Structural clustering of volatility regimes for dynamic trading strategies
This work addresses the challenge of modeling volatility in financial markets for traders and analysts, offering a method that is less restrictive than parametric models but is incremental in its approach.
The authors tackled the problem of identifying volatility regimes in nonstationary financial time series by developing an unsupervised learning method that clusters segments into discrete regimes, resulting in a dynamic trading strategy for online risk-avoidance decisions.
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.