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 distributionsof 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 equitiesand 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.
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. This empirical analysis could be used with other regime-switching implementations, justifying the parametric structure encoded in any candidate model. 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.
R
non-stationary
nonstationary
A
by applying unsupervised learning to its volatility structure
R
, then estimate the distributionsof each piece. The distributions
and then compute a distance matrix between segment distributions. The segments
R
optimisation
optimization
R
method, we investigate and determine a
framework, we determine a volatility
R
indices
financial indices, large-cap equities
D
large cap equitiesand
R
.
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. This empirical analysis could be used with other regime-switching implementations, justifying the parametric structure encoded in any candidate model.
R
portfolio allocation
trading
R
with
and