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COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices

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  • Lúcio, Francisco
  • Caiado, Jorge

Abstract

We study how the COVID-19 pandemic affected some of the conditional volatilities of S&P 500 industries, using a new model feature-based clustering method on a fitted TGARCH model. Rather than using the estimated model parameters to compute a distance matrix for the stock indices, we suggest using a distance based on the autocorrelations of the estimated conditional volatilities. Both hierarchical and non-hierarchical algorithms are used to assign the set of industries into clusters. The results show a clear change in the composition of each cluster between the period before the first US COVID-19 case and the period during the pandemic.

Suggested Citation

  • Lúcio, Francisco & Caiado, Jorge, 2022. "COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices," Finance Research Letters, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:finlet:v:49:y:2022:i:c:s1544612322003646
    DOI: 10.1016/j.frl.2022.103141
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    2. Hussein Hassan & Minko Markovski & Alexander Mihailov, 2023. "A TGARCH Quantification of the Average Effect of COVID-19 Cases on Share Prices by Sector: Comparing the US and the UK," Economics Discussion Papers em-dp2023-15, Department of Economics, University of Reading.
    3. Mariem Gaies & Walid Chkili, 2023. "Dynamic correlation and hedging strategy between Bitcoin prices and stock market during the Russo-Ukrainian war," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 307-319, June.

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    More about this item

    Keywords

    Autocorrelation; Cluster analysis; COVID-19; Threshold GARCH model; Unsupervised machine learning; S&P 500; Volatility;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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