Evidence for Hedge Fund Predictability from a Multivariate Student-T Full-Factor GARCH Model

38 Pages Posted: 18 Jan 2010

See all articles by Ioannis D. Vrontos

Ioannis D. Vrontos

Athens University of Economics and Business

Date Written: September 2009

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-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.

Keywords: Fat tails, Hedge funds, Model uncertainty, Multivariate GARCH model, Predictability, Student-t distiribution

JEL Classification: C11, C51, G12

Suggested Citation

Vrontos, Ioannis D., Evidence for Hedge Fund Predictability from a Multivariate Student-T Full-Factor GARCH Model (September 2009). Available at SSRN: https://ssrn.com/abstract=1538294 or http://dx.doi.org/10.2139/ssrn.1538294

Ioannis D. Vrontos (Contact Author)

Athens University of Economics and Business ( email )

76 Patission Street
Athens, 104 34
Greece