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Modeling local trends with regime shifting models with time-varying probabilities

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  • Focardi, Sergio M.
  • Fabozzi, Frank J.
  • Mazza, Davide

Abstract

In this paper we show that persistence and switching of trends are phenomena that appear in most long-lived stock return series. We model stock returns using a family of models based on hidden Markov models with duration-dependent transition probabilities. Trends are correlated so that aggregates such as indexes exhibit the same persistence and switching behavior as single stocks themselves. Hidden Markov models can thus explain medium-term momentum.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finana:v:66:y:2019:i:c:s105752191830752x
    DOI: 10.1016/j.irfa.2019.06.007
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    Cited by:

    1. Haase, Felix & Neuenkirch, Matthias, 2023. "Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US," International Journal of Forecasting, Elsevier, vol. 39(2), pages 587-605.
    2. Figà-Talamanca, Gianna & Focardi, Sergio & Patacca, Marco, 2021. "Regime switches and commonalities of the cryptocurrencies asset class," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).

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    More about this item

    Keywords

    Regime shifting; Hidden Markov models; Duration-dependent Markov switching models; Return models; Momentum; Reversals;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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