论文标题
COVID-19：尾巴风险和预测性回归
COVID-19: Tail Risk and Predictive Regressions
论文作者
论文摘要
该论文着重于对共证于199大流行对全球不同国家金融市场的影响的经济性合理的强大分析。它提供了对北美和南美，欧洲和亚洲23个国家的主要股票指数回报预测回归的强大估计结果的结果，并结合了COVID-19的时间序列的感染和死亡时间序列。我们还介绍了COVID-19感染和死亡率的时间序列的持久性，重尾和尾巴风险特性的详细研究，这激发了分析中强大的推理方法的应用。经济学上合理的分析基于异性恋性和自相关一致（HAC）推理方法，最近开发了强大的$ t $统计推理方法和稳健的尾部指数估计。
The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 infections and death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on heteroskedasticity and autocorrelation consistent (HAC) inference methods, recently developed robust $t$-statistic inference approaches and robust tail index estimation.
