Systemic Risk Estimation under Dynamic Volatility Matrix Models.

This paper proposes a two-step procedure for systemic risk estimation under the stochastic volatility/correlation models. The first step utilizes Fourier transform method for dynamic volatility matrix estimation, and the second step develops efficient importance sampling estimators for extreme event probability. For the empirical analysis, we find that the systemic risk can be useful to measure the stability of financial system because it seems be able to provide early signs for institutions in U.S. during the 2008-2010 financial crisis. Moreover, it can serve as a predictor of the capital injections during the crisis. SRISK in China and Taiwan are also compared.