HMM in Dynamic HAC Models
SFB 649 Discussion Paper 2012-001
29 Pages Posted: 7 Jan 2017
Date Written: January 2, 2012
Understanding the dynamics of 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), where the HAC represent a wide class of models for high dimensional dependency, and HMM is a statistical technique to describe time varying dynamics. HMM applied to HAC provide flexible modeling for 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, where the model’s performance is compared with other dynamic models, and in the second application we simulate rainfall process.
Keywords: Hidden Markov model, Hierarchical Archimedean Copulae, Multivariate Distribution
JEL Classification: C13, C14, G50
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