The objective of this essay is to investigate the effects of Covid-19 on the volatility of individual asset markets as well as the correlation between those markets using the Dynamic Conditional Correlation GARCH methodology developed by Engle (2002). The investigated assets are the major world equity indices as well as oil, gold, and bitcoin. I have found significant volatility clustering over the entire spectrum of assets, as well as increases in the correlation between assets during the initial phase of the pandemic. Furthermore, gold and bitcoin are shown to exhibit relatively low correlations with the investigated equity markets and may hence act as important components of a robust portfolio during turbulent times. While no direct effect of Covid-19 related policy variables on the returns could be established for all assets, the results indicate that the response of financial markets was immediate and not dependent on the national exposure to the pandemic itself. Finally, all markets are shown to recover within a reasonably short time span.
Table of Contents
1 Introduction
2 Background
3 Data
4 Method
5 Results and Discussion
6 Conclusion
References
Appendices
a Imputed Data
b DCC Diagrams
Research Objectives and Key Themes
This essay investigates the impact of the Covid-19 pandemic on the volatility of major global asset markets and examines the shifting correlations between these assets, aiming to determine whether financial contagion occurred and if safe-haven assets remained effective during the crisis.
- Analysis of volatility clustering in global equity indices, commodities (Gold, Oil), and Bitcoin.
- Investigation of time-dependent correlations using the DCC-GARCH modeling approach.
- Evaluation of the influence of pandemic-related exogenous variables on market returns.
- Assessment of safe-haven asset performance and portfolio diversification benefits during extreme market turbulence.
Excerpt from the Book
1 INTRODUCTION
Covid-19 is a contagious disease, which manifests itself as pneumonia in humans (see Sohrabi et al. 2020 for a medical overview). The first cases were reported in late December 2019 in Wuhan, one of the most populous cities in China. Due to its infectiousness, the WHO declared a Public Health Emergency of International Concern on January 30th, 2020. In the 100 days that followed, the virus infected more than 1 million people with an initial mortality rate of approximately 5% (Ali et al 2020). On March 11th, 2020, the WHO declared Covid-19 a pandemic. As of now (February 2022) Covid-19 has taken more than 5.9 million lives and infected more than 424 million people worldwide.
Initial reactions of many governments around the world were aimed at limiting – or at least slowing – the rate of infection. These practices amplified the uncertainty already inherent in the pandemic itself (e.g. uncertainties with respect to the infectiousness and lethality of the virus, the capacities of the healthcare systems, the development of vaccines, . . . ) by uncertainties related to the survival of businesses, effects on the accumulation of human capital (especially formal education), persistence of governmental policies, et cetera (see Baker et al. 2020). Combined, this has led to a unique disruption of life on our planet.
While Covid-19 had immediate effects on the real economy, delays in the availability of data makes the study of financial markets – as a leading indicator – important (Bai et al. 2020). Furthermore, financial markets and their volatility are intimately connected with the real economy, affecting anything from risk management to investment and consumption plans to regulatory decisions (Baker et al. 2020).
Summary of Chapters
1 Introduction: This chapter outlines the global spread of the Covid-19 pandemic and its disruptive impact on the real economy and financial markets, establishing the research objective to investigate volatility and market correlations.
2 Background: This section reviews existing literature on financial market volatility, the impact of pandemic-related uncertainty, and the integration of global financial systems, providing a theoretical foundation for the analysis.
3 Data: This chapter describes the financial datasets, including equity indices, commodities, and cryptocurrency, and the exogenous Covid-19 variables used, along with the data processing techniques applied to ensure consistency.
4 Method: This section presents the econometric framework, specifically the DCC-GARCH model, explaining how it is utilized to analyze conditional volatility and time-varying correlations between assets.
5 Results and Discussion: This chapter presents the empirical findings from the GARCH models and the DCC analysis, highlighting evidence of volatility clustering and financial contagion across markets.
6 Conclusion: This final section synthesizes the results, concluding that while Covid-19 significantly increased market correlations and volatility, a well-balanced portfolio remains the best strategy against such systemic shocks, and suggests directions for future research.
Keywords
Covid-19, Financial Markets, Volatility, DCC-GARCH, Asset Correlation, Financial Contagion, Portfolio Diversification, Equity Indices, Commodities, Bitcoin, Safe-haven Assets, Market Turmoil, Quantitative Easing, Economic Uncertainty, Risk Management
Frequently Asked Questions
What is the core focus of this research paper?
The paper examines the impact of the Covid-19 pandemic on the volatility of various global asset markets and analyzes how the correlation between these assets changed during the crisis.
Which asset classes are examined in this study?
The study includes major world equity indices from G7 and BRIC countries, commodities (Gold and WTI Oil), and Bitcoin.
What is the primary objective of the empirical analysis?
The objective is to identify volatility clustering and to investigate whether there is evidence of financial contagion by measuring time-dependent correlations between these different asset markets.
Which statistical methods are employed for this analysis?
The author uses a DCC-ARMA-GARCH model, which allows for the estimation of time-varying conditional correlations and individual asset volatilities.
What does the main body of the work cover?
The main body covers a literature review on financial market volatility, a detailed explanation of the data sources, the methodology behind the volatility modeling, and an extensive discussion of the results, including visual representations of correlation spikes.
What are the characterizing keywords of this study?
Key terms include Covid-19, financial markets, volatility, DCC-GARCH, asset correlation, financial contagion, and portfolio diversification.
How does the pandemic affect the correlation between assets according to the results?
The study finds clear evidence of increased correlation between all investigated assets at the start of the pandemic, suggesting financial contagion across markets and regions.
Does the paper identify a reliable safe-haven asset?
The findings suggest that Gold remains the preferred choice for a safe-haven asset, with Bitcoin placing second, though the author notes that a diversified portfolio across multiple markets is likely more optimal than relying on a single asset.
How did the author handle missing data in the financial time series?
Because the sample was relatively short, the author used Kalman smoothing to fill in missing data points caused by national holidays, rather than omitting observations.
Why are the Covid-19 predictors largely insignificant in the GARCH models?
The author notes that while statistically significant in some cases, the predictors (like new cases or deaths) had a relatively small absolute impact on market returns, suggesting other factors were also driving market movements.
- Arbeit zitieren
- Niklas Humann (Autor:in), 2022, The Volatility In Financial Markets During The Covid-19 Pandemic, München, GRIN Verlag, https://www.grin.com/document/1192934
