Mixture Normal Conditional Correlation Models
38 Pages Posted: 27 Jun 2010 Last revised: 20 Dec 2012
Date Written: October 19, 2012
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: Suggested Citation