COVID-19 AND STOCK MARKET VOLATILITY: A CLUSTERING APPROACH FOR S&P 500 INDUSTRY INDICES

The COVID-19 pandemic was the infectious disease outbreak that has had the strongest impact on the U.S. stock market. In this dissertation, we study how this impact affected some of the conditional volatilities of S&P 500 industries, using a new model feature-based clustering method on a fitted threshold generalised autoregressive conditional heteroscedasticity (TGARCH) specification. Rather than using the estimated model parameters to compute a distance matrix for the stock indices, which cannot capture all the information about the dependence of the time-varying variance, we suggest using a distance based on the autocorrelations of the estimated conditional volatilities. Both hierarchical (complete linkage) and non-hierarchical (k-means) unsupervised machine learning 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 U.S. COVID-19 case and the period during the pandemic, leading to the conclusion that the similarities or distances between industries underwent a significant change, with the industries most affected by the pandemic being Hotels, Consumer Durable & Apparel, Automobile, and Airlines. It was also made an analysis regarding the forecast accuracy of simple asymmetric GARCH models applied to S&P 500 industries and use the model forecast errors for different horizons to calculate a distance matrix for the stock indices. A hierarchical clustering algorithm is used to assign the set of industries into clusters. We found homogeneous clusters of industries in terms of the impact of COVID-19 on US stock market volatility. The industries most affected by the pandemic and with less accurate stock market prediction (Hotels, Resorts & Cruise Lines, Airline, Apparel, Accessories & Luxury Goods, and Automobile) are separated in Euclidean distance from those industries that were less impacted by COVID-19 and which had more accurate forecasting (Pharmaceuticals, Internet & Direct Marketing Retail, Data Processing & Outsourcing Services, and Movies & Entertainment).