摘要翻译：
这篇论文侧重于对新冠肺炎疫情对世界不同国家金融市场影响的经济合理的稳健分析。它提供了对北美和南美、欧洲和亚洲23个国家主要股票指数回报的稳健估计和预测回归的结果，并纳入了报告的新冠肺炎感染和死亡的时间序列。我们还详细研究了COVID-19感染和死亡率时间序列的持久性、重尾性和尾部风险特性，这些特性激发了在分析中应用鲁棒推理方法的必要性。经济合理分析是基于异方差和自相关一致(HAC)推断方法，最近发展起来的稳健$T$-统计推断方法和稳健尾指数估计。
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英文标题：
《COVID-19: Tail Risk and Predictive Regressions》
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作者：
Walter Distaso, Rustam Ibragimov, Alexander Semenov, Anton Skrobotov
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/2009.02486