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Fractal Market Hypothesis and Markov Regime Switching Model: A Possible Synthesis and Integration

Author

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  • Mishelle Doorasamy

    (School of Accounting, Economics and Finance, University of Kwa-Zulu Natal, Westville campus, South Africa,)

  • Prince Kwasi Sarpong

    (School of Accounting, Economics and Finance, University of Kwa-Zulu Natal, Westville campus, South Africa,)

Abstract

Peters (1994) proposed the fractal market hypothesis (FMH) as an alternative to the efficient market hypothesis, following his criticism of the EMH. In this study, we analyse whether the fractal nature of a financial market determines its riskiness and degree of persistence as measured by its Hurst exponent. To do so, we utilize the Markov Switching Model to derive a persistence index (PI) to measure the level of persistence of selected indices on Johannesburg Stock Exchange (JSE) and four other international stock markets. We conclude that markets with high Hurst exponents, show stronger persistence and less risk relative to markets with lower Hurst exponents.

Suggested Citation

  • Mishelle Doorasamy & Prince Kwasi Sarpong, 2018. "Fractal Market Hypothesis and Markov Regime Switching Model: A Possible Synthesis and Integration," International Journal of Economics and Financial Issues, Econjournals, vol. 8(1), pages 93-100.
  • Handle: RePEc:eco:journ1:2018-01-13
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    1. Onali, Enrico & Goddard, John, 2011. "Are European equity markets efficient? New evidence from fractal analysis," International Review of Financial Analysis, Elsevier, vol. 20(2), pages 59-67, April.
    2. Grech, D & Mazur, Z, 2004. "Can one make any crash prediction in finance using the local Hurst exponent idea?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 133-145.
    3. Maheu, John M & McCurdy, Thomas H, 2000. "Identifying Bull and Bear Markets in Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 100-112, January.
    4. Czarnecki, Łukasz & Grech, Dariusz & Pamuła, Grzegorz, 2008. "Comparison study of global and local approaches describing critical phenomena on the Polish stock exchange market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6801-6811.
    5. Bejaoui, Azza & Karaa, Adel, 2016. "Revisiting the bull and bear markets notions in the Tunisian stock market: New evidence from multi-state duration-dependence Markov-switching models," Economic Modelling, Elsevier, vol. 59(C), pages 529-545.
    6. 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.
    7. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
    8. Taro Ikeda, 2017. "A fractal analysis of world stock markets," Economics Bulletin, AccessEcon, vol. 37(3), pages 1514-1532.
    9. Xiaojian Yu & Zewei Chen & Weidong Xu & Junhui Fu, 2017. "Forecasting Bull and Bear Markets: Evidence from China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(8), pages 1720-1733, August.
    10. Arif Billah Dar & Niyati Bhanja & Aviral Kumar Tiwari, 2017. "Do global financial crises validate assertions of fractal market hypothesis?," International Economics and Economic Policy, Springer, vol. 14(1), pages 153-165, January.
    11. Boeing, Geoff, 2017. "Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction," SocArXiv c7p43, Center for Open Science.
    12. Grech, Dariusz & Pamuła, Grzegorz, 2008. "The local Hurst exponent of the financial time series in the vicinity of crashes on the Polish stock exchange market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4299-4308.
    13. Daye Li & Rongrong Li & Qiankun Sun, 2017. "How the heterogeneity in investment horizons affects market trends," Applied Economics, Taylor & Francis Journals, vol. 49(15), pages 1473-1482, March.
    14. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    15. Wei Chi & Robert Brooks & Emawtee Bissoondoyal-Bheenick & Xueli Tang, 2016. "Classifying Chinese bull and bear markets: indices and individual stocks," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 33(4), pages 509-531, October.
    16. Kung-Sik Chan & Bruce E. Hansen & Allan Timmermann, 2017. "Guest Editors’ Introduction: Regime Switching and Threshold Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 159-161, April.
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    2. Alexander V Laktyunkin & Alexander A Potapov, 2020. "Impact of COVID-19 on the Financial Crisis - Calculation of Fractal Parameters," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 30(5), pages 23768-23772, October.

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    More about this item

    Keywords

    Fractal Market Hypothesis; Markov Switching Model; Efficient Market Hypothesis;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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