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Markov-switching asset allocation: Do profitable strategies exist?

Author

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  • Jan Bulla

    (Université de Caen, LMNO, Université de Caen)

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

Abstract

This article 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 United States, Japan and Germany over the past 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 per cent. In addition, annualized excess returns attain 18.5 to 201.6 basis points.

Suggested Citation

  • Jan Bulla & Sascha Mergner & Ingo Bulla & André Sesboüé & Christophe Chesneau, 2011. "Markov-switching asset allocation: Do profitable strategies exist?," Journal of Asset Management, Palgrave Macmillan, vol. 12(5), pages 310-321, November.
    • Handle: RePEc:pal:assmgt:v:12:y:2011:i:5:d:10.1057_jam.2010.27
    DOI: 10.1057/jam.2010.27
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Hematizadeh, Roksana & Tajaddini, Reza & Hallahan, Terrence, 2022. "Dynamic asset allocation strategy using a state-dependent Markov model: Applications to international equity markets," Journal of International Money and Finance, Elsevier, vol. 128(C).
    3. Min Jeong Kim & Dohyoung Kwon, 2023. "Dynamic asset allocation strategy: an economic regime approach," Journal of Asset Management, Palgrave Macmillan, vol. 24(2), pages 136-147, March.
    4. Hwu Shih-Tang & Kim Chang-Jin, 2024. "Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 177-199, April.
    5. Goodarzi, Milad & Meinerding, Christoph, 2023. "Asset allocation with recursive parameter updating and macroeconomic regime identifiers," Discussion Papers 06/2023, Deutsche Bundesbank.
    6. 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.
    7. Bernardi, Mauro & Maruotti, Antonello & Petrella, Lea, 2017. "Multiple risk measures for multivariate dynamic heavy–tailed models," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 1-32.
    8. Kai Zheng & Weidong Xu & Xili Zhang, 2023. "Multivariate Regime Switching Model Estimation and Asset Allocation," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 165-196, January.
    9. 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.
    10. 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.
    11. Peter Nystrup & Bo William Hansen & Henrik Madsen & Erik Lindström, 2016. "Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation," Journal of Asset Management, Palgrave Macmillan, vol. 17(5), pages 361-374, September.
    12. 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.
    13. 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.
    14. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2024. "Regime-Aware Asset Allocation: a Statistical Jump Model Approach," Papers 2402.05272, arXiv.org.
    15. Marcelo Lewin & Carlos Heitor Campani, 2023. "Constrained portfolio strategies in a regime-switching economy," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(1), pages 27-59, March.
    16. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    17. 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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