A0348
Title: Large skew-t copula models and asymmetric dependence in intraday equity returns
Authors: Lin Deng - University of Melbourne (Australia) [presenting]
Michael Stanley Smith - Melbourne Business School (Australia)
Worapree Ole Maneesoonthorn - Monash University (Australia)
Abstract: Skew-t copula models are attractive for the modeling of financial data because they allow for asymmetric and extreme tail dependence. The copula implicit is shown in the skew-t distribution of Azzalini and Capitanio, allowing 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 a fast and accurate Bayesian variational inference (VI) approach is proposed. 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. Intraday predictive densities are shown from the skew-t copula and 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.
