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COVID-19: Tail risk and predictive regressions
Walter Distaso1, Rustam Ibragimov1,2, Alexander Semenov2,3
1Imperial College London, Business School, London, United Kingdom.
Plos One
|December 1, 2022
Summary
The COVID-19 pandemic significantly impacted global financial markets. This study used robust econometric methods to analyze these effects on stock market returns across 23 countries.
Area of Science:
- Econometrics
- Financial Markets Analysis
- Epidemiology
Background:
- The COVID-19 pandemic presented unprecedented challenges to global financial markets.
- Understanding the pandemic's economic impact, particularly on stock market volatility, is crucial for policymakers and investors.
- Previous analyses may not have fully accounted for the unique statistical properties of pandemic-related data.
Purpose of the Study:
- To conduct an econometrically sound, robust analysis of the COVID-19 pandemic's effects on financial markets worldwide.
- To estimate the impact of COVID-19 infections and deaths on stock market returns in 23 countries.
- To investigate the statistical characteristics of COVID-19 data and justify the use of robust inference methods.
Main Methods:
- Econometrically justified robust analysis.
- Robust estimation and inference on predictive regressions for stock index returns.
- Analysis of time series properties including persistence, heavy-tailedness, and tail risk of COVID-19 data.
- Application of heteroskedasticity and autocorrelation consistent (HAC) inference, robust t-statistic inference, and robust tail index estimation.
Main Results:
- The study provides robust estimates of the pandemic's effects on stock market returns across diverse geographical regions (North and South America, Europe, Asia).
- Detailed characterization of COVID-19 infection and death rate time series reveals properties necessitating robust statistical approaches.
- The findings highlight the importance of advanced econometric techniques for reliable financial market analysis during crises.
Conclusions:
- Robust econometric methods are essential for accurately assessing the impact of the COVID-19 pandemic on financial markets.
- The analysis confirms significant effects of the pandemic on stock market returns globally.
- The study underscores the need for sophisticated statistical tools to handle the complexities of crisis-driven economic data.
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