Systemic risk measures have been shown to be predictive of financial crises and declines in real activity. Thus, forecasting them is of major importance in finance and economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (MES). It is based on first de-volatilizing the observations and, then, calculating systemic risk for the residuals using an estimator based on extreme value theory. We show the validity of the method by establishing the asymptotic normality of the MES forecasts. The good finite-sample coverage of the implied MES forecast intervals is confirmed in simulations. An empirical application to major US banks illustrates the significant time variation in the precision of MES forecasts, and explores the implications of this fact from a regulatory perspective.

翻译：系统性风险指标已被证明能够预测金融危机和实际经济活动的衰退。因此，预测这些指标在金融和经济领域具有重大意义。本文提出了一种新的系统性风险预测方法，该方法以边际期望损失（MES）为衡量标准。其基本原理是，首先对观测值进行去波动化处理，然后基于极值理论估计量计算残差的系统性风险。我们通过建立MES预测的渐近正态性验证了该方法的有效性。模拟结果证实了隐含的MES预测区间具有良好的有限样本覆盖性质。对美国主要银行的实证分析表明，MES预测精度存在显著的时间变化，并从监管角度探讨了这一事实的启示。