Predicting the Distribution of Stock Returns: Model Formulation, Statistical Evaluation, VaR Analysis and Economic Significance

32 Pages Posted: 8 May 2013 Last revised: 19 Apr 2017

Date Written: January 13, 2015

Abstract

A large literature has investigated predictability of the conditional mean of low frequency stock returns by macroeconomic and financial variables; however, little is known about predictability of the conditional distribution. We look at one-step-ahead out-of-sample predictability of the conditional distribution of monthly U.S. stock returns in relation to the macroeconomic and financial environment. Our methodological approach is innovative: we consider several specifications for the conditional density and combinations schemes. Our results are as follows: the entire density is predicted under combination schemes as applied to univariate GARCH models with Gaussian innovations; the Bayesian winner in relation to GARCH - skewed - t models is informative about the 5% VaR; the average realised utility of a mean-variance investor is maximised under the Bayesian winner as applied to GARCH models with symmetric student-t innovations. Our results have two implications: the best prediction model depends on the evaluation criterion; and combination schemes outperform individual models.

Keywords: Stock Returns, Density Forecast, Evaluation Criterion, Forecast Combination

JEL Classification: C22, C53, G11, G12

Suggested Citation

Massacci, Daniele, Predicting the Distribution of Stock Returns: Model Formulation, Statistical Evaluation, VaR Analysis and Economic Significance (January 13, 2015). Available at SSRN: https://ssrn.com/abstract=2262339 or http://dx.doi.org/10.2139/ssrn.2262339

Daniele Massacci (Contact Author)

King's College London ( email )

United Kingdom

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