Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/56691 
Year of Publication: 
2012
Series/Report no.: 
SFB 649 Discussion Paper No. 2012-001
Publisher: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
Abstract: 
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.
Subjects: 
Hidden Markov model
Hierarchical Archimedean Copulae
multivariate distribution
JEL: 
C13
C14
G50
Document Type: 
Working Paper

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