Identifying Systemically Important Banks in Pakistan: A Quantile Regression Analysis

  •  Shumaila Zeb    
  •  Abdul Rashid    


The basic purpose of this study is to identify the systemically important banks of Pakistan using an unbalanced panel dataset of 21 commercial banks. The study covers the period 2004-2014. The systemic risk of financial institutes is calculated by using Conditional Value at Risk (CoVaR) approach. Specifically, first, the VaR and CoVaR are obtained as predicted values of quantile regression of individual and market losses. The state variables included in the analysis are the change in three month yield of treasury bills, the change in slope of yield curve, the inflation rate, monthly market returns, and the equity volatility. The study shows that the state variables have significant impacts on the CoVaR of financial institutions. The results of the study helps policy makers and regulatory authorities to revise policies and regulation by keeping in mind systemically important banks in the economy to reduce the chances of a financial debacle in future in Pakistan and thus rescuing the economy from financial crises.

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