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Markov-switching Asset Allocation: Do Profitable Strategies Exist?

Author

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  • Bulla, Jan
  • Mergner, Sascha
  • Bulla, Ingo
  • Sesboüé, André
  • Chesneau, Christophe

Abstract

This paper proposes a straightforward Markov-switching asset allocation model, which reduces the market exposure to periods of high volatility. The main purpose of the study is to examine the performance of a regime-based asset allocation strategy under realistic assumptions, compared to a buy and hold strategy. An empirical study, utilizing daily return series of major equity indices in the US, Japan, and Germany over the last 40 years, investigates the performance of the model. In an out-of-sample context, the strategy proves profitable after taking transaction costs into account. For the regional markets under consideration, the volatility reduces on average by 41%. Additionally, annualized excess returns attain 18.5 to 201.6 basis points.

Suggested Citation

  • 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.
    • Handle: RePEc:pra:mprapa:21154
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    File URL: https://mpra.ub.uni-muenchen.de/21154/1/MPRA_paper_21154.pdf
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    References listed on IDEAS

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    Cited by:

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    3. Alexander Berglund & Massimo Guidolin & Manuela Pedio, 2020. "Monetary policy after the crisis: A threat to hedge funds' alphas?," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 219-238, May.
    4. Peter Nystrup & Henrik Madsen & Erik Lindström, 2018. "Dynamic portfolio optimization across hidden market regimes," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 83-95, January.
    5. Giulia Dal Pra & Massimo Guidolin & Manuela Pedio & Fabiola Vasile, 2016. "Do Regimes in Excess Stock Return Predictability Create Economic Value? An Out-of-Sample Portfolio Analysis," BAFFI CAREFIN Working Papers 1637, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    6. Elizabeth Fons & Paula Dawson & Jeffrey Yau & Xiao-jun Zeng & John Keane, 2019. "A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing," Papers 1902.10849, arXiv.org.
    7. Peter Nystrup & Stephen Boyd & Erik Lindström & Henrik Madsen, 2019. "Multi-period portfolio selection with drawdown control," Annals of Operations Research, Springer, vol. 282(1), pages 245-271, November.
    8. Ioannis Anagnostou & Drona Kandhai, 2019. "Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model," Risks, MDPI, vol. 7(2), pages 1-22, June.
    9. Wasim Ahmad & N. Bhanumurthy & Sanjay Sehgal, 2015. "Regime dependent dynamics and European stock markets: Is asset allocation really possible?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(1), pages 77-107, February.
    10. 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.
    11. Yazid M Sharaiha & Kristoffer Kittilsen Johansson, 2014. "The state-dependent time variation in the value premium," Journal of Asset Management, Palgrave Macmillan, vol. 15(2), pages 150-161, April.

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    More about this item

    Keywords

    Hidden Markov model; Markov-switching model; asset allocation; timing; volatility regimes; daily returns;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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