Measuring Systemic Risk Using Multivariate Quantile-Located ES Models

Abstract

We examine the tail systemic risk between the global financial system and financial institutions that belong to different industry groups. Our main contribution is the development of a systemic risk measure Delta Quantile-Located Conditional Autoregressive Expected Shortfall, ΔQLMV−CoCARES. This new measure captures the extreme downside risk in terms of the ES of the system should both the financial system and the institution simultaneously be in distress. The evidence suggests that cross significant volatility and ES effects exist between the system and financial institutions. Furthermore, our measure presents better forecasting performance than standard or novel systemic risk measures based on VaR such as CoVaR or ΔQLMV−CoCAViaR and it is effective at predicting financial crises. We also develop a new systemic stress indicator SSIES based on ΔQLMV−CoCARES systemic risk measure which presents higher forecasting ability than other standard stress indicators.