Corpus ID: 216035747

Clustering volatility regimes for dynamic trading strategies

@article{Francis2020ClusteringVR,
  title={Clustering volatility regimes for dynamic trading strategies},
  author={G. Francis and Nick James and Max Menzies and A. Prakash},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.09963}
}

G. Francis, Nick James, +1 author A. Prakash

Published 2020

Computer Science, Economics

ArXiv

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… CONTINUE READING

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