HMM and HAC

Abstract

Understanding the dynamics of a high dimensional non-normal dependency structure is a challenging task. This research aims at attacking this problem by building up a hidden Markov model (HMM) for Hierarchical Archimedean Copulae (HAC). The HAC constitute a wide class of models for high dimensional dependencies, and HMM is a statistical technique for describing time varying dynamics. HMM applied to HAC flexibly models high dimensional non-Gaussian time series. Consistency results for both parameters and HAC structures are established in an HMM framework. The model is calibrated to exchange rate data with a VaR application, and the model's performance is compared to other dynamic models. © 2013 Springer-Verlag.