arXiv 19 Jan 2026 · Econometrics
arXiv:2601.13281 · PDF · DOI · OpenAlex · Extracted main text
We introduce a novel model for time-varying, asymmetric, tail-dependent copulas in high dimensions that incorporates both spectral dynamics and regularization. The dynamics of the dependence matrix' eigenvalues are modeled in a score-driven way, while biases in the unconditional eigenvalue spectrum are resolved by non-linear shrinkage. The dynamic parameterization of the copula dependence matrix ensures that it satisfies the appropriate restrictions at all times and for any dimension. The model is parsimonious, computationally efficient, easily scalable to high dimensions, and performs well for both simulated and empirical data. In an empirical application to financial market dynamics using 100 stocks from 10 different countries and 10 different industry sectors, we find that our copula model captures both geographic and industry related co-movements and outperforms recent computationally more intensive clustering-based factor copula alternatives. Both the spectral dynamics and the regularization contribute to the new model's performance. During periods of market stress, we find that the spectral dynamics reveal strong increases in international stock market dependence, which causes reductions in diversification potential and increases in systemic risk.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ledoit, O. and Wolf, M (2022) Quadratic shrinkage for large covariance matrices | 1.000 | 8 | 3 | 100% |
| 2 | Ledoit, O. and Wolf, M (2022) The power of (non-) linear shrinking: A review and guide to covariance matrix estimation | 0.928 | 4 | 3 | 100% |
| 3 | Hetland, Simon and Pedersen, Rasmus Søndergaard and Rahbek, Anders (2023) Dynamic conditional eigenvalue GARCH | 0.928 | 4 | 3 | 100% |
| 4 | Opschoor, A. and Lucas, A. and Barra, I. and Van Dijk, D (2021) Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings self | 0.920 | 9 | 4 | 78% |
| 5 | Oh, D. H. and Patton, A.J (2023) Dynamic factor copula models with estimated cluster assignments | 0.901 | 26 | 6 | 73% |
| 6 | Creal, D. D. and Koopman, S. and Lucas, A (2013) Generalized Autoregressive Score Models With Applications self | 0.811 | 4 | 2 | 100% |
| 7 | Oh, D. H. and Patton, A. J (2018) Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads | 0.811 | 4 | 2 | 100% |
| 8 | Harvey, A (2013) Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series | 0.737 | 3 | 2 | 100% |
| 9 | Oh, D. H. and Patton, A. J (2017) Modeling Dependence in High Dimensions With Factor Copulas | 0.737 | 3 | 2 | 100% |
| 10 | Engle, R.F. and Ledoit, O. and Wolf, M (2019) Large dynamic covariance matrices | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 35 scored citations.