THE ESTIMATION RISK IN EXTREME SYSTEMIC RISK FORECASTS
提出一种基于去波动化和极值理论的新方法，用于预测边际预期缺口（MES）这一系统性风险指标，并证明其渐近正态性，模拟显示预测区间覆盖良好，对美国大银行的应用表明预测精度随时间显著变化，对监管有启示。
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 U.S. banks illustrates the significant time variation in the precision of MES forecasts, and explores the implications of this fact from a regulatory perspective.