 Filter‐based portfolio strategies in an HMM setting with varying correlation parametrizationsChristina Erlwein‐Sayer, Stefanie Grimm, Peter Ruckdeschel, Jörn Sass and Tilman Sayer Applied Stochastic Models in Business and Industry, 2020, vol. 36, issue 3, 307-334 Abstract: We consider portfolio optimization 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: statewise 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 expectation‐maximization (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 naïve 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 second study using real data confirms these findings. Date: 2020 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:36:y:2020:i:3:p:307-334 Access Statistics for this article More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons |
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