Lin Deng, Michael Stanley Smith, Worapree Maneesoonthorn
arXiv 10 Aug 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 6 citations (OpenAlex)
arXiv:2308.05564 · PDF · DOI · OpenAlex · Extracted main text
Skew-t copula models are attractive for the modeling of financial data because they allow for asymmetric and extreme tail dependence. We show that the copula implicit in the skew-t distribution of Azzalini and Capitanio (2003) allows for a higher level of pairwise asymmetric dependence than two popular alternative skew-t copulas. Estimation of this copula in high dimensions is challenging, and we propose a fast and accurate Bayesian variational inference (VI) approach to do so. The method uses a generative representation of the skew-t distribution to define an augmented posterior that can be approximated accurately. A stochastic gradient ascent algorithm is used to solve the variational optimization. The methodology is used to estimate skew-t factor copula models with up to 15 factors for intraday returns from 2017 to 2021 on 93 U.S. equities. The copula captures substantial heterogeneity in asymmetric dependence over equity pairs, in addition to the variability in pairwise correlations. In a moving window study we show that the asymmetric dependencies also vary over time, and that intraday predictive densities from the skew-t copula are more accurate than those from benchmark copula models. Portfolio selection strategies based on the estimated pairwise asymmetric dependencies improve performance relative to the index.
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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 | Oh, D. H. and Patton, A. J (2023) Dynamic factor copula models with estimated cluster assignments | 1.000 | 10 | 6 | 100% |
| 2 | Loaiza-Maya, R., Smith, M. S., Nott, D. J., and Danaher, P. J (2022) Fast and accurate variational inference for models with many latent variables self | 1.000 | 6 | 4 | 100% |
| 3 | Smith, M. S., Gan, Q., and Kohn, R. J (2010) Modelling dependence using skew t copulas: Bayesian inference and applications self | 1.000 | 6 | 4 | 100% |
| 4 | Azzalini, A. and Capitanio, A (2003) Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t -distribution | 0.928 | 4 | 3 | 100% |
| 5 | Yoshiba, T (2018) Maximum likelihood estimation of skew-t copulas with its applications to stock returns | 0.928 | 4 | 3 | 100% |
| 6 | Ranganath, R., Gerrish, S., and Blei, D (2014) Black box variational inference | 0.843 | 3 | 3 | 100% |
| 7 | Sahu, S. K., Dey, D. K., and Branco, M. D (2003) A new class of multivariate skew distributions with applications to bayesian regression models | 0.811 | 4 | 2 | 100% |
| 8 | Ong, V. M.-H., Nott, D. J., and Smith, M. S (2018) Gaussian variational approximation with a factor covariance structure self | 0.737 | 3 | 2 | 100% |
| 9 | Christoffersen, P., Errunza, V., Jacobs, K., and Langlois, H (2012) Is the potential for international diversification disappearing? a dynamic copula approach | 0.644 | 2 | 2 | 100% |
| 10 | Kingma, D. P. and Welling, M (2014) Auto-encoding variational Bayes | 0.644 | 2 | 2 | 100% |
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