Structural Clustering of Volatility Regimes for Dynamic Trading Strategies. Issue 3 (4th May 2021)

• Record Type: Journal Article
• Authors: Prakash, Arjun; James, Nick; Menzies, Max; Francis, Gilad
• Is Part Of: Applied mathematical finance. Volume 28:Issue 3(2021)
• Page Start: 236
• Page End: 274
• Publication Date: 2021-05-04
• Subjects: Volatility modelling -- regime-switching model -- change point detection -- spectral clustering -- trading strategies; Business mathematics -- Periodicals; 650.0151
• DOI: 10.1080/1350486X.2021.2007146
• Languages: English
• ISSNs: 1350-486X

Abstract

ABSTRACT: We develop a new method to find the number of volatility regimes in a nonstationary financial time series by applying unsupervised learning to its volatility structure. We use change point detection to partition a time series into locally stationary segments and then compute a distance matrix between segment distributions. The segments are clustered into a learned number of discrete volatility regimes via an optimization routine. Using this framework, we determine the volatility clustering structure for financial indices, large-cap equities, exchange-traded funds and currency pairs. Our method overcomes the rigid assumptions necessary to implement many parametric regime-switching models while effectively distilling a time series into several characteristic behaviours. Our results provide a significant simplification of these time series and a strong descriptive analysis of prior behaviours of volatility. Finally, we create and validate a dynamic trading strategy that learns the optimal match between the current distribution of a time series and its past regimes, thereby making online risk-avoidance decisions at present.