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Filter‐based portfolio strategies in an HMM setting with varying correlation parametrizations

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  • Christina Erlwein‐Sayer
  • Stefanie Grimm
  • Peter Ruckdeschel
  • Jörn Sass
  • Tilman Sayer

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.

Suggested Citation

  • Christina Erlwein‐Sayer & Stefanie Grimm & Peter Ruckdeschel & Jörn Sass & Tilman Sayer, 2020. "Filter‐based portfolio strategies in an HMM setting with varying correlation parametrizations," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(3), pages 307-334, May.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:3:p:307-334
    DOI: 10.1002/asmb.2491
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    References listed on IDEAS

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    1. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    2. Bulla, Jan & Mergner, Sascha & Bulla, Ingo & Sesboüé, André & Chesneau, Christophe, 2010. "Markov-switching Asset Allocation: Do Profitable Strategies Exist?," MPRA Paper 21154, University Library of Munich, Germany.
    3. Jörn Sass & Ulrich Haussmann, 2004. "Optimizing the terminal wealth under partial information: The drift process as a continuous time Markov chain," Finance and Stochastics, Springer, vol. 8(4), pages 553-577, November.
    4. Robert J. Elliott & John van der Hoek, 1997. "An application of hidden Markov models to asset allocation problems (*)," Finance and Stochastics, Springer, vol. 1(3), pages 229-238.
    5. Jörn Sass & Manfred Schäl, 2014. "Numeraire portfolios and utility-based price systems under proportional transaction costs," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 195-234, October.
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