HMM and HAC

  • Weining Wang
  • Ostap Okhrin
  • Wolfgang Karl Härdle
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 190)

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.

Keywords

Hidden Markov model hierarchical Archimedean copulae multivariate distribution 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Weining Wang
    • 1
  • Ostap Okhrin
    • 1
  • Wolfgang Karl Härdle
    • 2
  1. 1.Ladislaus von Bortkiewicz Chair of StatisticsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Center for Applied Statistics and EconomicsHumboldt-Universität zu BerlinBerlinGermany

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