Systemic Risk Measurement in a Banking Network Using Conditional VaR and Vine Copula

Investors and regulators have paid more attention to the the sensitivity of the nancial system since recent nancial crises (the collapse of Lehman Brothers and the European sovereign debt crisis in 2008). A bank s default role in systemic risk reveals and shows its importance to manage and control this kind of risk because of its destructive e ects on nancial system. Using mathematical and statistical tools, we measure the systemic impact of nancial distress among simulated bank return data, and empirical data from some Iranian banks using conditional value at risk (CoVaR) as a systemic risk measure. In fact, it is important to know how the fragile position of one nancial institution could impair the performance of other nancial institutions. We model multivariate dependence between simulated data and empirical Iranian banks data using a hierarchical tree structure given by a vine copula, the dependence between and among these banks is identi ed. For about 1000 points in time for simulated data and from 2009 to 2018 for Iranian banks, we determine the increases and decreases in systemic risk. It is identi ed if any of them has a predominant or minor role in risk transmission. These results have implications for the regulation of capital in nancial institutions and for investor s risk management decisions.

Investors and regulators have paid more attention to the the sensitivity of the nancial system since recent nancial crises (the collapse of Lehman Brothers and the European sovereign debt crisis in 2008). A bank s default role in systemic risk reveals and shows its importance to manage and control this kind of risk because of its destructive e ects on nancial system. Using mathematical and statistical tools, we measure the systemic impact of nancial distress among simulated bank return data, and empirical data from some Iranian banks using conditional value at risk (CoVaR) as a systemic risk measure. In fact, it is important to know how the fragile position of one nancial institution could impair the performance of other nancial institutions. We model multivariate dependence between simulated data and empirical Iranian banks data using a hierarchical tree structure given by a vine copula, the dependence between and among these banks is identi ed. For about 1000 points in time for simulated data and from 2009 to 2018 for Iranian banks, we determine the increases and decreases in systemic risk. It is identi ed if any of them has a predominant or minor role in risk transmission. These results have implications for the regulation of capital in nancial institutions and for investor s risk management decisions.