Uncertainty in Systemic Risks Rankings : Bayesian and Frequentist Analysis

In this paper we propose efficient Bayesian Hamiltonian Monte Carlo method for estimation of systemic risk measures, LRMES, SRISK and ΔCoVaR, and apply it for thirty global systemically important banks and for eighteen largest US financial institutions over the period of 2000-2020. The systemic risk measures are computed based on the Dynamic Conditional Correlations model with generalized asymmetric volatility. A policymaker may choose to rank the firms using some quantile of their systemic risk distributions such as 90, 95, or 99% depending on risk preferences with higher quantiles being more conservative