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New testing approaches for mean-variance predictability

Author

Listed:
  • Gabriele Fiorentini

    (Università di Firenze, Italy; Rimini Centre for Economic Analysis)

  • Enrique Sentana

    (CEMFI, Spain)

Abstract
We propose tests for smooth but persistent serial correlation in risk premia and volatilities that exploit the non-normality of financial returns. Our parametric tests are robust to distributional misspecification, while our semiparametric tests are as powerful as if we knew the true return distribution. Local power analyses confirm their gains over existing methods, while Monte Carlo exercises assess their finite sample reliability. We apply our tests to quarterly returns on the five Fama-French factors for international stocks, whose distributions are mostly symmetric and fat-tailed. Our results highlight noticeable differences across regions and factors and confirm the fragility of Gaussian tests.

Suggested Citation

  • Gabriele Fiorentini & Enrique Sentana, 2019. "New testing approaches for mean-variance predictability," Working Paper series 19-01, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:19-01
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    References listed on IDEAS

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    1. Fiorentini, Gabriele & Sentana, Enrique, 2023. "Discrete mixtures of normals pseudo maximum likelihood estimators of structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 235(2), pages 643-665.

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    More about this item

    Keywords

    financial forecasting; moment tests; misspecification; robustness; volatility;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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