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A1716
Title: COVID-19: Tail risk and predictive regressions Authors:  Walter Distaso - Imperial College London (United Kingdom)
Rustam Ibragimov - Imperial College London and St. Petersburg State University (United Kingdom) [presenting]
Alexander Semenov - University of Florida and Saint Petersburg State University (United States)
Anton Skrobotov - Russian Presidential Academy of National Economy and Public Administration and SPBU (Russia)
Abstract: The focus is on an 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-statistic inference approaches and robust tail index estimation.