Title: Similarity-based clustering of extreme losses from the London stock exchange
Authors: Miguel de Carvalho - CEAUL (Centro de Estatistica e Aplicacoes), Universidade de Lisboa (Portugal) [presenting]
Raphael Huser - King Abdullah University of Science and Technology (Saudi Arabia)
Rodrigo Rubio - Pontificia Universidad Catolica de Chile (Chile)
Abstract: Rigorous analysis of the magnitude and the dynamics of extreme losses in a stock market is essential for institutional investors, professional money managers, and traders. We develop statistical methods of similarity-based clustering for heteroscedastic extremes, which allow us to group stocks according to their extreme-value index and scedasis function, i.e., the magnitude and dynamics of their extreme losses, respectively. Clustering is performed in a product-space and a tuning parameter is used to control whether more emphasis should be put on the latter or the former. This provides a practical tool to gain more insight into stocks synchronization during periods of stress, and can thus be practically useful for risk management. The analysis reveals an interesting mismatch between the magnitude and dynamics of extreme losses on the London Stock Exchange and the corresponding economic sectors of the affected stocks.