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

Francisco Lúcio and Jorge Caiado

Finance Research Letters, 2022, vol. 49, issue C

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.

Keywords: Autocorrelation; Cluster analysis; COVID-19; Threshold GARCH model; Unsupervised machine learning; S&P 500; Volatility (search for similar items in EconPapers)
JEL-codes: C22 C32 C38 G11 G17 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:49:y:2022:i:c:s1544612322003646

DOI: 10.1016/j.frl.2022.103141

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