Dynamic Adaptive Mixture Models with an Application to Volatility and Risk

Abstract

In this paper we propose a new class of dynamic mixture models (DAMMs) being able to sequentially adapt the mixture components as well as the mixture composition using information coming from the data. The information driven nature of the proposed class of models allows to exactly compute the full likelihood and to avoid computer intensive simulation schemes. Specific models for financial data are developed starting from the general specification. These models nest many specifications already available in the literature. The properties of the new class of models are discussed through the paper and a large-scale application in quantitative risk management using US equity data is reported.