IDEAS home Printed from https://ideas.repec.org/a/bas/econst/y2023i1p18-35.html
   My bibliography  Save this article

Stock Returns Under Different Market Regimes: An Application of Markov Switching Models to 24 European Indices

Author

Listed:
  • Anton Gerunov

Abstract

This paper studies the different modes of operation of European stock markets. Using data on 24 European indices over a period of 15 years, we show that these can be well represented by a Hidden Markov Model with two regimes that roughly correspond to bull and bear markets. We further estimate regime parameters and show that the alternate regimes have very different risk-return tradeoffs with clear implications for portfolio management. Corresponding transition probability matrices show the remarkable persistence of states and give a possible quantitative estimate of the degree of inertia in financial markets. Regime-switching coordination across markets is further examined, showing that moments of correlations are followed by idiosyncratic episodes and thus, risk diversification through regime arbitrage is possible.

Suggested Citation

  • Anton Gerunov, 2023. "Stock Returns Under Different Market Regimes: An Application of Markov Switching Models to 24 European Indices," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 18-35.
    • Handle: RePEc:bas:econst:y:2023:i:1:p:18-35
    as

    Download full text from publisher

    File URL: https://www.iki.bas.bg/Journals/EconomicStudies/2023/2023-1/02_Anton-Gerunov.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nguyet Nguyen, 2018. "Hidden Markov Model for Stock Trading," IJFS, MDPI, vol. 6(2), pages 1-17, March.
    2. Shu-Ping Shi & Yong Song, 2012. "Identifying Speculative Bubbles with an Infinite Hidden Markov Model," Working Paper series 26_12, Rimini Centre for Economic Analysis.
    3. Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
    4. Jennifer Pohle & Roland Langrock & Floris M. Beest & Niels Martin Schmidt, 2017. "Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 270-293, September.
    5. 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.
    6. Nguyet Nguyen & Dung Nguyen, 2015. "Hidden Markov Model for Stock Selection," Risks, MDPI, vol. 3(4), pages 1-19, October.
    7. Nguyet Nguyen, 2017. "An Analysis and Implementation of the Hidden Markov Model to Technology Stock Prediction," Risks, MDPI, vol. 5(4), pages 1-16, November.
    8. 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.
    9. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    10. Eun-chong Kim & Han-wook Jeong & Nak-young Lee, 2019. "Global Asset Allocation Strategy Using a Hidden Markov Model," JRFM, MDPI, vol. 12(4), pages 1-15, November.
    11. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    12. Baltas, Nick & Karyampas, Dimitrios, 2018. "Forecasting the equity risk premium: The importance of regime-dependent evaluation," Journal of Financial Markets, Elsevier, vol. 38(C), pages 83-102.
    13. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
    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. Mariana Petrova & Teodor Todorov, 2023. "Empirical Testing of Models of Autoregressive Conditional Heteroscedasticity Used for Prediction of the Volatility of Bulgarian Investment Funds," Risks, MDPI, vol. 11(11), pages 1-30, November.

    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. Fulvia Pennoni & Francesco Bartolucci & Gianfranco Forte & Ferdinando Ametrano, 2022. "Exploring the dependencies among main cryptocurrency log‐returns: A hidden Markov model," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(1), February.
    2. Focardi, Sergio M. & Fabozzi, Frank J. & Mazza, Davide, 2019. "Modeling local trends with regime shifting models with time-varying probabilities," International Review of Financial Analysis, Elsevier, vol. 66(C).
    3. De Gooijer, Jan G. & Henter, Gustav Eje & Yuan, Ao, 2022. "Kernel-based hidden Markov conditional densities," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    4. Liu, Wen-Hsien & Chyi, Yih-Luan, 2006. "A Markov regime-switching model for the semiconductor industry cycles," Economic Modelling, Elsevier, vol. 23(4), pages 569-578, July.
    5. Gaia Garino & Lucio Sarno, 2004. "Speculative Bubbles in U.K. House Prices: Some New Evidence," Southern Economic Journal, John Wiley & Sons, vol. 70(4), pages 777-795, April.
    6. Dmitry Kulikov, 2012. "Testing for Rational Speculative Bubbles on the Estonian Stock Market," Research in Economics and Business: Central and Eastern Europe, Tallinn School of Economics and Business Administration, Tallinn University of Technology, vol. 4(1).
    7. Masaru Chiba, 2023. "Robust and efficient specification tests in Markov-switching autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 99-137, April.
    8. 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.
    9. Tsung Wu Ho, 2000. "Regime-switching investment-saving correlation and international capital mobility," Applied Economics Letters, Taylor & Francis Journals, vol. 7(9), pages 619-622.
    10. Battulga Gankhuu, 2024. "Bayesian Markov-Switching Vector Autoregressive Process," Papers 2404.11235, arXiv.org, revised May 2024.
    11. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, vol. 70(1), pages 127-157, January.
    12. Pami Dua & Divya Tuteja, 2015. "Global Recession and Eurozone Debt Crisis: Impact on Exports of China and India," Working Papers id:7386, eSocialSciences.
    13. 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.
    14. Arash Refah-Kahriz & Hassan Heidari & Mahdiyeh Rahimdel, 2023. "Is there a similar Granger causality among CO2 emissions, energy consumption and economic growth in different regimes in Iran?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(4), pages 3801-3822, April.
    15. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "A multinomial and rank-ordered logit model with inter- and intra-individual heteroscedasticity," Tinbergen Institute Discussion Papers 20-069/III, Tinbergen Institute.
    16. Bildirici, Melike E. & Gökmenoğlu, Seyit M., 2017. "Environmental pollution, hydropower energy consumption and economic growth: Evidence from G7 countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 68-85.
    17. Boucher, C. & Jasinski, A. & Tokpavi, S., 2023. "Conditional mean reversion of financial ratios and the predictability of returns," Journal of International Money and Finance, Elsevier, vol. 137(C).
    18. Nan Li & Simon S. Kwok, 2021. "Jointly determining the state dimension and lag order for Markov‐switching vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 471-491, July.
    19. 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.
    20. Psaradakis, Zacharias & Sola, Martin, 1998. "Finite-sample properties of the maximum likelihood estimator in autoregressive models with Markov switching," Journal of Econometrics, Elsevier, vol. 86(2), pages 369-386, June.

    More about this item

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    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:bas:econst:y:2023:i:1:p:18-35. 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: Diana Dimitrova (email available below). General contact details of provider: https://edirc.repec.org/data/ikbasbg.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.