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A Flexible Regime Switching Model for Asset Returns


  • Marc S. Paolella

    (University of Zurich - Department of Banking and Finance; Swiss Finance Institute)

  • Pawel Polak

    (University of Zurich; Ecole Polytechnique Fédérale de Lausanne - Ecole Polytechnique Fédérale de Lausanne)

  • Patrick S. Walker

    (University of Zurich, Department of Banking and Finance)


A non-Gaussian multivariate regime switching dynamic correlation model for fi nancial asset returns is proposed. It incorporates the multivariate generalized hyperbolic law for the conditional distribution of returns. All model parameters are estimated consistently using a new two-stage expectation-maximization algorithm that also allows for incorporation of shrinkage estimation via quasi-Bayesian priors. It is shown that use of Markov switching correlation dynamics not only leads to highly accurate risk forecasts, but also potentially reduces the regulatory capital requirements during periods of distress. In terms of portfolio performance, the new regime switching model delivers consistently higher Sharpe ratios and smaller losses than the equally weighted portfolio and all competing models. Finally, the regime forecasts are employed in a dynamic risk control strategy that avoids most losses during the fi nancial crisis and vastly improves risk-adjusted returns.

Suggested Citation

  • Marc S. Paolella & Pawel Polak & Patrick S. Walker, 2019. "A Flexible Regime Switching Model for Asset Returns," Swiss Finance Institute Research Paper Series 19-27, Swiss Finance Institute, revised May 2019.
  • Handle: RePEc:chf:rpseri:rp1927

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    Cited by:

    1. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.

    More about this item


    sGARCH; Markov Switching; Multivariate Generalized Hyperbolic Distribution; Portfolio Optimization; Value-at-Risk;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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