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Cross-sectional Learning of Extremal Dependence among Financial Assets


  • Xing Yan
  • Qi Wu
  • Wen Zhang


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.

Suggested Citation

  • Xing Yan & Qi Wu & Wen Zhang, 2019. "Cross-sectional Learning of Extremal Dependence among Financial Assets," Papers 1905.13425,, revised Oct 2019.
  • Handle: RePEc:arx:papers:1905.13425

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    Cited by:

    1. Zhonghao Xian & Xing Yan & Cheuk Hang Leung & Qi Wu, 2024. "Risk-Neutral Generative Networks," Papers 2405.17770,
    2. Chuting Sun & Qi Wu & Xing Yan, 2023. "Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning," Papers 2301.07318,, revised Jan 2024.
    3. Yaquan Zhang & Qi Wu & Nanbo Peng & Min Dai & Jing Zhang & Hu Wang, 2020. "Memory-Gated Recurrent Networks," Papers 2012.13121,, revised Dec 2020.
    4. Xiangqian Sun & Xing Yan & Qi Wu, 2020. "Generative Learning of Heterogeneous Tail Dependence," Papers 2011.13132,, revised Nov 2023.
    5. Sun, Chuting & Wu, Qi & Yan, Xing, 2024. "Dynamic CVaR portfolio construction with attention-powered generative factor learning," Journal of Economic Dynamics and Control, Elsevier, vol. 160(C).
    6. Siyi Wang & Xing Yan & Bangqi Zheng & Hu Wang & Wangli Xu & Nanbo Peng & Qi Wu, 2021. "Risk and return prediction for pricing portfolios of non-performing consumer credit," Papers 2110.15102,

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