Forecasting systemic risk measures using a dynamic semiparametric approach based on the Asymmetric Laplace distribution

The CoVaR and CoES are two of the most widely used measures of systemic risk in economics and finance. In this paper, we introduce a novel quantile regression approach for jointly estimating CoVaR and CoES. This method extends existing Asymmetric Laplace (AL) joint estimation techniques for Value at Risk (VaR) and Expected Shortfall (ES) to the realm of systemic risk measures. We generalize the joint quantile regression model to a time-varying setting by allowing the multiplicative factor between CoVaR and CoES to vary over time using a score-driven dynamic approach. We address the inference problem by developing a suitable likelihood-based Expectation-Maximization (EM) algorithm. We apply the new model to real data from the Chinese stock market, covering the period from 2010 to 2023. The results indicate that our models outperform their constant multiplicative factor counterparts and alternative GARCH-type models. In risk management applications, we construct a Skewness Mean-Variance (SMV) portfolio to manage risk exposure to system-wide distress. Finally, we employ network techniques to capture systemic risk contagion among industries and to monitor the market’s systemic risk level dynamically.