Clustering volatility regimes for dynamic trading strategies.

We develop a new method to find the number of volatility regimes in a non-stationary financial time series. We use change point detection to partition a time series into locally stationary segments, then estimate the distributions of each piece. The distributions are clustered into a learned number of discrete volatility regimes via an optimisation routine. Using this method, we investigate and determine a clustering structure for indices, large cap equities and exchange-traded funds. Finally, we create and validate a dynamic portfolio allocation strategy that learns the optimal match between the current distribution of a time series with its past regimes, thereby making online risk-avoidance decisions in the present.