COVID-19: Tail Risk and Predictive Regressions

TL;DR

This paper analyzes the impact of COVID-19 on global financial markets using robust econometric methods, focusing on tail risk, persistence, and heavy-tailed properties of infection and death data across 23 countries.

Contribution

It introduces a robust econometric framework for analyzing COVID-19 effects on stock returns, incorporating tail risk and heavy-tailedness in the data, with applications across multiple countries.

Findings

Robust estimation shows significant COVID-19 effects on stock returns.

COVID-19 infection and death rates exhibit heavy tails and persistence.

Tail risk properties are crucial for accurate financial market analysis during the pandemic.

Abstract

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.