Title: Varying correlation parametrizations in an HMM setting for filter-based portfolio strategies
Authors: Christina Erlwein-Sayer - University of Applied Sciences HTW Berlin (Germany) [presenting]
Stefanie Grimm - Fraunhofer Institute of Industrial Mathematics ITWM (Germany)
Peter Ruckdeschel - University of Oldenburg (Germany)
Joern Sass - University of Kaiserslautern (Germany)
Tilman Sayer - Advanced Logic Analytics (United Kingdom)
Abstract: Portfolio optimization is considered in a regime-switching market. The assets of the portfolio are modeled through a hidden Markov model (HMM) in discrete time, where drift and volatility of the single assets are allowed to switch between different states. We consider different parametrizations of the involved asset covariances namely state-wise uncorrelated assets (though linked through the common Markov chain), assets correlated in a state-independent way, and assets where the correlation varies from state to state. As a benchmark we also consider a model without regime switches. We utilize a filter-based EM-algorithm to obtain optimal parameter estimates within this multivariate HMM and present parameter estimators in all three HMM settings. We discuss the impact of these different models on the performance of several portfolio strategies. Our findings show that for simulated returns our strategies in many settings outperform na\"ive investment strategies, like the equal weights strategy. Information criteria can be used to detect the best model for estimation as well as for portfolio optimization. A study using real data confirms these findings.