We propose efficient Bayesian Hamiltonian Monte Carlo method for estimation of systemic risk measures, LRMES, SRISK and $\Delta CoVaR$, and apply it for thirty global systemically important banks and for eighteen largest US financial institutions over the period of 2000–2020. The advantage of the Hamiltonian method is an efficient estimation of all parameters jointly in high dimensional models and providing posterior distributions incorporating parameter uncertainty. The systemic risk measures are computed based on the Dynamic Conditional Correlations model with generalized asymmetric volatility. We estimate the systemic risks posterior distributions and two-step maximum likelihood distributions with bootstrap simulations for LRMES. The systemic risk rankings at different quantiles of the distributions vary considerably using bootstrap approach for computation of LRMES and SRISK, and are more stable with Bayesian posterior distributions using a parametric model. 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.

我们提出了有效的贝叶斯哈密顿蒙特卡洛方法来估计系统性风险措施、LRMES、SRISK 和$△CoVAR$，并将其应用于 2000 年至 2020 年期间的 30 家全球系统重要性银行和 18 家最大的美国金融机构。哈密​​顿方法的优点是在高维模型中对所有参数进行有效联合估计，并提供包含参数不确定性的后验分布。系统性风险措施是根据具有广义非对称波动率的动态条件相关模型计算的。我们使用 LRMES 的自举模拟估计系统风险后验分布和两步最大似然分布。使用用于计算 LRMES 和 SRISK 的引导程序方法，分布的不同分位数的系统风险排名差异很大，并且使用参数模型的贝叶斯后验分布更稳定。