Structural clustering of volatility regimes for dynamic trading strategies

Arjun Prakash, Nick James, Max Menzies and Gilad Francis

Papers from arXiv.org

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 a 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 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 in the present.

Date: 2020-04, Revised 2021-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-gen and nep-rmg
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Citations: View citations in EconPapers (9)

Published in Applied Mathematical Finance 28, 236-274 (2022)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2004.09963

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