IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/21154.html
   My bibliography  Save this paper

Markov-switching Asset Allocation: Do Profitable Strategies Exist?

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/21154/1/MPRA_paper_21154.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Turner, Christopher M. & Startz, Richard & Nelson, Charles R., 1989. "A Markov model of heteroskedasticity, risk, and learning in the stock market," Journal of Financial Economics, Elsevier, vol. 25(1), pages 3-22, November.
    2. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    3. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-1153, December.
    4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    5. Massimo Guidolin & Allan Timmermann, 2005. "Economic Implications of Bull and Bear Regimes in UK Stock and Bond Returns," Economic Journal, Royal Economic Society, vol. 115(500), pages 111-143, January.
    6. Andreas Graflund & Birger Nilsson, 2003. "Dynamic Portfolio Selection: the Relevance of Switching Regimes and Investment Horizon," European Financial Management, European Financial Management Association, vol. 9(2), pages 179-200, June.
    7. Tobias Rydén & Timo Teräsvirta & Stefan Åsbrink, 1998. "Stylized facts of daily return series and the hidden Markov model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(3), pages 217-244.
    8. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    9. Martin Hess, 2006. "Timing and diversification: A state-dependent asset allocation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 12(3), pages 189-204.
    10. Jan Bulla & Andreas Berzel, 2008. "Computational issues in parameter estimation for stationary hidden Markov models," Computational Statistics, Springer, vol. 23(1), pages 1-18, January.
    11. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    12. Jedrzej Bialkowski, 2004. "Modelling Returns on Stock Indices for Western and Central European Stock Exchanges - a Markov Switching Approach," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 2(2), pages 81-100.
    13. Manuel Ammann & Michael Verhofen, 2006. "The Effect of Market Regimes on Style Allocation," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(3), pages 309-337, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Jan Bulla, 2010. "Hidden Markov models with t components. Increased persistence and other aspects," Quantitative Finance, Taylor & Francis Journals, vol. 11(3), pages 459-475.
    3. Chang, Kuang-Liang, 2009. "Do macroeconomic variables have regime-dependent effects on stock return dynamics? Evidence from the Markov regime switching model," Economic Modelling, Elsevier, vol. 26(6), pages 1283-1299, November.
    4. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).
    5. Emrah İ. Çevik & Turhan Korkmaz & Erdal Atukeren, 2012. "Business confidence and stock returns in the USA: a time-varying Markov regime-switching model," Applied Financial Economics, Taylor & Francis Journals, vol. 22(4), pages 299-312, February.
    6. Manuel Ammann & Michael Verhofen, 2006. "The Effect of Market Regimes on Style Allocation," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(3), pages 309-337, September.
    7. Chatziantoniou, Ioannis & Filis, George & Floros, Christos, 2017. "Asset prices regime-switching and the role of inflation targeting monetary policy," Global Finance Journal, Elsevier, vol. 32(C), pages 97-112.
    8. Branger, Nicole & Kraft, Holger & Meinerding, Christoph, 2014. "Partial information about contagion risk, self-exciting processes and portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 18-36.
    9. Meenagh, David & Minford, Patrick & Peel, David, 2007. "Simulating stock returns under switching regimes - A new test of market efficiency," Economics Letters, Elsevier, vol. 94(2), pages 235-239, February.
    10. John M. Maheu & Thomas H. McCurdy & Yong Song, 2012. "Components of Bull and Bear Markets: Bull Corrections and Bear Rallies," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 391-403, February.
    11. Jun Tu, 2010. "Is Regime Switching in Stock Returns Important in Portfolio Decisions?," Management Science, INFORMS, vol. 56(7), pages 1198-1215, July.
    12. Guidolin, Massimo & Ono, Sadayuki, 2006. "Are the dynamic linkages between the macroeconomy and asset prices time-varying?," Journal of Economics and Business, Elsevier, vol. 58(5-6), pages 480-518.
    13. Sei‐Wan Kim & Bong‐Soo Lee, 2008. "Stock Returns, Asymmetric Volatility, Risk Aversion, And Business Cycle: Some New Evidence," Economic Inquiry, Western Economic Association International, vol. 46(2), pages 131-148, April.
    14. 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.
    15. Alexander, Carol & Kaeck, Andreas, 2008. "Regime dependent determinants of credit default swap spreads," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1008-1021, June.
    16. Massimo Guidolin & Allan Timmermann, 2006. "An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 1-22, January.
    17. 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).
    18. Massimo Guidolin & Giovanna Nicodano, 2007. "Managing international portfolios with small capitalization stocks," Working Papers 2007-030, Federal Reserve Bank of St. Louis.
    19. Zeng, Songlin & Bec, Frédérique, 2015. "Do stock returns rebound after bear markets? An empirical analysis from five OECD countries," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 50-61.
    20. Kim, Woo Chang & Kim, Jang Ho & Mulvey, John M. & Fabozzi, Frank J., 2015. "Focusing on the worst state for robust investing," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 19-31.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:21154. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.