Copula hidden Markov model with unknown number of states

Yujian Liu (), Dejun Xie () and Siyi Yu ()
Additional contact information
Yujian Liu: Shanghai University of Sport
Dejun Xie: City University of Macau
Siyi Yu: Shanghai University of Sport

Computational Statistics, 2025, vol. 40, issue 5, No 11, 2583-2610

Abstract: Abstract In this study, we investigate the construction of hidden Markov models (HMMs) with copulas serving as the emission distributions. Additionally, we relax the traditional assumption that the number of hidden states must be predetermined before model fitting. Instead, in our approach, the number of states is estimated simultaneously with other model parameters of the copula-HMM when datasets are applied. This is achieved by incorporating the hierarchical Dirichlet process as a prior during the Bayesian inference procedure. We provide a comprehensive algorithm for this methodology, including a detailed implementable example using the t-copula, which is a novel contribution not previously available in the copula literature. The proposed estimator was validated through simulation studies, which demonstrated its superiority over traditional BIC-based approaches for model selection in HMMs. Furthermore, we applied this methodology to real data to examine the dependence structure among stock markets.

Keywords: Non-parametric Bayesian inference; Hidden Markov model; Copula functions; Multivariate analysis (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-024-01571-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:40:y:2025:i:5:d:10.1007_s00180-024-01571-5

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-024-01571-5

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().