Abstract:Risk management became an important dilemma in the banking literature and has gained consideration since the financial crisis of 2007-08 which brought numerous challenges for most organizations. More than 325 banks' failure was reported in the United States during the worldwide financial crisis. The high number of banks failures needs to evaluate the risk management efficiency of banking institutions of Pakistan. In this study, we used the PVAR model and Simultaneous equation approach to examine the link betwe… Show more
“…The reciprocal affiliation between credit and liquidity risks was examined by Ghenimi (2017) and Ahmad (2019) through employing the TSLS and panel vector auto-regression models. Impaired loan ratio used as proxy for Credit risk & ratio of liquid assets for liquidity.…”
Section: Empirical Studies the Relationship Between Liquidity Risk An...mentioning
confidence: 99%
“…Through empirical analysis, Ghenimi (2017) and Ahmad (2019) examined the impacts of CR and LR on bank stability. The bank's stability is measured by Z-score, the distance to insolvency.…”
Section: The Impact Of Credit Risk and Liquidity Risk On Stability Of...mentioning
confidence: 99%
“…Previous studies and papers on effect of liquidity and credit risks on bank stability used different economic models including two/three stage least square, panel vector autoregression models, seemingly unrelated regression, pearson correlation, experimental research, generalized method of moment (GMM), fixed and random effects models. Since the nature of data under this study is panel dataset that includes both cross sectional and time series and the past studies by (Ghenimi, 2017;Ahmad, 2019) we used the two stage least square to examine the mutual relationship between liquidity risk and credit risk and to investigate the effect of credit risk and liquidity risk on bank stability we employed the panel data regression model fixed effects as applied in the past study by (Ahmad, 2019).…”
Section: Econometric Tools and Model Designmentioning
confidence: 99%
“…For this study, the relationship between credit and liquidity risks was evaluated by the TSLS model. This model was applied by (Shen, 2018 andAhmad, 2019.…”
Section: Two-stage-least-squares "Tsls"mentioning
confidence: 99%
“…To analysis and examine the impact of Credit risk and liquidity risk on stability of banks the panel data regression model is used. This model was applied by (Ahmad, 2019).…”
Section: Panel Data Regression Model (Fixed Effect Model)mentioning
This study investigates the effects of liquidity and credit risks on the stability of banks with empirical evidence from the Afghanistan banking sector over the period 2014–2020. The stability of a bank is measured through the dependent variable of its capital adequacy ratio. Credit risk (calculated by the ratio of impaired loans) is included as an independent variable along with liquidity risk. The bank specific factors, namely bank net interest margin, size of the bank, return on assets, loan growth rate, liquidity gap, return on equity, loan to asset and macro-economic factors, inflation and GDP growth rate are included as control variables. This study includes all 10 operationalized banks in Afghanistan, excluding the two branches of foreign banks. The panel dataset was collected from banks’ websites and the macroeconomic data was derived from World Bank reports. This study employed the simultaneous equation approach of a two-stage least square and a fixed effect panel regression model to investigate the affiliation between liquidity and credit risks and their effects on the stability of banks. The results of this study indicate that liquidity and credit risks don’t have a mutual relationship, while the interaction of both types of risks jointly impacts bank stability. It shows that NIM, loan assets, ROA, liquidity gap, loan growth rate, and ROE have positive impacts on bank stability, whereas the size of the bank has negative effects on bank stability. Among the macroeconomic variables, only the growth rate of GDP signifies a negative effect on the stability of banks. The finding under this paper recommends that the governance body of the banking sector drafts policies aimed at strengthening bank capital and taking liquidity measurement according to the best standards introduced by the Basel committee. Also, to create frameworks for measuring liquidity and capital standards.
“…The reciprocal affiliation between credit and liquidity risks was examined by Ghenimi (2017) and Ahmad (2019) through employing the TSLS and panel vector auto-regression models. Impaired loan ratio used as proxy for Credit risk & ratio of liquid assets for liquidity.…”
Section: Empirical Studies the Relationship Between Liquidity Risk An...mentioning
confidence: 99%
“…Through empirical analysis, Ghenimi (2017) and Ahmad (2019) examined the impacts of CR and LR on bank stability. The bank's stability is measured by Z-score, the distance to insolvency.…”
Section: The Impact Of Credit Risk and Liquidity Risk On Stability Of...mentioning
confidence: 99%
“…Previous studies and papers on effect of liquidity and credit risks on bank stability used different economic models including two/three stage least square, panel vector autoregression models, seemingly unrelated regression, pearson correlation, experimental research, generalized method of moment (GMM), fixed and random effects models. Since the nature of data under this study is panel dataset that includes both cross sectional and time series and the past studies by (Ghenimi, 2017;Ahmad, 2019) we used the two stage least square to examine the mutual relationship between liquidity risk and credit risk and to investigate the effect of credit risk and liquidity risk on bank stability we employed the panel data regression model fixed effects as applied in the past study by (Ahmad, 2019).…”
Section: Econometric Tools and Model Designmentioning
confidence: 99%
“…For this study, the relationship between credit and liquidity risks was evaluated by the TSLS model. This model was applied by (Shen, 2018 andAhmad, 2019.…”
Section: Two-stage-least-squares "Tsls"mentioning
confidence: 99%
“…To analysis and examine the impact of Credit risk and liquidity risk on stability of banks the panel data regression model is used. This model was applied by (Ahmad, 2019).…”
Section: Panel Data Regression Model (Fixed Effect Model)mentioning
This study investigates the effects of liquidity and credit risks on the stability of banks with empirical evidence from the Afghanistan banking sector over the period 2014–2020. The stability of a bank is measured through the dependent variable of its capital adequacy ratio. Credit risk (calculated by the ratio of impaired loans) is included as an independent variable along with liquidity risk. The bank specific factors, namely bank net interest margin, size of the bank, return on assets, loan growth rate, liquidity gap, return on equity, loan to asset and macro-economic factors, inflation and GDP growth rate are included as control variables. This study includes all 10 operationalized banks in Afghanistan, excluding the two branches of foreign banks. The panel dataset was collected from banks’ websites and the macroeconomic data was derived from World Bank reports. This study employed the simultaneous equation approach of a two-stage least square and a fixed effect panel regression model to investigate the affiliation between liquidity and credit risks and their effects on the stability of banks. The results of this study indicate that liquidity and credit risks don’t have a mutual relationship, while the interaction of both types of risks jointly impacts bank stability. It shows that NIM, loan assets, ROA, liquidity gap, loan growth rate, and ROE have positive impacts on bank stability, whereas the size of the bank has negative effects on bank stability. Among the macroeconomic variables, only the growth rate of GDP signifies a negative effect on the stability of banks. The finding under this paper recommends that the governance body of the banking sector drafts policies aimed at strengthening bank capital and taking liquidity measurement according to the best standards introduced by the Basel committee. Also, to create frameworks for measuring liquidity and capital standards.
This article aims at illustrating the link between the bank risk, regulatory capital, and the bank performance. By using a panel dynamic data in the GMM estimator on a large dataset of 73 banks belonging to Golf Cooperation Countries from 2000 to 2018, we have discovered that both the regulatory capital and the bank performance have a negative association with bank risk. Thus, we have analyzed the link between the bank risk and the bank performance on regulatory capital and discovered that the bank risk negatively influences the bank performance. However, with banks with larger capital ratios the bank performance improves noticeably. Finally, we finished by demonstrating the impact of the financial crisis on these relationships, and our robustness check confirms our major findings.
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