The nexus between credit risk and liquidity risk and their impact on banks financial performance: Evidence from Pakistan

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 between Liquidity Risk and Credit Risk and its influence on banks' performance working in Pakistan. The panel data was collected from 33 banking institutions between the period 2008-2018. The results revealed that Credit Risk and Liquidity Risk are not interrelated with each other. However, the two risks independently influence the banks' performance and their relative interaction plays a major role in the instability of the banking sector. The findings form the foundation for recent regulatory exertions to better understand the two types of risks and to strengthen the joint management of both liquidity risk and credit risk.

Effect of Credit and Liquidity Risks on Bank Stability: Empirical Evidence from Afghanistan

“…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.…”

“…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.…”

“…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).…”

“…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.…”

“…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).…”