Hidden Markov structures for dynamic copulae

Research output: Contribution to journalArticle

Published copy (DOI)



Publication details

JournalEconometric Theory
DateE-pub ahead of print - 22 Dec 2014
DatePublished (current) - Oct 2015
Issue number5
Number of pages35
Pages (from-to)981-1015
Early online date22/12/14
Original languageEnglish


Understanding the time series dynamics of a multi-dimensional dependency structure is a challenging task. Multivariate covariance driven Gaussian or mixed normal time varying models have only a limited ability to capture important features of the data such as heavy tails, asymmetry, and nonlinear dependencies. The present paper tackles this problem by proposing and analyzing a hidden Markov model (HMM) for hierarchical Archimedean copulae (HAC). The HAC constitute a wide class of models for multi-dimensional dependencies, and HMM is a statistical technique for describing regime switching dynamics. HMM applied to HAC flexibly models multivariate dimensional non-Gaussian time series.

We apply the expectation maximization (EM) algorithm for parameter estimation. 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. This example is motivated by a local adaptive analysis that yields a time varying HAC model. We compare its forecasting performance with that of other classical dynamic models. In another, second, application, we model a rainfall process. This task is of particular theoretical and practical interest because of the specific structure and required untypical treatment of precipitation data.

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations