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Multiperiod portfolio allocation: A study of volatility clustering, non-normalities and predictable returns

Author

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  • Simonato, Jean-Guy
  • Denault, Michel

Abstract

This paper examines the dynamic, multiperiod portfolio choices of an investor facing predictable returns with volatility clustering and non-normalities, two pervasive stock return data characteristics. With a portfolio of one risk-free and one risky asset, we calibrate the model to the U.S. stock market and consider multiperiod choices, GARCH volatilities and Johnson-distributed non-normal errors. Quadrature techniques are used to determine the optimal allocation of the risky asset. The results show that accounting for volatility clustering strongly reduces the large hedging demands typically obtained with predictable returns. Non-normalities have modest impacts on allocations. Out-of-sample tests reveal that despite the changes in allocation observed in many cases, little statistical evidence is found that the Certainty Equivalent returns are impacted by multiperiod portfolios, nor by portfolios based on non-normal error.

Suggested Citation

  • Simonato, Jean-Guy & Denault, Michel, 2023. "Multiperiod portfolio allocation: A study of volatility clustering, non-normalities and predictable returns," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:ecofin:v:68:y:2023:i:c:s1062940823001201
    DOI: 10.1016/j.najef.2023.101997
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    Keywords

    Dynamic asset allocation; Portfolio optimization; Return predictability; Volatility clustering; GARCH volatility; Non-normal returns; Johnson distribution; Dynamic programming; Gauss–Hermite quadrature;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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