Cluster GARCH
Bibliographic Data
| ID | 19419292 |
|---|---|
| Authors | Chen Tong (0000-0002-3065-5978, Xiamen University), Peter Reinhard Hansen (0000-0001-9728-4860, Department of Economics, University of North Carolina, corresponding author), Ilya Archakov (0009-0001-0871-2622, York University) |
| Year | 2026 |
| Volume | 44 |
| Issue | 1 |
| Pages | 148-161 |
| Publication date | 2026-01-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2025.2510325 |
| OpenAlex | W4410779679 |
| Language | EN |
| Citations received | 1 |
| References cited | 32 |
We introduce a novel multivariate GARCH model with flexible convolution-t distributions that is applicable in high-dimensional systems. The model is called Cluster GARCH because it can accommodate cluster structures in the conditional correlation matrix and in tail dependencies. The expressions for the log-likelihood function and its derivatives are tractable, and the latter facilitate a score-driven model for the dynamic correlation structure. We apply the Cluster GARCH model to daily returns for 100 assets and find that it outperforms existing models, both in-sample and out-of-sample. Moreover, the convolution-t distribution provides a better empirical performance than the conventional multivariate t-distribution
Autoregressive conditional heteroskedasticity · Computer network · Econometrics · Computer Science · Mathematics · Metal-Organic Frameworks: Synthesis and Applications
Multivariate GARCH models
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Dynamic Conditional Correlation
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A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
Dynamic Conditional Correlation
Modelling the Coherence in Short-Run Nominal Exchange Rates
A Canonical Representation of Block Matrices with Applications to Covariance and Correlation Matrices
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |