# Structural clustering of volatility regimes for dynamic trading strategies

arXiv: https://arxiv.org/abs/2004.09963 · HTML: https://mlsanity.com/paper/2004.09963/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Modeling Market States with Clustering and State Machines](https://mlsanity.com/paper/2510.00953/) (2025)
- [Improving S&P 500 Volatility Forecasting through Regime-Switching Methods](https://mlsanity.com/paper/2510.03236/) (2025)

## Abstract (arXiv)

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.