Cross-sectional Learning of Extremal Dependence Among Financial Assets

Advances in Neural Information Processing Systems, pages 3852-3862, 2019

11 Pages Posted: 13 Jun 2019 Last revised: 13 Jan 2020

See all articles by Xing Yan

Xing Yan

City University of Hong Kong

Qi Wu

City University of Hong Kong (CityUHK)

Wen Zhang

Independent

Date Written: May 30, 2019

Abstract

We propose a novel probabilistic model to facilitate the learning of multivariate tail dependence of multiple financial assets. Our method allows one to construct from known random vectors, e.g., standard normal, sophisticated joint heavy-tailed random vectors featuring not only distinct marginal tail heaviness, but also flexible tail dependence structure. The novelty lies in that pairwise tail dependence between any two dimensions is modeled separately from their correlation, and can vary respectively according to its own parameter rather than the correlation parameter, which is an essential advantage over many commonly used methods such as multivariate $t$ or elliptical distribution. It is also intuitive to interpret, easy to track, and simple to sample comparing to the copula approach. We show its flexible tail dependence structure through simulation. Coupled with a GARCH model to eliminate serial dependence of each individual asset return series, we use this novel method to model and forecast multivariate conditional distribution of stock returns, and obtain notable performance improvements in multi-dimensional coverage tests. Besides, our empirical finding about the asymmetry of tails of the idiosyncratic component as well as the market component is interesting and worth to be well studied in the future.

Keywords: Tail Dependence, Distinct Pairwise Tail Dependencies, Random Vector Transformation, Tail-side Sensitivity, Coverage Test

JEL Classification: G32, C53, C33

Suggested Citation

Yan, Xing and Wu, Qi and Zhang, Wen, Cross-sectional Learning of Extremal Dependence Among Financial Assets (May 30, 2019). Advances in Neural Information Processing Systems, pages 3852-3862, 2019, Available at SSRN: https://ssrn.com/abstract=3396238 or http://dx.doi.org/10.2139/ssrn.3396238

Xing Yan

City University of Hong Kong ( email )

Hong Kong

Qi Wu (Contact Author)

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Wen Zhang

Independent ( email )

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