Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returns
Journal of Business & Economic Statistics(2023)
摘要
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 conditionally Gaussian generative
representation of the skew-t distribution to define an augmented posterior that
can be approximated accurately. A fast stochastic gradient ascent algorithm is
used to solve the variational optimization. The new methodology is used to
estimate skew-t factor copula models 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. We show that intraday predictive densities from the skew-t copula
are more accurate than from some other copula models, while portfolio selection
strategies based on the estimated pairwise tail dependencies improve
performance relative to the benchmark index.
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