Mixture Normal Conditional Correlation Models

38 Pages Posted: 27 Jun 2010 Last revised: 20 Dec 2012

See all articles by Maria Putintseva

Maria Putintseva

University of Zurich - Swiss Banking Institute (ISB); Ecole Polytechnique Fédérale de Lausanne

Date Written: October 19, 2012

Abstract

I propose a class of hybrid models to describe and predict the dynamics of a multivariate stationary random vector, e.g. a vector of stock returns. These models combine essential features of the multivariate mixture normal distribution and the conditional correlation models. I describe in detail the expectation-maximization algorithm, which makes the parameter estimation feasible and fast virtually for any random vector length. I fit the suggested models to five data sets, consisting of vectors of stock returns, with the maximal vector length of fifteen stocks. The predictive ability of this model class is compared to other widely used multivariate models, and it turns out that my models provide the best forecasts, both on average and for extreme negative returns. All necessary formulas to apply these models for important financial objectives are also provided.

Keywords: Finite Mixtures, Dynamic Conditional Correlation, Forecasting, Multivariate Modelling, Predictive Ability

JEL Classification: C51, C53, G17

Suggested Citation

Putintseva, Maria, Mixture Normal Conditional Correlation Models (October 19, 2012). Swiss Finance Institute Research Paper No. 12-41, Available at SSRN: https://ssrn.com/abstract=1630827 or http://dx.doi.org/10.2139/ssrn.1630827

Maria Putintseva (Contact Author)

University of Zurich - Swiss Banking Institute (ISB) ( email )

Plattenstrasse 14
CH-8032 Zurich, Zurich 8032
Switzerland

Ecole Polytechnique Fédérale de Lausanne ( email )

c/o University of Geneve
40, Bd du Pont-d'Arve
1211 Geneva, CH-6900
Switzerland

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
224
Abstract Views
1,564
rank
159,726
PlumX Metrics