Structural clustering of volatility regimes for dynamic trading strategies

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.

Accepted manuscript

References in corpus (2)

• Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach
• Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19

Cited by in corpus (11)

• Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time
• Economic state classification and portfolio optimisation with application to stagflationary environments
• Global and regional changes in carbon dioxide emissions: 1970-2019
• Spatio-temporal trends in the propagation and capacity of low-carbon hydrogen projects
• In search of peak human athletic potential: A mathematical investigation
• Semi-metric portfolio optimization: a new algorithm reducing simultaneous asset shocks
• Distributional trends in the generation and end-use sector of low-carbon hydrogen plants
• Equivalence relations and $L^p$ distances between time series with application to the Black Summer Australian bushfires
• Collective infectivity of the pandemic over time and association with vaccine coverage and economic development
• On financial market correlation structures and diversification benefits across and within equity sectors
• Optimally adaptive Bayesian spectral density estimation for stationary and nonstationary processes