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Type Journal Article
Scope Discipline-based scholarship
Title Multivariate asset return prediction with mixture models
Organization Unit
Authors
  • Marc Paolella
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title The European journal of finance
Publisher Taylor & Francis
Geographical Reach international
ISSN 1351-847X
Volume 21
Number 13-14
Page Range 1214 - 1252
Date 2015
Abstract Text The use of mixture distributions for modeling asset returns has a long history in finance. New methods of demonstrating support for the presence of mixtures in the multivariate case are provided. The use of a two-component multivariate normal mixture distribution, coupled with shrinkage via a quasi-Bayesian prior, is motivated, and shown to be numerically simple and reliable to estimate, unlike the majority of multivariate GARCH models in existence. Equally important, it provides a clear improvement over use of GARCH models feasible for use with a large number of assets, such as constant conditional correlation, dynamic conditional correlation, and their extensions, with respect to out-of-sample density forecasting. A generalization to a mixture of multivariate Laplace distributions is motivated via univariate and multivariate analysis of the data, and an expectation–maximization algorithm is developed for its estimation in conjunction with a quasi-Bayesian prior. It is shown to deliver significantly better forecasts than the mixed normal, with fast and numerically reliable estimation. Crucially, the distribution theory required for portfolio theory and risk assessment is developed.
Digital Object Identifier 10.1080/1351847X.2012.760167
Other Identification Number merlin-id:9252
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Additional Information Special Issue: Skew-elliptical distributions in finance and skewness in asset returns