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Evidence for hedge fund predictability from a multivariate Student's t full-factor GARCH model

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  • Ioannis Vrontos

Abstract

Extending previous work on hedge fund return predictability, this paper introduces the idea of modelling the conditional distribution of hedge fund returns using Student's t full-factor multivariate GARCH models. This class of models takes into account the stylized facts of hedge fund return series, that is, heteroskedasticity, fat tails and deviations from normality. For the proposed class of multivariate predictive regression models, we derive analytic expressions for the score and the Hessian matrix, which can be used within classical and Bayesian inferential procedures to estimate the model parameters, as well as to compare different predictive regression models. We propose a Bayesian approach to model comparison which provides posterior probabilities for various predictive models that can be used for model averaging. Our empirical application indicates that accounting for fat tails and time-varying covariances/correlations provides a more appropriate modelling approach of the underlying dynamics of financial series and improves our ability to predict hedge fund returns.

Suggested Citation

  • Ioannis Vrontos, 2012. "Evidence for hedge fund predictability from a multivariate Student's t full-factor GARCH model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1295-1321, November.
  • Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1295-1321
    DOI: 10.1080/02664763.2011.644771
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    Cited by:

    1. Jørgen Vitting Andersen & Ioannis Vrontos & Petros Dellaportas & Serge Galam, 2014. "A Socio-Finance Model: Inference and empirical application," Working Papers hal-01215605, HAL.
    2. Olmo, José & Sanso-Navarro, Marcos, 2012. "Forecasting the performance of hedge fund styles," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2351-2365.
    3. Jørgen Vitting Andersen & Ioannis D. Vrontos & Petros Dellaportas & Serge Galam, 2015. "A Socio-Finance Model: Inference and empirical application," SciencePo Working papers Main halshs-01242248, HAL.
    4. Jørgen Vitting Andersen & Ioannis Vrontos & Petros Dellaportas & Serge Galam, 2014. "A Socio-Finance Model: Inference and empirical application," SciencePo Working papers Main hal-01215605, HAL.
    5. Kao, Wei-Shun & Lin, Chu-Hsiung & Changchien, Chang-Cheng & Wu, Chien-Hui, 2017. "Return distribution, leverage effect and spot-futures spread on the hedging effectiveness," Finance Research Letters, Elsevier, vol. 22(C), pages 158-162.
    6. Ioannis D. Vrontos & John Galakis & Ekaterini Panopoulou & Spyridon D. Vrontos, 2024. "Forecasting GDP growth: The economic impact of COVID‐19 pandemic," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1042-1086, July.

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