journal of Financial Economics, 105(1), 1-17.
Berger, A. N., & Bouwman, C. H. (2013). How does capital affect bank performance during financial crises?
Journal of Financial economics, 109(1), 146-176.
Bernanke, B. S., Lown, C. S., & Friedman, B. M. (1991).
References
Boyd, J. H., & Graham, S. L. (1988). The Profitability And Risk Effects Of Allowing Bank Holding. Federal Reserve Bank of Minneapolis. Quarterly ReviewFederal Reserve Bank of Minneapolis, 12(2), 3.
Brewer, E., & Lee, C. F. (1986). How the market judges
bank risk. Federal Reserve Bank of Chicago
Economic Perspectives, 10(6), 25-31.
Carmona, G. (2007). Bank failures caused by Large withdrawals: An explanation based purely on liquidity. Journal of Mathematical Economics, 43(7-8), 818-841.
Chiaramonte, L., & Casu, B. (2017). Capital and liquidity
ratios and financial distress. Evidence from the
European banking industry. The British
Accounting Review, 49(2), 138-161.
Cole, R. A., & Gunther, J. W. (1995). Separating the likelihood and timing of bank failure. Journal of
Banking & Finance, 19(6), 1073-1089.
Cole, R. A., & Gunther, J. W. (1995). Separating the likelihood and timing of bank failure. Journal of
References Banking & Finance, 19(6), 1073-1089.
Agusman, A., Monroe, G. S., Gasbarro, D., & Zumwalt, J. Cole, R. A., & White, L. J. (2012). Déjà vu all over again:
K. (2008). Accounting and capital market The causes of US commercial bank failures this
measures of risk: Evidence from Asian banks time around. Journal of Financial Services
during 1998–2003. Journal of Banking & Finance, Research, 42(1-2), 5-29.
32(4), 480-488. Cornett, M. M., McNutt, J. J., Strahan, P. E., & Tehranian,
Altunbas, Y., Manganelli, S., & Marques-Ibanez, D. H. (2011). Liquidity risk management and credit
(2017). Realized bank risk during the Great supply in the financial crisis. Journal of Financial
Recession. Journal of Financial Intermediation, economics, 101(2), 297-312.
32, 29-44. Delis, M. D., & Staikouras, P. K. (2011). Supervisory
Asli Demirgüç-Kunt, A., & Kane, E. J. (2002). Deposit effectiveness and bank risk. Review of Finance,
insurance around the globe: Where does it work? 15(3), 511-543.
Journal of Economic Perspectives, 16(2), 175-195. Demirgüç-Kunt, A., & Huizinga, H. (2000). Financial
Barry, T. A., Lepetit, L., & Tarazi, A. (2011). Ownership structure and bank profitability.
structure and risk in publicly held and privately owned banks. Journal of Banking & Finance, 35(5), 1327-1340. DeYoung, R. (2003). De novo bank exit. Journal of Money, Credit, and Banking, 35(5), 711-728.
Barth, J. R., Caprio, G., & Levine, R. (2008). Bank regulations are changing: for better or worse?
Comparative Economic Studies, 50(4), 537-563. DeYoung, R., & Jang, K. Y. (2016). Do banks actively manage their liquidity? Journal of Banking & Finance, 66, 143-161.
Beltratti, A., & Stulz, R. M. (2012). The credit crisis around the globe: Why did some banks perform better? Diamond, D. W., & Kashyap, A. K. (2016). Liquidity requirements, liquidity choice, and financial
stability Handbook of macroeconomics (Vol. 2, Kapan, M. T., & Minoiu, C. (2013). Balance sheet strength
pp. 2263-2303): Elsevier. and bank lending during the global financial crisis:
Distinguin, I., Roulet, C., & Tarazi, A. (2013). Bank International Monetary Fund.
regulatory capital and liquidity: Evidence from US Kashyap, A. K., & Stein, J. C. (2000). What do a million
and European publicly traded banks. Journal of observations on banks say about the transmission
Banking & Finance, 37(9), 3295-3317. of monetary policy? American Economic Review,
Francis, W. B., & Osborne, M. (2012). Capital 90(3), 407-428.
requirements and bank behavior in the UK: Are Kim, D., & Santomero, A. M. (1988). Risk in banking and
there lessons for international capital standards? capital regulation. The Journal of Finance, 43(5),
Journal of Banking & Finance, 36(3), 803-816. 1219-1233.
Gambacorta, L., & Mistrulli, P. E. (2004). Does bank Kim, D., & Sohn, W. (2017). The effect of bank capital on
capital affect lending behavior? Journal of lending: Does liquidity matter? Journal of Banking
Financial Intermediation, 13(4), 436-457. & Finance, 77, 95-107.
Haneef, R., Archer, S., & Karim, R. A. A. (2018). Laeven, L., & Levine, R. (2009). Bank governance,
Regulatory Aspects of the Islamic Capital Market regulation and risk taking. Journal of Financial
and Basel III Requirements–Shari'ah-Compliant Economics, 93(2), 259-275.
Bank Capital Instruments. Islamic Capital Markets Pettway, R. H. (1976). Market tests of capital adequacy of
and Products: Managing Capital and Liquidity large commercial banks. The Journal of Finance,
Requirements Under Basel III, 221-236. 31(3), 865-875.
Hugonnier, J., & Morellec, E. (2017). Bank capital, liquid Raz, A. F. (2018). Risk and capital in Indonesian large
reserves, and insolvency risk. Journal of Financial banks. Journal of Financial Economic Policy(just-
Economics, 125(2), 266-285. accepted), 00-00.
Iannotta, G., Nocera, G., & Sironi, A. (2007). Ownership Repullo, R. (2004). Capital requirements, market power,
structure, risk and performance in the European and risk-taking in banking. Journal of Financial
banking industry. Journal of Banking & Finance, 31(7), 2127-2149. Intermediation, 13(2), 156-182.
Rime, B. (2001). Capital requirements and bank behaviour:
Jacques, K., & Nigro, P. (1997). Risk-based capital, Empirical evidence for Switzerland. Journal of
portfolio risk, and bank capital: A simultaneous equations approach. Journal of Economics and Banking & Finance, 25(4), 789-805.
business, 49(6), 533-547. Shrieves, R. E., & Dahl, D. (1992). The relationship between risk and capital in commercial banks.
Jahankhani, A., & Lynge, M. J. (1979). Commercial bank financial policies and their impact on market- Journal of Banking & Finance, 16(2), 439-457.
determined measures of risk/BEBR No. 556. Wheelock, D. C., & Wilson, P. W. (2000). Why do banks
