HMM in dynamic HAC models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2012-001

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Semiparametric and Nonparametric Methods: General
• Subject: Hidden Markov model ; Hierarchical Archimedean Copulae ; multivariate distribution ; Markovscher Prozess ; Kopula (Mathematik) ; Statistische Verteilung ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Härdle, Wolfgang Karl ; Okhrin, Ostap ; Wang, Weining
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/56691
• Last update: 10.03.2025, 11:44 AM CET