Mixture Normal Conditional Correlation Models

Maria Putintseva
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Maria Putintseva: University of Zurich, Ecole Polytechnique Fédérale de Lausanne, and Swiss Finance Institute

No 12-41, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 (search for similar items in EconPapers)
JEL-codes: C51 C53 G17 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2012-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1241

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