Vector Copula-Based Markov Models

@misc{sun2026vector,
  title = {{Vector Copula-Based Markov Models}},
  author = {Li-Hsien Sun and Po-Chun Huang and Po-Cheng Chen and Chi-Yang Chiu},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.25129},
  eprint = {2610.25129},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.25129},
  note = {CC BY 4.0},
}

TY  - UNPB
TI  - Vector Copula-Based Markov Models
AU  - Li-Hsien Sun
AU  - Po-Chun Huang
AU  - Po-Cheng Chen
AU  - Chi-Yang Chiu
PY  - 2026
DA  - 2026/10/26
PB  - arXiv
DO  - 10.48550/arxiv.2610.25129
UR  - https://arxiv.org/abs/2610.25129
AB  - We propose the vector copula-based Markov model (VCBMM) for describing multivariate time series with nonlinear serial dependence. To extend univariate copula-based Markov models to multivariate time series, we develop a hierarchical construction based on scalar aggregate variables and consider the vector Sklar theorem to obtain the joint density of the VCBMM. We further assume the Clayton copula for serial dependence and the multivariate Gaussian distribution for the marginals. The corresponding likelihood function and maximum likelihood estimation (MLE) with the inference functions for margins (IFM) method are provided. The performance of the proposed model is illustrated through simulation studies. In real data analysis, we apply the model to bivariate daily log returns of stocks from the 2008 global financial crisis (2008 GFC) and the 2020 COVID-19 pandemic (2020 COVID-19).
ER  -