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How Do Capital Ratios Affect Bank Risk-Taking: New Evidence From the United States:

How Do Capital Ratios Affect Bank Risk-Taking: New Evidence From the United States: This study aims to examine the impact of different capital ratios on Non-Performing loans, Loan Loss Reserves, and Risk- Weighted Assets by studying large commercial banks of the United States. The study employed a two-step system generalized method of movement (GMM) approach by collecting the data over the period ranging from 2002 to 2018. The study finds that using Non-Performing loans and Loan Loss Reserves as a proxy for risk, results support moral hazard hypothesis theory, whereas the results support regulatory hypothesis theory when Risk-Weighted Assets is used as a proxy for risk. The results confirm that the influence of high-quality capital on Non-Performing loans, Loan Loss Reserves, and Risk-Weighted Assets is substantial. The distinctive signs of Non-Performing loans, Loan Loss Reserves, and Risk-Weighted Assets have indications for policymakers. The results are intimate for formulating new guidelines regarding risk mitigation to recognize Non-Performing loans and Loan Loss Reserves and the Risk-Weighted Assets for better results. JEL Classification: G21, G28, G29 Keywords bank capital ratios, non-performing loans, loan loss reserves, risk-weighted assets base ratio of 6% of Tier 1 capital to RWAs; and (c) Tier 1 Introduction Common equity ratio (T1CER), which requires a base ratio Since the order of the Basel-I Accord in 1988, followed by of 4.5% of RWAs. In this paper, we look at the effect of capi- Basel II in 2004 and most as of late the Basel III Accord in tal on the risk of large commercial banks. In particular, we 2010, the definitions of bank capital has advanced signifi- thoroughly scrutinize different definitions of bank capital cantly in an exertion to improve banking framework sound- (risk-based and non-risk-based capital and capital buffer ness and fill the harmonization hole that had caused past ratios). Capital fills in as a security component to absorb monetary crises. The 2007 to 2008 economic crisis accu- losses. In this way, it ought to be quite compelling to experts rately interpreted that capital provisions alone are lacking to and regulators in the United States because, from one per- forestall bank distresses. spective, a holding of a higher capital ratio intensifies the The weaknesses of prior Basel Accords provoked the bank’s loss absorption potential. The holding of excessive Basel Committee on Banking and Supervision (BCBS) to funds skewed economic enlargement and increases bank risk execute one more arrangement of rules for banking guide- (Bitar et al., 2018). In recently published studies, scholars lines. The BCBS’s endeavors brought about the Basel III explore the importance of capital ratios and their impact on rules (Basel III recommended conservative buffer 2.5% of banks risk-taking (Bitar et al., 2016, 2018; Brandao-Marques risk-weighted assets (RWAs) to enhance the loss absorption et al., 2020; Cohen & Scatigna, 2016; Ding & Sickles, 2018, capacity during stressful conditions. For detail see: http:// www.bis.org/bcbs/basel3.htm.), which expect banks to be increasingly thorough by rethinking capital structure. The The University of Lahore, Pakistan Basel III Accord intends to improve the quality and incre- Air University, Islamabad, Pakistan ment the size of a bank’s core capital base. Likewise, the new 3 University of Malaya, Kuala Lumpur, Malaysia regulations consider three related ratios of capital as funda- Corresponding Author: mentals. (a) the first is a risk-based capital ratio (TRBCR), Faisal Abbas, Department of Accounting & Finance, The University of which requires a base ratio of 8% against risk-weighted- Lahore, Lahore PK-PB 46000, Pakistan. assets; (b) the Tier 1 capital ratio (T1RBR), which requires a Email: [email protected] Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open 2019). The contradictory conclusion of the recent literature capital buffer ratios are significant for policymakers to know motivates us to explore and fill this gap. whether the recommendations for holding of higher capital Most of the previous studies use basic definition of bank are useful. In the methodological context, the study contrib- capital (equity to total assets) to interpret the impact of capital utes to the empirical literature and uses a two-step system ratio on risk-taking (Shrieves & Dahl, 1992). This study use GMM approach, which indicates that the results hold when and analyze the newly evolved definitions of bank capital like we apply different ratios of bank capital. The rest of the study total risk-based capital ratio, tier one risk-based capital ratio, is structured as follows: The next section contains a literature tier-one risk-based Common equity capital ratio, total risk- review and hypotheses development. The third segment is based capital buffer ratio, tier-one risk-based capital buffer consists of data sources and econometric techniques. The ratio, and Common equity buffer ratio. The measurement of fourth section reports the results and discussion, and the final risk using the bank balance sheet remains contradictory. section is about the conclusion and policy implications. Some studies use loan loss reserves, and others apply for non- performing loans and RWAs. We use the above three defini- Literature Review and Hypotheses tions of bank risk in this study for the robustness of the Development relationship. This methodology permits us to explore which types of capital definitions are most useful to drive which The question of how different bank capital ratios affect loan kind of risk measure of large commercial banks in the United loss reserves, non-performing loans and RWAs is far from States. The study starts by investigating which risk-based being settled. The financial theories document various esti- capital ratios are more useful for which type of bank risk mea- mations about the influence of capital ratios on bank portfo- sure (loan loss reserves, non-performing loans, and RWAs). lio risk-taking. In recently published studies, scholars explore Then followed the non-risk-based capital ratios connection the importance of capital ratios and their impact on banks’ with loan loss reserves, non-performing loans, and RWAs. risk-taking (Bitar et al., 2016, 2018; Brandao-Marques et al., The previous theoretical and empirical literature casts suspi- 2020; Cohen & Scatigna, 2016; Ding & Sickles, 2018, 2019). cions over the adequacy of risk-based capital ratios (Dermine, The contradictory conclusion of the recent literature moti- 2015). We explore whether high-quality capital, such as tier vates to explore and fill this gap. Anginer and Demirguc- one risk-based capital ratio, is more useful to absorb losses Kunt (2014) argue that the purpose behind high capital ratios than non-risk based capital ratios (Distinguin et al., 2013). is to increase their resistance against unexpected economic The study also explores whether tier one common equity risk- shocks and meet the demand for deposits withdrawals. They based capital buffer ratio is more useful to absorb losses than claim that the presence of a higher capital buffer ratio tier one risk-based capital buffer and total-risk-based capital enhances the owner’s investment behavior. buffer ratios. The findings of this study have significant Jacques and Nigro (1997) opine that increasing the capital implications for experts and decision-makers for the develop- against RWAs may reduce bank risk-taking; other factors held ment of capital guidelines for the financial system stability in constant. Correspondingly, Aggarwal and Jacques (1998) the United States. explain that the holding of a higher capital ratio than regula- The study contributes both theoretically and methodolog- tory capital ratios protects from distress in crisis conditions. ically in the following ways to the existing literature. In pre- Berger and Bouwman (2013) uncover that the presence of vious studies, some researchers advocated the use of RWAs capital is favorable to small banks in adverse economic con- as a portfolio risk proxy relative to non-performing loans ditions. Tan and Floros (2013), Lee and Hsieh (2013) reveal (NPLs) and loan loss reserves (LLRs). The other justifies in an inverse connection between various capital ratios and bank favor of LLR and NPL against RWA. We here contribute to risk-taking. Eventually, more efficient bank management the existing literature by using these three risk proxies at a may play a crucial part in adjusting depositors and owners’ time to resolve the contradiction of superiority. The study benefits in decreasing agency issues. Abusharba et al. (2013) used three risk measures on a fundamental information basis, demonstrate an increase in non-performing loans leads to a includes NPL, LLR, and RWA. In the theoretical context, the decrease in the bank capital adequacy ratio. Konishi and study contributes to the debate of the impact of different cap- Yasuda (2004) find that regulatory capital decreases the risk- ital definitions on a various measure of bank portfolio risk taking of commercial banks. This argument favors the nega- (Altunbas et al., 2007; Bitar et al., 2018; Ding & Sickles, tive relationship between bank capital and risk-taking. 2018; Jacques & Nigro, 1997; Lee & Hsieh, 2013; Shrieves & Dahl, 1992) by investigating the impact of different risk- Hypothesis 1: There is a negative relationship between based capital ratios, non-risk-based capital ratios, and capital the risk-taking of large commercial banks of the United buffer ratios on various proxies of bank risk. The findings of States and regulatory capital ratios. this study allow regulators and governments to control which types of capital ratios are more considerable to reduce the The corresponding set of arguments posits that banks take risk of commercial banks. The outcomes obtained from the higher risks to maximize the owner’s wealth at the cost of comparison of risk-based capital, non-risk-based capital, and depositor’s money. The deposit insurance plan motivates Abbas et al. 3 bank managers to engage in risky projects. The depositor’s concluded in their study conducted in Europe that banks usually money remains secure due to insurance guarantees. The the- create a higher amount of buffer due to the higher cost of capital ory of too-big-to-fail follows a similar argument in which the adjustment. They found an inverse relationship between the systemic banks involve in excessive risk-taking due to gov- economic cycle and capital buffers for large commercial and for ernment bailout options (Kim & Santomero, 1988). These saving banks. They also revealed that small banks incline to arguments favor the “regulatory hypothesis” theory, which increase their capital buffer during economic booms. Jokipii suggests an increase in capital ratio with the increase in a and Milne (2011) found that bank capital and risk have a posi- risky portfolio. Koehn and Santomero (1980) conclude that tive and causal relationship in the U.S. banking sector. They the rise in capital leads to an increase in banks’ risk. Avery indicated that banks usually use capital buffers amount to adjust and Berger (1991) reveal that risk-based capital and bank capital and risk. This relationship signifies that banks increase risk-taking move in the same direction. J. Blum (1999) docu- their capitalization with the increase in their risk. Guidara et al. ments a positive relationship between an increase in bank (2013) explored the relationship between bank risk and bank capital and portfolio risk in the short run. In a specific con- capital buffers using the data of commercial banks of Canada, text, Iannotta et al. (2007) provide that there is a positive and they found that higher capitalized banks follow the market relationship between loan loss provision and bank capital. In discipline. Anginer et al. (2014) showed that higher capital pro- a most recent study of Bitar et al. (2018) validate a positive vides greater power to resist earning shocks. They demonstrated relationship between loan loss reserves and non-risk-based that higher capital buffers provide more confidence to stock- capital ratios. Altunbas et al. (2007) uncover that there is a holders to make full investment choices. Examples of other positive connection between loan loss reserves and capital studies conducted to explore the bank buffers are Ayuso et al. ratio. Likewise, we develop the following hypothesis: (2004), Fonseca & González (2010), and Valencia & Bolaños (2018). Hypothesis 2: There is a positive relationship between the risk-taking of large commercial banks of the United Hypothesis 4: There is a positive relationship between States and capital ratios. the risk-taking of large commercial banks of the United States and capital buffer ratios. In recent literature, various studies investigate the usefulness of risk-based and non-risk-based capital ratios. Most of those Data and Econometric Model studies’ findings cast doubts on the effectiveness of risk- based capital ratios to reduce risk. J. M. Blum (2008) con- In the data structure of the present study, the Federal Deposit cludes that the own determination of banks’ risk exposure Insurance Corporation (FDIC) institutional directory was provides an incentive to express lower risk to limit regula- used for extracting detailed information about the financial tory capital requirement. These conditions favor involving in system necessary to analyze the data in the long-run accord- riskier activities. Dermine (2015) suggests that there should ing to the reports of FDIC call/TFR, which is updated quar- be a complementary risk-based capital in the real sense to terly by FDIC. The annual dataset provided for financial avoid the problem of riskiness. Cathcart et al. (2015) report institutions and covered the long period of the current that banks with higher risk-based capital ratio than regula- research study between 2002 and 2018. The sample of the tory requirements are unable to survive in financial crisis. present research study is balanced to comparable panel data Haldane and Madouros (2012), the study provides inconclu- containing insured commercial banks of the United States, as sive findings of the effectiveness of risk-based capital ratios described in FDIC reports. to influence bank risk-taking. Rime (2001) concludes that Furthermore, the assets are also based on a consolidated the relationship between bank risk-taking and regulatory theme. There were many banks in nearly 1806 in the men- capital is not clear and conspicuous. Bitar et al. (2018) reveal tioned list on December 31, 2018, which were listed by FDIC. that risk-based capital ratios fail to reduce portfolio risk by However, for appropriate and reliable data analysis, the inclu- using the sample of Organisation for Economic Co-operation sion of the study sample units was based on the following cri- and Development (OECD) countries banks. In a specific teria: the listed banks should have been active on the reported context, they find that when RWAs measure risk, the influ- date. There must not be any missing observations for any spe- ence of capital ratios is insignificant. In the context of the cific study variables of at least 2 years in the studied period. above debate, the hypothesis is: The total assets of banks must be higher than $300 million on the December 31, 2018. After filtration of properly used crite- Hypothesis 3: There is no relationship between the risk- ria, there were 942 banks selected for the study sample size. taking of large commercial banks of the United States and capital ratios. Dependent Variable Definitions Different researchers have examined the relationship between Bank risk. In previous studies, some researchers favor the use capital buffers and bank risk. Jokipii and Milne (2008) of RWA as a portfolio risk proxy superior to NPL and LLR. 4 SAGE Open The other justifies in favor of LLR and NPL against RWA. Nguyen (2018). It indicates that higher diversification of We here use these three risk proxies (see Table 1) at a time to funding may encourage to take a greater risk. The income resolve the contradiction of superiority. The study used three diversification measure is consistent with Nguyen (2018). risk measures on a fundamental information basis, includes The more diversified banks have more options to earn profits NPL, LLR, and RWA. The NPL is used by Ding and Sickles which may encourage to take higher risk to increase returns. (2019), Jiang et al. (2020), Ozili (2019a, 2019b), Shim (2013), and Shrieves and Dahl (1992). The RWA is used by Model Specifications Ding and Sickles (2019), Jacques and Nigro (1997), and Shrieves and Dahl (1992). The LLR is employed by Altun- The dynamic model is applied in this study. There are several bas et al. (2007), Bitar et al. (2018), and Lee and Hsieh reasons for applying GMM (Generalized method of move- (2013). The reason for using these three proxies is to find out ment)., it controls the endogeneity of the lagged reliant vari- the robustness of relationships in the present conditions of able in a dynamic setting. GMM controls the measurement the United States. Besides, the purpose includes the mea- error problem, reduces omitted bias issues, and controls the sures and risk theories clarification and association and unobserved heterogeneity problem in panels. The means of reexaminations. the dynamic panel regression models have p lags of the dependent variable and comprise unobserved panel effects, Capital ratios. Bank capital ratio is an independent variable. which may be fixed or random. The correlation between We use several capital ratios, including risk-based capital unknown panel effects and the lagged value of dependent ratios and non-risk-based capital ratios reported in Table 1. variables makes the estimators inconsistent. Arellano and The study also uses the capital buffer ratios for the robust- Bond (1991) provide a method called the generalized method ness and more in-depth understanding between the relation- of moments as the solution to make the estimators consistent. ship of different risk measures and real increase and decrease They argue that the use of a one-step and two-step approach in capital ratios. The rates taken for capital are consistent in large instrument matrix and robust standard errors for a with many studies (Abbas, Butt, et al., 2019; Abbas & one-step GMM approach are to be found severely biased. To Masood, 2020b; Bitar et al., 2018; Jacques & Nigro, 1997; overcome this severe biasness, Windmeijer (2005) presented Jiang et al., 2020; Jokipii & Milne, 2011). a robust estimator for the two-step GMM approach, which is more efficient and is a biased-free method to calculate esti- Control variables definitions. The profitability is measure with mators. Later, Blundell and Bond (1998) worked on it fur- net income to total assets similar to Lee and Hsieh (2013) ther, and their findings have been used by various studies in and Shrieves and Dahl (1992). The higher profits required the field of banking (Fiordelisi et al., 2011; Lee & Hsieh, higher risk due to this assumption; there may exist a positive 2013; Tan, 2016; Tran et al., 2016). we use the two-step sys- relationship between profitability and NPLs. The negative tem GMM in this study, as its more efficient than the one- relationship is also not less applied. We are implying that the step system GMM, and two-step system GMM can capture increase in NPL leads to a decrease in bank profits (Ozili, the maximum values to calculate the estimators. 2019b). The loan growth ratio and risk are related, and the proxy measure is consistent with (Abbas, Butt, et al., 2019; System GMM Model Specifications Abbas, Iqbal, & Aziz, 2019). We control the bank size by taking the natural logarithm of bank total assets similar to The basic model of the system GMM approach is the follow- many studies (Ding & Sickles, 2018; Lee & Hsieh, 2013; ing form: Shrieves & Dahl, 1992). The size is used for economies of scale as a whole and for “too big to fail” hypothesis for (1) lnYY =+ φβX ’ ++ ηε () it ,, it−1 it ,, ii t banks. The liquidity always remains crucial in the banking It is assumed that the above specification is a random walk industry to meet the demand for depositors. We followed the equation, and the dependent variables are persistent. measurement of liquid assets to total assets like Ding and Accordingly, the results of difference GMM produce an ineffi- Sickles (2018) and Jiang et al. (2020). The market share role cient and biased parameter, particularly in finite samples. The is reflected in the behavior of commercial banks for risk- empirical literature explains that the bias and poor performance taking (Jiang et al., 2020). The market share represents the of difference GMM are due to poor instruments (Blundell & market power of the banks. The cost to income ratio is used Bond, 1998). The system GMM uses one equation in levels for bank efficiency control, which is consistent with Abbas, form with the first differences as instruments, whereas the sec- Butt, et al. (2019) and Jiang et al. (2020). There are several ond equation is used in the first differences form with levels as aspects which explore the connection of bank efficiency and instruments. The system GMM approach implicates a greater proxies of risk (NPL, LLR, RWA). Efficient banks control number of instruments. Still, Monte Carlo evidence recom- the problem of higher NPL and riskiness. The funding diver- sification is relevant to risk. Here we use the proxy similar to mends that where the period is limited, and the dependent Abbas et al. 5 variable is found to be persistent, the use of system GMM there are no high correlations exits among explanatory. The low correlation also indicates that there is no problem of reduces the bias of a small sample. There is another feature of multicollinearity. system GMM; if there are autocorrelation and heteroscedastic- We first analyze the impact of different capital ratios on ity in the data, a two-step system GMM should be applied by NPL for the large commercial banks sample applying a two- developing a weighting matrix using residuals from the first step system GMM approach. A two-step system GMM step. It is also argued that in limited samples, the standard approach is employed to provide consistent estimates. The errors were found to be downward biased. In this situation, Hansen test is reported for the confirmation and validation of researchers recommend applying the robust standard error instruments used. The Sargen test statistics are presented for approach developed by Windmeijer (2005), which corrects the the over-identification and autocorrelations check among sample bias. residuals (Ding & Sickles, 2018). In the first turn, we will Difference or system GMM to be used. The basic model address the influence of total capital ratio (TCAPR) and total equation: risk-based capital ratio (RBCR) on NPL, LLR, and RWA in Tables 4 to 6, respectively. For the effect of total risk-based (2) lnYY =+ φβX ’ ++ ηε capital ratio and total capital ratio on NPLs coefficients, see () it ,, it−1 it ,, ii t Table 4 Columns 1 and 4. What is better to apply for consistent and unbiased parame- The empirical findings explore that there is a negative ters? The rule of thumb provided by Bond et al. (2001) sug- and statistically significant relationship between bank capi- gests the ordinary least squares (OLS) is to be applied first, tal ratios and NPL. In the context of risk theory, higher and the least-squares dummy variables (LSDV) method is NPLs show higher risk, an antagonistic relationship with used second to find out the estimators. The panel OLS esti- capital ratios are similar to the argument of Bitar et al. mator ϕ should be the upper-bound estimate, whereas the (2018) and Jacques and Nigro (1997). The negative relation- fixed effects estimator is considered a lower-bound estima- ship between capital ratios and NPL is contradicting the tor. The decision is taken based on difference GMM esti- findings of Shrieves and Dahl (1992) but consistent with mates; if the estimates are close to or below the estimators of (Jiang et al., 2020). There are several justifications for the the fixed effects method, the former estimators are consid- disagreement between the present study results and Shrieves ered to be a downward biased due to the weak instruments, and Dahl (1992) study. First, there were different economic and system GMM is to be preferred as the best choice to conditions in the 1990s, and now the economic conditions apply instead of difference GMM. The following model is entirely change. The second reason for the difference is the used in this study under the condition elaborated above: present strict regulations and monitoring of commercial banks compared to the 1990s. The negative relationship (3) YY =+ αβ ++ XZ βε + supports the regulations for capital. The higher regulatory it ,, it−11 it ,, 2i t capital experience fewer NPLs were consistent with the Here, the Y is a dependent variable, which is bank risk (NPL, argument of Ozili (2019a). One of the justifications for the LLR, RWA) in this study i, represents banks and t shows negative association between NPL and capital ratios indi- period, is lagged value of risk. Unknown parameters t−1 cates that banks with lower capital take higher risk, which X is the independent variable, which is capital in this case upsurges the chances of NPL consistent with Klein (2013). where it may be total capital ratio, total risk-based capital The banks having a higher capital ratio reports lower NPL, ratio, and capital buffer ratio based on the simulation under as argued by Boudriga et al. (2009). The empirical findings observation. Z shows the list of control variables, and ε is an of the present study are supporting the moral hazard hypoth- error term. esis theory. The moral hazard theory suggests an inverse relationship between capital and portfolio risk. The negative sign between NPL and capital ratios indicates that banks Results and Discussion hold lower capital when NPL is augmented, similar to the Table 2 contains the summary statistics information for argument of Altunbas et al. (2007) and Ding and Sickles dependent, independent, and control variables. The details (2018). There is an inverse relationship between bank prof- include the number of observations, average value, and itability and bank NPLs. The simple theory is that NPL gen- standard deviation from the mean, minimum, and maxi- erates lower income for lending firms due to that the profit mum level of proxies. The information explores that the remain lowers. The lower profits due to the charge-off NPL reported values for proxies are in line with the previous may decrease bank capital ratios, as argued by Ozili (2019b). studies (Abbas & Masood, 2020a; Ding & Sickles, 2019). The findings conclude that the availability of higher liquid- Table 3 reports the correlation between explanatory vari- ity increases the ratio of NPL. The role of loan growth is ables. The sign and significance are as per the economic decisive; the findings of NPL is consistent with the results theory and consistent with (Abbas & Masood, 2020a; Ding of Ozili (2019b). The increase in bank size causes a decrease & Sickles, 2019). The correlations matrix confirms that in the NPL. The one possible justification of bank size and 6 SAGE Open Table 1. Variables and Data Sources. Variables names Measurement Reference Dependent Variables RWATA Risk-Weighted Assets to total assets (Abbas, Butt, et al., 2019) LLRGL Loan Loss Reserves to gross loans (Bitar et al., 2018) NPLTA Non-performing loans to total assets (Ding & Sickles, 2019) Independent Variables TCAPR Total Bank equity to total assets (Lee & Hsieh, 2013) TITA Tier I Capital to Total Assets (Bitar et al., 2018) TIRBR Tier I capital to risk-weighted assets (Bitar et al., 2018) CERBR Common Equity to risk-weighted assets (Bitar et al., 2018) RBCR Risk-Based Capital/Risk-weighted Assets (Bitar et al., 2018) BRBCR Actual Risk-based Capital less 8% (Guidara et al., 2013) TIRBR Tier I risk-based ratio less 6% (Abbas, Butt, et al., 2019) CEBR Common equity risk-based ratio less 4.5% (Abbas, Butt, et al., 2019) ROA Net Income to Total Assets (Guidara et al., 2013) LIQ The ratio of liquid assets to total assets (Abbas, Butt, et al., 2019) LG Yearly change in loans (Abbas, Butt, et al., 2019) BE Operating expenses to total asset (Abbas, Butt, et al., 2019) ID 1-(net income-operating income)/operating income (Nguyen, 2018) FD 1-((equity/total)2+(subordinate debt/ total)2+(deposits/total)2 (Nguyen, 2018) +(short term fund/total)2) MP Total bank deposits to total industrial assets (Jiang et al., 2020) Size Natural Log of total assets (Shrieves & Dahl, 1992) Note. TCAPR = total Capital ratio; CERBR = common equity risk-based ratio; CEBR = common equity buffer ratio; LIQ = liquidity; LG = loan growth; BE = bank efficiency; ID = income diversification; FD = funding diversity; MP = market power. Table 2. Descriptive Statistics. Variable Obs. M SD Minimum Maximum Risk-weighted assets/TA (RWATA) 14,944 0.67 0.43 0.52 0.84 Loan loss reserves/GL (LLRGL) 14,944 0.009 0.003 0.004 0.018 Non-performing loans/TA (NPLTA) 14,944 0.104 0.022 0.073 0.157 Risk-based capital ratio (TRBCR) 14,944 0.141 0.027 0.108 0.192 Total capital ratio (TCAPR) 14,944 0.102 0.018 0.078 0.136 Capital buffer ratio (BRBCR) 14,944 0.059 0.021 0.033 0.092 Tier-I capital/RWA (TIRBR) 14,944 0.128 0.02 0.104 0.157 Tier-I buffer ratio (TIBR) 14,944 0.066 0.019 0.043 0.095 Common equity risk-based ratio (CERBR) 14,944 0.126 0.021 0.102 0.157 Tier-I capital/TA (TITA) 14,944 0.093 0.015 0.073 0.121 Common equity buffer ratio (CEBR) 14,944 0.082 0.025 0.053 0.123 Profitability (ROA) 14,944 0.01 0.005 −0.001 0.02 Liquidity (LIQ) 14,944 0.048 0.027 0.021 0.095 Loan growth (LG) 14,944 0.666 0.113 0.455 0.824 Income diversification (ID) 14,944 0.463 0.098 0.262 0.593 Bank efficiency (BE) 14,944 3.048 1.756 0.906 6.852 Bank size 14,944 13.554 0.95 12.259 15.368 Funding diversity (FD) 14,944 0.555 0.011 0.539 0.581 Market power (MP) 14,944 0.139 0.271 −0.171 2.909 Source. (Authors calculations using Stata Output). Note. TA = total assets; GL = gross loans; TIRBR = Tier I risk-based ratio. NPL is that larger banks tend to take a higher risk, as argued positively connected. The market share and NPL are nega- by Ding and Sickles (2018). Funding diversity and NPL are tively related. Abbas et al. 7 Table 3. Matrix of Correlations. Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) LLRGL 1.000 RBCR −.052 1.000 ROA −.082 .076 1.000 LIQ .101 .185 −.090 1.000 LG .290 −.450 .016 −.179 1.000 ID −.168 .087 .583 .034 −.114 1.000 BE .022 −.007 −.489 .100 −.015 −.219 1.000 Size .016 −.107 −.017 −.057 −.028 .310 −.034 1.000 FD .017 .404 .084 −.033 −.076 .109 −.056 .215 1.000 MP .003 −.022 .029 .030 −.081 .111 −.024 .098 .068 1.000 Source. (Authors calculations using ‘Stata’s’ Output). Note. LLRGL = loan loss reserves to gross loans; RBCR = risk-based capital/risk; LIQ = liquidity; LG = loan growth; ID = income diversification; BE = bank efficiency; FD = funding diversity; MP = market power. For the effect of total risk-based capital ratio and total coefficients see Table 6 Columns 1 and 4. The results show capital ratio on LLR coefficients, see Table 5 Columns 1 and that bank capital ratios and RWA are positively associated. 4, respectively. There is a negative relationship between cap- The relationship is weak and in line with the recent develop- ital ratios and LLR. The negative relationship between risk- ments of (Bitar et al., 2018). One more crucial explanation is taking and capital ratios are in line with the conclusions of not less compelling between RWA and capital ratios. The many studies (Altunbas et al., 2007; Bitar et al., 2018; more complicated regulations for bank capital ratios permit Jacques & Nigro, 1997). The one possible justification for an to manipulate their risky portfolio for the adjustment of inverse relationship between capital ratios and LLR, indicat- required capital. Due to this reason, the bank’s actual risk ing an aggressive reserve policy based on moral hazard exposure does not expose in line with the studies of Cathcart hypothesis theory, and these results are contrary to the find- et al. (2015) and Dermine (2015). The positive relationship is ings of Altunbas et al. (2007). One more channel for this con- favoring the regulatory hypothesis theory. The results show nection may be due to an increase in LLR; the profitability of that profitability, loan growth, income diversity, and market the bank decreases, which may cause to reduce the capital share push forward to increase the RWA. Liquidity and ratios. The general debate provides that higher LLR indicates RWAs have a negative relationship. the higher expected risk in a portfolio. In this regard, an inverse relationship is supported by Aggarwal and Jacques Robustness (1998) and Jacques and Nigro (1997). In the specific context, higher regulatory capital experience less LLR, consistent In this part, we discuss the impact of tier-one risk-based capi- with the argument of Ozili (2019a). One of the reasons for a tal ratio, common equity risk-based capital ratio, tier one negative relationship between LLR and capital ratios indi- capital ratio on NPL, LLR, and RWA. In the first turn, we use cates that banks with lower capital take higher risk, which to test the impact of the tier-I risk-based capital ratio on NPL. upsurges the chances of LLR consistent with Klein (2013). The relationship between the tier-I risk-based capital ratio The banks having a higher capital ratio reports lower LLR, as and NPL is negative. In absolute terms, the coefficient is argued by Boudriga et al. (2009). The negative relationship more substantial concerning the total risk-based capital ratio. is consistent with moral hazard theory and supported by The impact of tier one risk-based common equity ratio is many studies (Aggarwal & Jacques, 1998; Altunbas et al., more significant than the tier one risk-based capital ratio. 2007; Jacques & Nigro, 1997). The findings provide economic sense to understand this con- The results indicate that an increase in LLR pushes banks’ nection. It means higher the quality of capital and lowers the profitability downward (other things held equal). The NPLs. For the robustness of the non-risk-based capital ratio, increase in liquid assets increases the loan growth, which we use tier one capital ratio, and results are consistent con- leads to moving LLR of banks upward. Income diversity cerning sign and significance. The similar capital ratios are provides excessive profits than lending due to that the used to test the robustness of LLR and RWA. The findings demand for LLR decreased. The improvement in bank effi- remain consistent and confirm that LLR is favoring the moral ciency decreases the proportion of LLR. The funding diver- hazard hypothesis, and RWA supports the regulatory hypoth- sity and LLR have a negative relationship. The market share esis theory. The study further uses the capital buffer ratios for has a positive impact on LLR. For the effect of total risk- a deeper understanding of the relationship. Table 7 shows the based capital ratio and total capital ratio on RWA, results of the impact of total capital buffer ratio, tier one 8 SAGE Open Table 4. Impact of Capital Ratios on NPLs. (1) (2) (3) (5) (6) Variables NPL NPL NPL NPL NPL L.NPL 0.759*** 0.537*** 0.627*** 0.565*** 0.440*** (0.061) (0.092) (0.109) (0.102) (0.085) RBCR −0.364*** (0.038) TIRBR −0.738*** (0.090) CERBR −0.919*** (0.136) TCAPR −1.404*** (0.206) TITA −0.820*** (0.092) ROA −0.005 −0.085** 0.0681 −0.140*** −0.096*** (0.031) (0.039) (0.050) (0.053) (0.036) LIQ 0.046*** 0.089*** 0.100*** 0.085*** 0.087*** (0.009) (0.016) (0.021) (0.017) (0.014) LG −0.018*** −0.017** −0.034*** 0.0356*** 0.0476*** (0.005) (0.008) (0.010) (0.013) (0.011) ID −0.003 −0.001 −0.003 0.002 0.001 (0.002) (0.004) (0.004) (0.005) (0.003) BE 0.001** 0.001 0.001 −0.001 0.002 (0.001) (0.005) (0.001) (0.001) (0.001) Size −0.002*** −0.003*** −0.005*** −0.001** −0.003*** (0.003) (0.001) (0.001) (0.001) (0.001) FD 0.386*** 0.620*** 0.762*** 2.008*** 0.774*** (0.046) (0.083) (0.120) (0.300) (0.091) MP −0.002* −0.004*** −0.005*** −0.002 −0.002 (0.000) (0.001) (0.002) (0.002) (0.001) Constant −0.126*** −0.191*** −0.222*** −0.977*** −0.346*** (0.019) (0.031) (0.042) (0.149) (0.043) Observations 14,944 14,928 14,944 14,928 14,928 Number of id 934 933 934 933 933 AR (2) 0.991 0.577 0.600 0.794 0.866 No. of Instruments 12 12 12 12 12 Hansen Statistics 0.592 0.993 0.471 0.006 0.129 Note. A two-step system GMM approach results and robust standard errors are reported. Robust Standard errors in parentheses. NPL = non-performing loan; RBCR = risk-based capital/risk; TIRBR = Tier I risk-based ratio; CERBR = common equity risk-based ratio; TCAPR = total capital ratio; TITA = Tier I capital to total assets; LIQ = liquidity; LG = loan growth; ID = income diversification; BE = bank efficiency; FD = funding diversity; MP = market power; GMM = generalized method of movement. *p < .1. **p < .05. ***p < .01. risk-based capital buffer ratio, and common equity buffer Conclusion ratio on RWA and LLR. The results confirm that RWA and In the present research, we explain the effect of capital ratios capital buffer ratios are positively associated, whereas the on bank risk-taking of large insured commercial banks in the relationship is negative in the case of LLR. The findings USA, covering the extensive period ranging from 2002 to explore for robustness checks are also in line with the previ- 2018. Our results indicate that the risk proxies LLR and NPL ous studies like (Abbas et al., 2020; Aggarwal & Jacques, are following the moral hazard hypothesis, and RWA is consis- 1998; Altunbas et al., 2007; Bitar et al., 2018; Ding & tent with regulatory theory. The study concludes an inverse Sickles, 2018; Jacques & Nigro, 1997; Jokipii & Milne, relationship between LLR, NPL, and bank capital ratios. On 2011; Lee & Hsieh, 2013; Rime, 2001; Shrieves & Dahl, the other, the hand RWA and capital ratio are positively 1992). Abbas et al. 9 Table 5. Impact of Capital Ratios on LLR. (1) (2) (3) (5) (6) Variables LLR LLR LLR LLR LLR L.LLR 0.649*** 0.648*** 0.651*** 0.641*** 0.643*** (0.029) (0.028) (0.029) (0.028) (0.028) RBCR −0.011** (0.005) TIRBR −0.014** (0.007) CERBR −0.016** (0.008) TCAPR −0.030* (0.015) TITA −0.016** (0.008) ROA −0.029*** −0.030*** −0.028*** −0.031*** −0.030*** (0.006) (0.006) (0.006) (0.007) (0.006) LIQ 0.008*** 0.008*** 0.008*** 0.008*** 0.008*** (0.001) (0.001) (0.001) (0.001) (0.001) LG 0.006*** 0.006*** 0.006*** 0.007*** 0.007*** (0.000) (0.000) (0.000) (0.001) (0.001) ID −0.003*** −0.003*** −0.003*** −0.003*** −0.003*** (0.000) (0.000) (0.000) (0.000) (0.000) BE −0.001*** −0.001*** −0.001*** −0.001*** −0.001*** (0.000) (0.000) (0.000) (0.000) (0.000) Size −0.005 −0.004 −0.005 −0.001 −0.002 (0.000) (0.000) (0.000) (0.000) (0.000) FD 0.019*** 0.019*** 0.020*** 0.051** 0.022*** (0.006) (0.006) (0.007) (0.022) (0.008) MP 0.002** 0.001* 0.001* 0.001* 0.001** (0.000) (0.000) (0.000) (0.000) (0.000) Constant −0.008*** −0.008*** −0.008*** −0.025** −0.011*** (0.003) (0.002) (0.002) (0.011) (0.004) Observations 14,880 14,880 14,880 14,880 14,880 Number of id 930 930 930 930 930 AR (2) 0.460 0.437 0.431 0.489 0.481 No. of Ins. 12 12 12 12 12 Hansen Statistics 0.907 0.870 0.761 0.666 0.825 Note. A two-step system GMM approach results and robust standard errors are reported. Robust standard errors in parentheses. LLR = loan loss reserve; RBCR = risk-based capital/risk; TIRBR = Tier I risk-based ratio; CERBR = common equity risk-based ratio; TCAPR = total capital ratio; TITA = Tier I capital to total assets; LIQ = liquidity; LG = loan growth; ID = income diversification; BE = bank efficiency; FD = funding diversity; MP = market power; GMM = generalized method of movement. *p < .1. **p < .05. ***p < .01. connected. The findings are robust when using capital buffer in their consideration for formulating new regulations for risk ratios to explain the connection with LLR, NPL, and RWA. mitigation. The sole reliance on required capital ratios base on The outcomes have significant importance for regulators and RWA is not enough to control risk-taking. In general, the policymakers. These results cast doubts to rely on one of the development of capital buffer ratios and risk-based capital proxies as a risk measure. The risk-weighting approach carries ratios are right but not enough to control the risk-taking of similar in Basel III, which may provide conspiracy to manipu- commercial banks. There is a need to revise the risk-weighted late the portfolio for the adjustment of regulatory capital ratios. criteria and use NPL and LLR as a supplement for the mitiga- The results suggest that regulators must include LLR and NPL tion of risk-taking in commercial banks for the future. 10 SAGE Open Table 6. Impact of Capital Ratios on RWA. (1) (2) (3) (5) (6) Variables RWA RWA RWA RWA RWA L.RWA 0.714*** 0.712*** 0.721*** 0.696*** 0.690*** (0.070) (0.070) (0.072) (0.062) (0.060) RBCR 0.242* (0.145) TIRBR 0.290 (0.177) CERBR 0.341* (0.196) TCAPR 0.506* (0.301) TITA 0.316* (0.186) ROA 0.263* 0.314** 0.248 0.342** 0.313** (0.154) (0.158) (0.152) (0.164) (0.153) LIQ −0.443*** −0.441*** −0.449*** −0.432*** −0.430*** (0.062) (0.063) (0.063) (0.057) (0.056) LG 0.206*** 0.205*** 0.203*** 0.194*** 0.194*** (0.064) (0.065) (0.064) (0.066) (0.066) ID 0.022** 0.022** 0.024*** 0.021** 0.021** (0.009) (0.009) (0.009) (0.009) (0.009) BE 0.003 0.004 0.004 0.004 0.004 (0.000) (0.000) (0.000) (0.000) (0.000) Size 0.001 0.001 0.001 0.001 0.001 (0.001) (0.001) (0.001) (0.001) (0.001) FD 0.186 0.205 0.160 −0.276 0.155 (0.188) (0.180) (0.196) (0.453) (0.202) MP 0.006** 0.007** 0.007** 0.006** 0.006** (0.002) (0.002) (0.002) (0.002) (0.002) Constant −0.074 −0.087 −0.077 0.192 −0.030 (0.079) (0.073) (0.075) (0.229) (0.100) Observations 14,944 14,928 14,944 14,928 14,928 Number of id 934 933 934 933 933 AR (2) 0.217 0.224 0.103 0.278 0.262 No. of Instruments 12 12 12 12 12 Hansen Statistics 0.602 0.545 0.772 0.578 0.538 Note. A two-step system GMM approach results and robust standard errors are reported. Robust Standard errors in parentheses. RWA = risk-weighted asset; RBCR = risk-based capital/risk; TIRBR = Tier I risk-based ratio; CERBR = common equity risk-based ratio; TCAPR = total Capital ratio; TITA = Tier I capital to total assets; LIQ = liquidity; LG = loan growth; ID = Income diversification; BE = bank efficiency; FD = funding diversity; MP = Market power; GMM = Generalized method of movement. *p < .1. **p < .05. ***p < .01. Abbas et al. 11 Table 7. Impact of Capital Buffer Ratios on Risk-Weighted Assets & Loan Loss Reserves. (1) (2) (3) (4) (5) (6) Variables RWA RWA RWA LLR LLR LLR L.RWA 0.712*** 0.708*** 0.723*** (0.070) (0.068) (0.072) BCBR 0.285* −0.013** (0.170) (0.00677) TIBR 0.283* −0.014** (0.169) (0.006) CEBR 0.302* −0.014** (0.174) (0.007) L.LLR 0.650*** 0.647*** 0.650*** (0.029) (0.028) (0.029) ROA 0.268* 0.281* 0.242 −0.029*** −0.030*** −0.027*** (0.155) (0.157) (0.151) (0.00673) (0.00681) (0.00634) LIQ −0.441*** −0.437*** −0.451*** 0.008*** 0.008*** 0.008*** (0.061) (0.059) (0.064) (0.001) (0.001) (0.001) LG 0.206*** 0.206*** 0.202*** 0.006*** 0.006*** 0.006*** (0.064) (0.064) (0.064) (0.000) (0.000) (0.000) ID 0.0225** 0.022** 0.024*** −0.003*** −0.003*** −0.003*** (0.009) (0.009) (0.009) (0.000) (0.000) (0.000) BE 0.003 0.003 0.004 −0.006*** −0.006*** −0.007*** (0.000) (0.000) (0.000) (0.001) (0.001) (0.001) Size 0.001 0.001 0.001 −0.002 −0.002 −0.005 (0.001) (0.001) (0.001) (0.005) (0.005) (0.006) FD 0.212 0.246 0.143 0.018*** 0.017*** 0.021*** (0.173) (0.155) (0.206) (0.006) (0.005) (0.007) MP 0.006** 0.006** 0.007** 0.002** 0.002** 0.001 (0.002) (0.002) (0.002) (0.009) (0.009) (0.000) Constant −0.069 −0.087 −0.050 −0.008*** −0.008*** −0.009*** (0.082) (0.073) (0.089) (0.003) (0.002) (0.003) Observations 14,944 14,944 14,944 14,880 14,880 14,880 Number of id 934 934 934 930 930 930 AR (2) 0.216 0.212 0.191 0.448 0.455 0.457 No. of Instrument 12 12 12 12 12 12 Hansen Statistics 0.589 0.574 0.789 0.888 0.832 0.745 Note. A two-step system GMM approach results and robust standard errors are reported. Robust Standard errors in parentheses. RWA = risk-weighted asset; LLR = loan loss reserves; TIBR = Tier-I buffer ratio; CEBR = Common equity buffer ratio; LIQ = Liquidity; LG = Loan growth; ID = Income diversification; BE = Bank Efficiency; FD = Funding diversity; MP = Market power; GMM = Generalized method of movement. *p < .1. **p < .05. ***p < .01. Declaration of Conflicting Interests References The author(s) declared no potential conflicts of interest with respect Abbas, F., Butt, S., Masood, O., & Javaria, K. (2019). The effect to the research, authorship, and/or publication of this article. of bank capital buffer on bank risk and net interest margin: Evidence from the US. Studies, 5(2), 72–87. Funding Abbas, F., Iqbal, S., & Aziz, B. (2019). 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How Do Capital Ratios Affect Bank Risk-Taking: New Evidence From the United States:

Abbas, Faisal; Masood, Omar; Ali, Shoaib; Rizwan, Sohail
SAGE Open , Volume 11 (1): 1 – Jan 9, 2021
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Abstract

This study aims to examine the impact of different capital ratios on Non-Performing loans, Loan Loss Reserves, and Risk- Weighted Assets by studying large commercial banks of the United States. The study employed a two-step system generalized method of movement (GMM) approach by collecting the data over the period ranging from 2002 to 2018. The study finds that using Non-Performing loans and Loan Loss Reserves as a proxy for risk, results support moral hazard hypothesis theory, whereas the results support regulatory hypothesis theory when Risk-Weighted Assets is used as a proxy for risk. The results confirm that the influence of high-quality capital on Non-Performing loans, Loan Loss Reserves, and Risk-Weighted Assets is substantial. The distinctive signs of Non-Performing loans, Loan Loss Reserves, and Risk-Weighted Assets have indications for policymakers. The results are intimate for formulating new guidelines regarding risk mitigation to recognize Non-Performing loans and Loan Loss Reserves and the Risk-Weighted Assets for better results. JEL Classification: G21, G28, G29 Keywords bank capital ratios, non-performing loans, loan loss reserves, risk-weighted assets base ratio of 6% of Tier 1 capital to RWAs; and (c) Tier 1 Introduction Common equity ratio (T1CER), which requires a base ratio Since the order of the Basel-I Accord in 1988, followed by of 4.5% of RWAs. In this paper, we look at the effect of capi- Basel II in 2004 and most as of late the Basel III Accord in tal on the risk of large commercial banks. In particular, we 2010, the definitions of bank capital has advanced signifi- thoroughly scrutinize different definitions of bank capital cantly in an exertion to improve banking framework sound- (risk-based and non-risk-based capital and capital buffer ness and fill the harmonization hole that had caused past ratios). Capital fills in as a security component to absorb monetary crises. The 2007 to 2008 economic crisis accu- losses. In this way, it ought to be quite compelling to experts rately interpreted that capital provisions alone are lacking to and regulators in the United States because, from one per- forestall bank distresses. spective, a holding of a higher capital ratio intensifies the The weaknesses of prior Basel Accords provoked the bank’s loss absorption potential. The holding of excessive Basel Committee on Banking and Supervision (BCBS) to funds skewed economic enlargement and increases bank risk execute one more arrangement of rules for banking guide- (Bitar et al., 2018). In recently published studies, scholars lines. The BCBS’s endeavors brought about the Basel III explore the importance of capital ratios and their impact on rules (Basel III recommended conservative buffer 2.5% of banks risk-taking (Bitar et al., 2016, 2018; Brandao-Marques risk-weighted assets (RWAs) to enhance the loss absorption et al., 2020; Cohen & Scatigna, 2016; Ding & Sickles, 2018, capacity during stressful conditions. For detail see: http:// www.bis.org/bcbs/basel3.htm.), which expect banks to be increasingly thorough by rethinking capital structure. The The University of Lahore, Pakistan Basel III Accord intends to improve the quality and incre- Air University, Islamabad, Pakistan ment the size of a bank’s core capital base. Likewise, the new 3 University of Malaya, Kuala Lumpur, Malaysia regulations consider three related ratios of capital as funda- Corresponding Author: mentals. (a) the first is a risk-based capital ratio (TRBCR), Faisal Abbas, Department of Accounting & Finance, The University of which requires a base ratio of 8% against risk-weighted- Lahore, Lahore PK-PB 46000, Pakistan. assets; (b) the Tier 1 capital ratio (T1RBR), which requires a Email: [email protected] Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open 2019). The contradictory conclusion of the recent literature capital buffer ratios are significant for policymakers to know motivates us to explore and fill this gap. whether the recommendations for holding of higher capital Most of the previous studies use basic definition of bank are useful. In the methodological context, the study contrib- capital (equity to total assets) to interpret the impact of capital utes to the empirical literature and uses a two-step system ratio on risk-taking (Shrieves & Dahl, 1992). This study use GMM approach, which indicates that the results hold when and analyze the newly evolved definitions of bank capital like we apply different ratios of bank capital. The rest of the study total risk-based capital ratio, tier one risk-based capital ratio, is structured as follows: The next section contains a literature tier-one risk-based Common equity capital ratio, total risk- review and hypotheses development. The third segment is based capital buffer ratio, tier-one risk-based capital buffer consists of data sources and econometric techniques. The ratio, and Common equity buffer ratio. The measurement of fourth section reports the results and discussion, and the final risk using the bank balance sheet remains contradictory. section is about the conclusion and policy implications. Some studies use loan loss reserves, and others apply for non- performing loans and RWAs. We use the above three defini- Literature Review and Hypotheses tions of bank risk in this study for the robustness of the Development relationship. This methodology permits us to explore which types of capital definitions are most useful to drive which The question of how different bank capital ratios affect loan kind of risk measure of large commercial banks in the United loss reserves, non-performing loans and RWAs is far from States. The study starts by investigating which risk-based being settled. The financial theories document various esti- capital ratios are more useful for which type of bank risk mea- mations about the influence of capital ratios on bank portfo- sure (loan loss reserves, non-performing loans, and RWAs). lio risk-taking. In recently published studies, scholars explore Then followed the non-risk-based capital ratios connection the importance of capital ratios and their impact on banks’ with loan loss reserves, non-performing loans, and RWAs. risk-taking (Bitar et al., 2016, 2018; Brandao-Marques et al., The previous theoretical and empirical literature casts suspi- 2020; Cohen & Scatigna, 2016; Ding & Sickles, 2018, 2019). cions over the adequacy of risk-based capital ratios (Dermine, The contradictory conclusion of the recent literature moti- 2015). We explore whether high-quality capital, such as tier vates to explore and fill this gap. Anginer and Demirguc- one risk-based capital ratio, is more useful to absorb losses Kunt (2014) argue that the purpose behind high capital ratios than non-risk based capital ratios (Distinguin et al., 2013). is to increase their resistance against unexpected economic The study also explores whether tier one common equity risk- shocks and meet the demand for deposits withdrawals. They based capital buffer ratio is more useful to absorb losses than claim that the presence of a higher capital buffer ratio tier one risk-based capital buffer and total-risk-based capital enhances the owner’s investment behavior. buffer ratios. The findings of this study have significant Jacques and Nigro (1997) opine that increasing the capital implications for experts and decision-makers for the develop- against RWAs may reduce bank risk-taking; other factors held ment of capital guidelines for the financial system stability in constant. Correspondingly, Aggarwal and Jacques (1998) the United States. explain that the holding of a higher capital ratio than regula- The study contributes both theoretically and methodolog- tory capital ratios protects from distress in crisis conditions. ically in the following ways to the existing literature. In pre- Berger and Bouwman (2013) uncover that the presence of vious studies, some researchers advocated the use of RWAs capital is favorable to small banks in adverse economic con- as a portfolio risk proxy relative to non-performing loans ditions. Tan and Floros (2013), Lee and Hsieh (2013) reveal (NPLs) and loan loss reserves (LLRs). The other justifies in an inverse connection between various capital ratios and bank favor of LLR and NPL against RWA. We here contribute to risk-taking. Eventually, more efficient bank management the existing literature by using these three risk proxies at a may play a crucial part in adjusting depositors and owners’ time to resolve the contradiction of superiority. The study benefits in decreasing agency issues. Abusharba et al. (2013) used three risk measures on a fundamental information basis, demonstrate an increase in non-performing loans leads to a includes NPL, LLR, and RWA. In the theoretical context, the decrease in the bank capital adequacy ratio. Konishi and study contributes to the debate of the impact of different cap- Yasuda (2004) find that regulatory capital decreases the risk- ital definitions on a various measure of bank portfolio risk taking of commercial banks. This argument favors the nega- (Altunbas et al., 2007; Bitar et al., 2018; Ding & Sickles, tive relationship between bank capital and risk-taking. 2018; Jacques & Nigro, 1997; Lee & Hsieh, 2013; Shrieves & Dahl, 1992) by investigating the impact of different risk- Hypothesis 1: There is a negative relationship between based capital ratios, non-risk-based capital ratios, and capital the risk-taking of large commercial banks of the United buffer ratios on various proxies of bank risk. The findings of States and regulatory capital ratios. this study allow regulators and governments to control which types of capital ratios are more considerable to reduce the The corresponding set of arguments posits that banks take risk of commercial banks. The outcomes obtained from the higher risks to maximize the owner’s wealth at the cost of comparison of risk-based capital, non-risk-based capital, and depositor’s money. The deposit insurance plan motivates Abbas et al. 3 bank managers to engage in risky projects. The depositor’s concluded in their study conducted in Europe that banks usually money remains secure due to insurance guarantees. The the- create a higher amount of buffer due to the higher cost of capital ory of too-big-to-fail follows a similar argument in which the adjustment. They found an inverse relationship between the systemic banks involve in excessive risk-taking due to gov- economic cycle and capital buffers for large commercial and for ernment bailout options (Kim & Santomero, 1988). These saving banks. They also revealed that small banks incline to arguments favor the “regulatory hypothesis” theory, which increase their capital buffer during economic booms. Jokipii suggests an increase in capital ratio with the increase in a and Milne (2011) found that bank capital and risk have a posi- risky portfolio. Koehn and Santomero (1980) conclude that tive and causal relationship in the U.S. banking sector. They the rise in capital leads to an increase in banks’ risk. Avery indicated that banks usually use capital buffers amount to adjust and Berger (1991) reveal that risk-based capital and bank capital and risk. This relationship signifies that banks increase risk-taking move in the same direction. J. Blum (1999) docu- their capitalization with the increase in their risk. Guidara et al. ments a positive relationship between an increase in bank (2013) explored the relationship between bank risk and bank capital and portfolio risk in the short run. In a specific con- capital buffers using the data of commercial banks of Canada, text, Iannotta et al. (2007) provide that there is a positive and they found that higher capitalized banks follow the market relationship between loan loss provision and bank capital. In discipline. Anginer et al. (2014) showed that higher capital pro- a most recent study of Bitar et al. (2018) validate a positive vides greater power to resist earning shocks. They demonstrated relationship between loan loss reserves and non-risk-based that higher capital buffers provide more confidence to stock- capital ratios. Altunbas et al. (2007) uncover that there is a holders to make full investment choices. Examples of other positive connection between loan loss reserves and capital studies conducted to explore the bank buffers are Ayuso et al. ratio. Likewise, we develop the following hypothesis: (2004), Fonseca & González (2010), and Valencia & Bolaños (2018). Hypothesis 2: There is a positive relationship between the risk-taking of large commercial banks of the United Hypothesis 4: There is a positive relationship between States and capital ratios. the risk-taking of large commercial banks of the United States and capital buffer ratios. In recent literature, various studies investigate the usefulness of risk-based and non-risk-based capital ratios. Most of those Data and Econometric Model studies’ findings cast doubts on the effectiveness of risk- based capital ratios to reduce risk. J. M. Blum (2008) con- In the data structure of the present study, the Federal Deposit cludes that the own determination of banks’ risk exposure Insurance Corporation (FDIC) institutional directory was provides an incentive to express lower risk to limit regula- used for extracting detailed information about the financial tory capital requirement. These conditions favor involving in system necessary to analyze the data in the long-run accord- riskier activities. Dermine (2015) suggests that there should ing to the reports of FDIC call/TFR, which is updated quar- be a complementary risk-based capital in the real sense to terly by FDIC. The annual dataset provided for financial avoid the problem of riskiness. Cathcart et al. (2015) report institutions and covered the long period of the current that banks with higher risk-based capital ratio than regula- research study between 2002 and 2018. The sample of the tory requirements are unable to survive in financial crisis. present research study is balanced to comparable panel data Haldane and Madouros (2012), the study provides inconclu- containing insured commercial banks of the United States, as sive findings of the effectiveness of risk-based capital ratios described in FDIC reports. to influence bank risk-taking. Rime (2001) concludes that Furthermore, the assets are also based on a consolidated the relationship between bank risk-taking and regulatory theme. There were many banks in nearly 1806 in the men- capital is not clear and conspicuous. Bitar et al. (2018) reveal tioned list on December 31, 2018, which were listed by FDIC. that risk-based capital ratios fail to reduce portfolio risk by However, for appropriate and reliable data analysis, the inclu- using the sample of Organisation for Economic Co-operation sion of the study sample units was based on the following cri- and Development (OECD) countries banks. In a specific teria: the listed banks should have been active on the reported context, they find that when RWAs measure risk, the influ- date. There must not be any missing observations for any spe- ence of capital ratios is insignificant. In the context of the cific study variables of at least 2 years in the studied period. above debate, the hypothesis is: The total assets of banks must be higher than $300 million on the December 31, 2018. After filtration of properly used crite- Hypothesis 3: There is no relationship between the risk- ria, there were 942 banks selected for the study sample size. taking of large commercial banks of the United States and capital ratios. Dependent Variable Definitions Different researchers have examined the relationship between Bank risk. In previous studies, some researchers favor the use capital buffers and bank risk. Jokipii and Milne (2008) of RWA as a portfolio risk proxy superior to NPL and LLR. 4 SAGE Open The other justifies in favor of LLR and NPL against RWA. Nguyen (2018). It indicates that higher diversification of We here use these three risk proxies (see Table 1) at a time to funding may encourage to take a greater risk. The income resolve the contradiction of superiority. The study used three diversification measure is consistent with Nguyen (2018). risk measures on a fundamental information basis, includes The more diversified banks have more options to earn profits NPL, LLR, and RWA. The NPL is used by Ding and Sickles which may encourage to take higher risk to increase returns. (2019), Jiang et al. (2020), Ozili (2019a, 2019b), Shim (2013), and Shrieves and Dahl (1992). The RWA is used by Model Specifications Ding and Sickles (2019), Jacques and Nigro (1997), and Shrieves and Dahl (1992). The LLR is employed by Altun- The dynamic model is applied in this study. There are several bas et al. (2007), Bitar et al. (2018), and Lee and Hsieh reasons for applying GMM (Generalized method of move- (2013). The reason for using these three proxies is to find out ment)., it controls the endogeneity of the lagged reliant vari- the robustness of relationships in the present conditions of able in a dynamic setting. GMM controls the measurement the United States. Besides, the purpose includes the mea- error problem, reduces omitted bias issues, and controls the sures and risk theories clarification and association and unobserved heterogeneity problem in panels. The means of reexaminations. the dynamic panel regression models have p lags of the dependent variable and comprise unobserved panel effects, Capital ratios. Bank capital ratio is an independent variable. which may be fixed or random. The correlation between We use several capital ratios, including risk-based capital unknown panel effects and the lagged value of dependent ratios and non-risk-based capital ratios reported in Table 1. variables makes the estimators inconsistent. Arellano and The study also uses the capital buffer ratios for the robust- Bond (1991) provide a method called the generalized method ness and more in-depth understanding between the relation- of moments as the solution to make the estimators consistent. ship of different risk measures and real increase and decrease They argue that the use of a one-step and two-step approach in capital ratios. The rates taken for capital are consistent in large instrument matrix and robust standard errors for a with many studies (Abbas, Butt, et al., 2019; Abbas & one-step GMM approach are to be found severely biased. To Masood, 2020b; Bitar et al., 2018; Jacques & Nigro, 1997; overcome this severe biasness, Windmeijer (2005) presented Jiang et al., 2020; Jokipii & Milne, 2011). a robust estimator for the two-step GMM approach, which is more efficient and is a biased-free method to calculate esti- Control variables definitions. The profitability is measure with mators. Later, Blundell and Bond (1998) worked on it fur- net income to total assets similar to Lee and Hsieh (2013) ther, and their findings have been used by various studies in and Shrieves and Dahl (1992). The higher profits required the field of banking (Fiordelisi et al., 2011; Lee & Hsieh, higher risk due to this assumption; there may exist a positive 2013; Tan, 2016; Tran et al., 2016). we use the two-step sys- relationship between profitability and NPLs. The negative tem GMM in this study, as its more efficient than the one- relationship is also not less applied. We are implying that the step system GMM, and two-step system GMM can capture increase in NPL leads to a decrease in bank profits (Ozili, the maximum values to calculate the estimators. 2019b). The loan growth ratio and risk are related, and the proxy measure is consistent with (Abbas, Butt, et al., 2019; System GMM Model Specifications Abbas, Iqbal, & Aziz, 2019). We control the bank size by taking the natural logarithm of bank total assets similar to The basic model of the system GMM approach is the follow- many studies (Ding & Sickles, 2018; Lee & Hsieh, 2013; ing form: Shrieves & Dahl, 1992). The size is used for economies of scale as a whole and for “too big to fail” hypothesis for (1) lnYY =+ φβX ’ ++ ηε () it ,, it−1 it ,, ii t banks. The liquidity always remains crucial in the banking It is assumed that the above specification is a random walk industry to meet the demand for depositors. We followed the equation, and the dependent variables are persistent. measurement of liquid assets to total assets like Ding and Accordingly, the results of difference GMM produce an ineffi- Sickles (2018) and Jiang et al. (2020). The market share role cient and biased parameter, particularly in finite samples. The is reflected in the behavior of commercial banks for risk- empirical literature explains that the bias and poor performance taking (Jiang et al., 2020). The market share represents the of difference GMM are due to poor instruments (Blundell & market power of the banks. The cost to income ratio is used Bond, 1998). The system GMM uses one equation in levels for bank efficiency control, which is consistent with Abbas, form with the first differences as instruments, whereas the sec- Butt, et al. (2019) and Jiang et al. (2020). There are several ond equation is used in the first differences form with levels as aspects which explore the connection of bank efficiency and instruments. The system GMM approach implicates a greater proxies of risk (NPL, LLR, RWA). Efficient banks control number of instruments. Still, Monte Carlo evidence recom- the problem of higher NPL and riskiness. The funding diver- sification is relevant to risk. Here we use the proxy similar to mends that where the period is limited, and the dependent Abbas et al. 5 variable is found to be persistent, the use of system GMM there are no high correlations exits among explanatory. The low correlation also indicates that there is no problem of reduces the bias of a small sample. There is another feature of multicollinearity. system GMM; if there are autocorrelation and heteroscedastic- We first analyze the impact of different capital ratios on ity in the data, a two-step system GMM should be applied by NPL for the large commercial banks sample applying a two- developing a weighting matrix using residuals from the first step system GMM approach. A two-step system GMM step. It is also argued that in limited samples, the standard approach is employed to provide consistent estimates. The errors were found to be downward biased. In this situation, Hansen test is reported for the confirmation and validation of researchers recommend applying the robust standard error instruments used. The Sargen test statistics are presented for approach developed by Windmeijer (2005), which corrects the the over-identification and autocorrelations check among sample bias. residuals (Ding & Sickles, 2018). In the first turn, we will Difference or system GMM to be used. The basic model address the influence of total capital ratio (TCAPR) and total equation: risk-based capital ratio (RBCR) on NPL, LLR, and RWA in Tables 4 to 6, respectively. For the effect of total risk-based (2) lnYY =+ φβX ’ ++ ηε capital ratio and total capital ratio on NPLs coefficients, see () it ,, it−1 it ,, ii t Table 4 Columns 1 and 4. What is better to apply for consistent and unbiased parame- The empirical findings explore that there is a negative ters? The rule of thumb provided by Bond et al. (2001) sug- and statistically significant relationship between bank capi- gests the ordinary least squares (OLS) is to be applied first, tal ratios and NPL. In the context of risk theory, higher and the least-squares dummy variables (LSDV) method is NPLs show higher risk, an antagonistic relationship with used second to find out the estimators. The panel OLS esti- capital ratios are similar to the argument of Bitar et al. mator ϕ should be the upper-bound estimate, whereas the (2018) and Jacques and Nigro (1997). The negative relation- fixed effects estimator is considered a lower-bound estima- ship between capital ratios and NPL is contradicting the tor. The decision is taken based on difference GMM esti- findings of Shrieves and Dahl (1992) but consistent with mates; if the estimates are close to or below the estimators of (Jiang et al., 2020). There are several justifications for the the fixed effects method, the former estimators are consid- disagreement between the present study results and Shrieves ered to be a downward biased due to the weak instruments, and Dahl (1992) study. First, there were different economic and system GMM is to be preferred as the best choice to conditions in the 1990s, and now the economic conditions apply instead of difference GMM. The following model is entirely change. The second reason for the difference is the used in this study under the condition elaborated above: present strict regulations and monitoring of commercial banks compared to the 1990s. The negative relationship (3) YY =+ αβ ++ XZ βε + supports the regulations for capital. The higher regulatory it ,, it−11 it ,, 2i t capital experience fewer NPLs were consistent with the Here, the Y is a dependent variable, which is bank risk (NPL, argument of Ozili (2019a). One of the justifications for the LLR, RWA) in this study i, represents banks and t shows negative association between NPL and capital ratios indi- period, is lagged value of risk. Unknown parameters t−1 cates that banks with lower capital take higher risk, which X is the independent variable, which is capital in this case upsurges the chances of NPL consistent with Klein (2013). where it may be total capital ratio, total risk-based capital The banks having a higher capital ratio reports lower NPL, ratio, and capital buffer ratio based on the simulation under as argued by Boudriga et al. (2009). The empirical findings observation. Z shows the list of control variables, and ε is an of the present study are supporting the moral hazard hypoth- error term. esis theory. The moral hazard theory suggests an inverse relationship between capital and portfolio risk. The negative sign between NPL and capital ratios indicates that banks Results and Discussion hold lower capital when NPL is augmented, similar to the Table 2 contains the summary statistics information for argument of Altunbas et al. (2007) and Ding and Sickles dependent, independent, and control variables. The details (2018). There is an inverse relationship between bank prof- include the number of observations, average value, and itability and bank NPLs. The simple theory is that NPL gen- standard deviation from the mean, minimum, and maxi- erates lower income for lending firms due to that the profit mum level of proxies. The information explores that the remain lowers. The lower profits due to the charge-off NPL reported values for proxies are in line with the previous may decrease bank capital ratios, as argued by Ozili (2019b). studies (Abbas & Masood, 2020a; Ding & Sickles, 2019). The findings conclude that the availability of higher liquid- Table 3 reports the correlation between explanatory vari- ity increases the ratio of NPL. The role of loan growth is ables. The sign and significance are as per the economic decisive; the findings of NPL is consistent with the results theory and consistent with (Abbas & Masood, 2020a; Ding of Ozili (2019b). The increase in bank size causes a decrease & Sickles, 2019). The correlations matrix confirms that in the NPL. The one possible justification of bank size and 6 SAGE Open Table 1. Variables and Data Sources. Variables names Measurement Reference Dependent Variables RWATA Risk-Weighted Assets to total assets (Abbas, Butt, et al., 2019) LLRGL Loan Loss Reserves to gross loans (Bitar et al., 2018) NPLTA Non-performing loans to total assets (Ding & Sickles, 2019) Independent Variables TCAPR Total Bank equity to total assets (Lee & Hsieh, 2013) TITA Tier I Capital to Total Assets (Bitar et al., 2018) TIRBR Tier I capital to risk-weighted assets (Bitar et al., 2018) CERBR Common Equity to risk-weighted assets (Bitar et al., 2018) RBCR Risk-Based Capital/Risk-weighted Assets (Bitar et al., 2018) BRBCR Actual Risk-based Capital less 8% (Guidara et al., 2013) TIRBR Tier I risk-based ratio less 6% (Abbas, Butt, et al., 2019) CEBR Common equity risk-based ratio less 4.5% (Abbas, Butt, et al., 2019) ROA Net Income to Total Assets (Guidara et al., 2013) LIQ The ratio of liquid assets to total assets (Abbas, Butt, et al., 2019) LG Yearly change in loans (Abbas, Butt, et al., 2019) BE Operating expenses to total asset (Abbas, Butt, et al., 2019) ID 1-(net income-operating income)/operating income (Nguyen, 2018) FD 1-((equity/total)2+(subordinate debt/ total)2+(deposits/total)2 (Nguyen, 2018) +(short term fund/total)2) MP Total bank deposits to total industrial assets (Jiang et al., 2020) Size Natural Log of total assets (Shrieves & Dahl, 1992) Note. TCAPR = total Capital ratio; CERBR = common equity risk-based ratio; CEBR = common equity buffer ratio; LIQ = liquidity; LG = loan growth; BE = bank efficiency; ID = income diversification; FD = funding diversity; MP = market power. Table 2. Descriptive Statistics. Variable Obs. M SD Minimum Maximum Risk-weighted assets/TA (RWATA) 14,944 0.67 0.43 0.52 0.84 Loan loss reserves/GL (LLRGL) 14,944 0.009 0.003 0.004 0.018 Non-performing loans/TA (NPLTA) 14,944 0.104 0.022 0.073 0.157 Risk-based capital ratio (TRBCR) 14,944 0.141 0.027 0.108 0.192 Total capital ratio (TCAPR) 14,944 0.102 0.018 0.078 0.136 Capital buffer ratio (BRBCR) 14,944 0.059 0.021 0.033 0.092 Tier-I capital/RWA (TIRBR) 14,944 0.128 0.02 0.104 0.157 Tier-I buffer ratio (TIBR) 14,944 0.066 0.019 0.043 0.095 Common equity risk-based ratio (CERBR) 14,944 0.126 0.021 0.102 0.157 Tier-I capital/TA (TITA) 14,944 0.093 0.015 0.073 0.121 Common equity buffer ratio (CEBR) 14,944 0.082 0.025 0.053 0.123 Profitability (ROA) 14,944 0.01 0.005 −0.001 0.02 Liquidity (LIQ) 14,944 0.048 0.027 0.021 0.095 Loan growth (LG) 14,944 0.666 0.113 0.455 0.824 Income diversification (ID) 14,944 0.463 0.098 0.262 0.593 Bank efficiency (BE) 14,944 3.048 1.756 0.906 6.852 Bank size 14,944 13.554 0.95 12.259 15.368 Funding diversity (FD) 14,944 0.555 0.011 0.539 0.581 Market power (MP) 14,944 0.139 0.271 −0.171 2.909 Source. (Authors calculations using Stata Output). Note. TA = total assets; GL = gross loans; TIRBR = Tier I risk-based ratio. NPL is that larger banks tend to take a higher risk, as argued positively connected. The market share and NPL are nega- by Ding and Sickles (2018). Funding diversity and NPL are tively related. Abbas et al. 7 Table 3. Matrix of Correlations. Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) LLRGL 1.000 RBCR −.052 1.000 ROA −.082 .076 1.000 LIQ .101 .185 −.090 1.000 LG .290 −.450 .016 −.179 1.000 ID −.168 .087 .583 .034 −.114 1.000 BE .022 −.007 −.489 .100 −.015 −.219 1.000 Size .016 −.107 −.017 −.057 −.028 .310 −.034 1.000 FD .017 .404 .084 −.033 −.076 .109 −.056 .215 1.000 MP .003 −.022 .029 .030 −.081 .111 −.024 .098 .068 1.000 Source. (Authors calculations using ‘Stata’s’ Output). Note. LLRGL = loan loss reserves to gross loans; RBCR = risk-based capital/risk; LIQ = liquidity; LG = loan growth; ID = income diversification; BE = bank efficiency; FD = funding diversity; MP = market power. For the effect of total risk-based capital ratio and total coefficients see Table 6 Columns 1 and 4. The results show capital ratio on LLR coefficients, see Table 5 Columns 1 and that bank capital ratios and RWA are positively associated. 4, respectively. There is a negative relationship between cap- The relationship is weak and in line with the recent develop- ital ratios and LLR. The negative relationship between risk- ments of (Bitar et al., 2018). One more crucial explanation is taking and capital ratios are in line with the conclusions of not less compelling between RWA and capital ratios. The many studies (Altunbas et al., 2007; Bitar et al., 2018; more complicated regulations for bank capital ratios permit Jacques & Nigro, 1997). The one possible justification for an to manipulate their risky portfolio for the adjustment of inverse relationship between capital ratios and LLR, indicat- required capital. Due to this reason, the bank’s actual risk ing an aggressive reserve policy based on moral hazard exposure does not expose in line with the studies of Cathcart hypothesis theory, and these results are contrary to the find- et al. (2015) and Dermine (2015). The positive relationship is ings of Altunbas et al. (2007). One more channel for this con- favoring the regulatory hypothesis theory. The results show nection may be due to an increase in LLR; the profitability of that profitability, loan growth, income diversity, and market the bank decreases, which may cause to reduce the capital share push forward to increase the RWA. Liquidity and ratios. The general debate provides that higher LLR indicates RWAs have a negative relationship. the higher expected risk in a portfolio. In this regard, an inverse relationship is supported by Aggarwal and Jacques Robustness (1998) and Jacques and Nigro (1997). In the specific context, higher regulatory capital experience less LLR, consistent In this part, we discuss the impact of tier-one risk-based capi- with the argument of Ozili (2019a). One of the reasons for a tal ratio, common equity risk-based capital ratio, tier one negative relationship between LLR and capital ratios indi- capital ratio on NPL, LLR, and RWA. In the first turn, we use cates that banks with lower capital take higher risk, which to test the impact of the tier-I risk-based capital ratio on NPL. upsurges the chances of LLR consistent with Klein (2013). The relationship between the tier-I risk-based capital ratio The banks having a higher capital ratio reports lower LLR, as and NPL is negative. In absolute terms, the coefficient is argued by Boudriga et al. (2009). The negative relationship more substantial concerning the total risk-based capital ratio. is consistent with moral hazard theory and supported by The impact of tier one risk-based common equity ratio is many studies (Aggarwal & Jacques, 1998; Altunbas et al., more significant than the tier one risk-based capital ratio. 2007; Jacques & Nigro, 1997). The findings provide economic sense to understand this con- The results indicate that an increase in LLR pushes banks’ nection. It means higher the quality of capital and lowers the profitability downward (other things held equal). The NPLs. For the robustness of the non-risk-based capital ratio, increase in liquid assets increases the loan growth, which we use tier one capital ratio, and results are consistent con- leads to moving LLR of banks upward. Income diversity cerning sign and significance. The similar capital ratios are provides excessive profits than lending due to that the used to test the robustness of LLR and RWA. The findings demand for LLR decreased. The improvement in bank effi- remain consistent and confirm that LLR is favoring the moral ciency decreases the proportion of LLR. The funding diver- hazard hypothesis, and RWA supports the regulatory hypoth- sity and LLR have a negative relationship. The market share esis theory. The study further uses the capital buffer ratios for has a positive impact on LLR. For the effect of total risk- a deeper understanding of the relationship. Table 7 shows the based capital ratio and total capital ratio on RWA, results of the impact of total capital buffer ratio, tier one 8 SAGE Open Table 4. Impact of Capital Ratios on NPLs. (1) (2) (3) (5) (6) Variables NPL NPL NPL NPL NPL L.NPL 0.759*** 0.537*** 0.627*** 0.565*** 0.440*** (0.061) (0.092) (0.109) (0.102) (0.085) RBCR −0.364*** (0.038) TIRBR −0.738*** (0.090) CERBR −0.919*** (0.136) TCAPR −1.404*** (0.206) TITA −0.820*** (0.092) ROA −0.005 −0.085** 0.0681 −0.140*** −0.096*** (0.031) (0.039) (0.050) (0.053) (0.036) LIQ 0.046*** 0.089*** 0.100*** 0.085*** 0.087*** (0.009) (0.016) (0.021) (0.017) (0.014) LG −0.018*** −0.017** −0.034*** 0.0356*** 0.0476*** (0.005) (0.008) (0.010) (0.013) (0.011) ID −0.003 −0.001 −0.003 0.002 0.001 (0.002) (0.004) (0.004) (0.005) (0.003) BE 0.001** 0.001 0.001 −0.001 0.002 (0.001) (0.005) (0.001) (0.001) (0.001) Size −0.002*** −0.003*** −0.005*** −0.001** −0.003*** (0.003) (0.001) (0.001) (0.001) (0.001) FD 0.386*** 0.620*** 0.762*** 2.008*** 0.774*** (0.046) (0.083) (0.120) (0.300) (0.091) MP −0.002* −0.004*** −0.005*** −0.002 −0.002 (0.000) (0.001) (0.002) (0.002) (0.001) Constant −0.126*** −0.191*** −0.222*** −0.977*** −0.346*** (0.019) (0.031) (0.042) (0.149) (0.043) Observations 14,944 14,928 14,944 14,928 14,928 Number of id 934 933 934 933 933 AR (2) 0.991 0.577 0.600 0.794 0.866 No. of Instruments 12 12 12 12 12 Hansen Statistics 0.592 0.993 0.471 0.006 0.129 Note. A two-step system GMM approach results and robust standard errors are reported. Robust Standard errors in parentheses. NPL = non-performing loan; RBCR = risk-based capital/risk; TIRBR = Tier I risk-based ratio; CERBR = common equity risk-based ratio; TCAPR = total capital ratio; TITA = Tier I capital to total assets; LIQ = liquidity; LG = loan growth; ID = income diversification; BE = bank efficiency; FD = funding diversity; MP = market power; GMM = generalized method of movement. *p < .1. **p < .05. ***p < .01. risk-based capital buffer ratio, and common equity buffer Conclusion ratio on RWA and LLR. The results confirm that RWA and In the present research, we explain the effect of capital ratios capital buffer ratios are positively associated, whereas the on bank risk-taking of large insured commercial banks in the relationship is negative in the case of LLR. The findings USA, covering the extensive period ranging from 2002 to explore for robustness checks are also in line with the previ- 2018. Our results indicate that the risk proxies LLR and NPL ous studies like (Abbas et al., 2020; Aggarwal & Jacques, are following the moral hazard hypothesis, and RWA is consis- 1998; Altunbas et al., 2007; Bitar et al., 2018; Ding & tent with regulatory theory. The study concludes an inverse Sickles, 2018; Jacques & Nigro, 1997; Jokipii & Milne, relationship between LLR, NPL, and bank capital ratios. On 2011; Lee & Hsieh, 2013; Rime, 2001; Shrieves & Dahl, the other, the hand RWA and capital ratio are positively 1992). Abbas et al. 9 Table 5. Impact of Capital Ratios on LLR. (1) (2) (3) (5) (6) Variables LLR LLR LLR LLR LLR L.LLR 0.649*** 0.648*** 0.651*** 0.641*** 0.643*** (0.029) (0.028) (0.029) (0.028) (0.028) RBCR −0.011** (0.005) TIRBR −0.014** (0.007) CERBR −0.016** (0.008) TCAPR −0.030* (0.015) TITA −0.016** (0.008) ROA −0.029*** −0.030*** −0.028*** −0.031*** −0.030*** (0.006) (0.006) (0.006) (0.007) (0.006) LIQ 0.008*** 0.008*** 0.008*** 0.008*** 0.008*** (0.001) (0.001) (0.001) (0.001) (0.001) LG 0.006*** 0.006*** 0.006*** 0.007*** 0.007*** (0.000) (0.000) (0.000) (0.001) (0.001) ID −0.003*** −0.003*** −0.003*** −0.003*** −0.003*** (0.000) (0.000) (0.000) (0.000) (0.000) BE −0.001*** −0.001*** −0.001*** −0.001*** −0.001*** (0.000) (0.000) (0.000) (0.000) (0.000) Size −0.005 −0.004 −0.005 −0.001 −0.002 (0.000) (0.000) (0.000) (0.000) (0.000) FD 0.019*** 0.019*** 0.020*** 0.051** 0.022*** (0.006) (0.006) (0.007) (0.022) (0.008) MP 0.002** 0.001* 0.001* 0.001* 0.001** (0.000) (0.000) (0.000) (0.000) (0.000) Constant −0.008*** −0.008*** −0.008*** −0.025** −0.011*** (0.003) (0.002) (0.002) (0.011) (0.004) Observations 14,880 14,880 14,880 14,880 14,880 Number of id 930 930 930 930 930 AR (2) 0.460 0.437 0.431 0.489 0.481 No. of Ins. 12 12 12 12 12 Hansen Statistics 0.907 0.870 0.761 0.666 0.825 Note. A two-step system GMM approach results and robust standard errors are reported. Robust standard errors in parentheses. LLR = loan loss reserve; RBCR = risk-based capital/risk; TIRBR = Tier I risk-based ratio; CERBR = common equity risk-based ratio; TCAPR = total capital ratio; TITA = Tier I capital to total assets; LIQ = liquidity; LG = loan growth; ID = income diversification; BE = bank efficiency; FD = funding diversity; MP = market power; GMM = generalized method of movement. *p < .1. **p < .05. ***p < .01. connected. The findings are robust when using capital buffer in their consideration for formulating new regulations for risk ratios to explain the connection with LLR, NPL, and RWA. mitigation. The sole reliance on required capital ratios base on The outcomes have significant importance for regulators and RWA is not enough to control risk-taking. In general, the policymakers. These results cast doubts to rely on one of the development of capital buffer ratios and risk-based capital proxies as a risk measure. The risk-weighting approach carries ratios are right but not enough to control the risk-taking of similar in Basel III, which may provide conspiracy to manipu- commercial banks. There is a need to revise the risk-weighted late the portfolio for the adjustment of regulatory capital ratios. criteria and use NPL and LLR as a supplement for the mitiga- The results suggest that regulators must include LLR and NPL tion of risk-taking in commercial banks for the future. 10 SAGE Open Table 6. Impact of Capital Ratios on RWA. (1) (2) (3) (5) (6) Variables RWA RWA RWA RWA RWA L.RWA 0.714*** 0.712*** 0.721*** 0.696*** 0.690*** (0.070) (0.070) (0.072) (0.062) (0.060) RBCR 0.242* (0.145) TIRBR 0.290 (0.177) CERBR 0.341* (0.196) TCAPR 0.506* (0.301) TITA 0.316* (0.186) ROA 0.263* 0.314** 0.248 0.342** 0.313** (0.154) (0.158) (0.152) (0.164) (0.153) LIQ −0.443*** −0.441*** −0.449*** −0.432*** −0.430*** (0.062) (0.063) (0.063) (0.057) (0.056) LG 0.206*** 0.205*** 0.203*** 0.194*** 0.194*** (0.064) (0.065) (0.064) (0.066) (0.066) ID 0.022** 0.022** 0.024*** 0.021** 0.021** (0.009) (0.009) (0.009) (0.009) (0.009) BE 0.003 0.004 0.004 0.004 0.004 (0.000) (0.000) (0.000) (0.000) (0.000) Size 0.001 0.001 0.001 0.001 0.001 (0.001) (0.001) (0.001) (0.001) (0.001) FD 0.186 0.205 0.160 −0.276 0.155 (0.188) (0.180) (0.196) (0.453) (0.202) MP 0.006** 0.007** 0.007** 0.006** 0.006** (0.002) (0.002) (0.002) (0.002) (0.002) Constant −0.074 −0.087 −0.077 0.192 −0.030 (0.079) (0.073) (0.075) (0.229) (0.100) Observations 14,944 14,928 14,944 14,928 14,928 Number of id 934 933 934 933 933 AR (2) 0.217 0.224 0.103 0.278 0.262 No. of Instruments 12 12 12 12 12 Hansen Statistics 0.602 0.545 0.772 0.578 0.538 Note. A two-step system GMM approach results and robust standard errors are reported. Robust Standard errors in parentheses. RWA = risk-weighted asset; RBCR = risk-based capital/risk; TIRBR = Tier I risk-based ratio; CERBR = common equity risk-based ratio; TCAPR = total Capital ratio; TITA = Tier I capital to total assets; LIQ = liquidity; LG = loan growth; ID = Income diversification; BE = bank efficiency; FD = funding diversity; MP = Market power; GMM = Generalized method of movement. *p < .1. **p < .05. ***p < .01. Abbas et al. 11 Table 7. Impact of Capital Buffer Ratios on Risk-Weighted Assets & Loan Loss Reserves. (1) (2) (3) (4) (5) (6) Variables RWA RWA RWA LLR LLR LLR L.RWA 0.712*** 0.708*** 0.723*** (0.070) (0.068) (0.072) BCBR 0.285* −0.013** (0.170) (0.00677) TIBR 0.283* −0.014** (0.169) (0.006) CEBR 0.302* −0.014** (0.174) (0.007) L.LLR 0.650*** 0.647*** 0.650*** (0.029) (0.028) (0.029) ROA 0.268* 0.281* 0.242 −0.029*** −0.030*** −0.027*** (0.155) (0.157) (0.151) (0.00673) (0.00681) (0.00634) LIQ −0.441*** −0.437*** −0.451*** 0.008*** 0.008*** 0.008*** (0.061) (0.059) (0.064) (0.001) (0.001) (0.001) LG 0.206*** 0.206*** 0.202*** 0.006*** 0.006*** 0.006*** (0.064) (0.064) (0.064) (0.000) (0.000) (0.000) ID 0.0225** 0.022** 0.024*** −0.003*** −0.003*** −0.003*** (0.009) (0.009) (0.009) (0.000) (0.000) (0.000) BE 0.003 0.003 0.004 −0.006*** −0.006*** −0.007*** (0.000) (0.000) (0.000) (0.001) (0.001) (0.001) Size 0.001 0.001 0.001 −0.002 −0.002 −0.005 (0.001) (0.001) (0.001) (0.005) (0.005) (0.006) FD 0.212 0.246 0.143 0.018*** 0.017*** 0.021*** (0.173) (0.155) (0.206) (0.006) (0.005) (0.007) MP 0.006** 0.006** 0.007** 0.002** 0.002** 0.001 (0.002) (0.002) (0.002) (0.009) (0.009) (0.000) Constant −0.069 −0.087 −0.050 −0.008*** −0.008*** −0.009*** (0.082) (0.073) (0.089) (0.003) (0.002) (0.003) Observations 14,944 14,944 14,944 14,880 14,880 14,880 Number of id 934 934 934 930 930 930 AR (2) 0.216 0.212 0.191 0.448 0.455 0.457 No. of Instrument 12 12 12 12 12 12 Hansen Statistics 0.589 0.574 0.789 0.888 0.832 0.745 Note. A two-step system GMM approach results and robust standard errors are reported. Robust Standard errors in parentheses. RWA = risk-weighted asset; LLR = loan loss reserves; TIBR = Tier-I buffer ratio; CEBR = Common equity buffer ratio; LIQ = Liquidity; LG = Loan growth; ID = Income diversification; BE = Bank Efficiency; FD = Funding diversity; MP = Market power; GMM = Generalized method of movement. *p < .1. **p < .05. ***p < .01. Declaration of Conflicting Interests References The author(s) declared no potential conflicts of interest with respect Abbas, F., Butt, S., Masood, O., & Javaria, K. (2019). The effect to the research, authorship, and/or publication of this article. of bank capital buffer on bank risk and net interest margin: Evidence from the US. Studies, 5(2), 72–87. Funding Abbas, F., Iqbal, S., & Aziz, B. (2019). 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Econometrics, 126(1), 25–51.

Journal

SAGE Open – SAGE

Published: Jan 9, 2021

Keywords: bank capital ratios; non-performing loans; loan loss reserves; risk-weighted assets

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