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Do financial variables help predict the conditional distribution of the market portfolio?

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  • Shamsi Zamenjani, Azam

Abstract

This paper investigates predictability of the full density of market returns. The proposed model is a Bayesian nonparametric mixture model where the mixture weights are functions of predictors, allowing us to study predictability of the unknown and time-varying density of market returns, in contrast to the extant literature which essentially focuses on point forecasts of the predictive mean which contains no description of the associated uncertainty. We compare statistical and economic performance of the proposed model with a set of competing models. Despite little or no improvement in point forecasts, certain variables display significant out-of-sample predictive ability for the stock return density and increase economic value for investors when employed in portfolio decisions.

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  • Shamsi Zamenjani, Azam, 2021. "Do financial variables help predict the conditional distribution of the market portfolio?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 327-345.
    • Handle: RePEc:eee:empfin:v:62:y:2021:i:c:p:327-345
    DOI: 10.1016/j.jempfin.2021.05.001
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    2. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.

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