英文标题：
《Cross-sectional Learning of Extremal Dependence among Financial Assets》
---
作者：
Xing Yan, Qi Wu, Wen Zhang
---
最新提交年份：
2019
---
英文摘要：
  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.
---
中文摘要：
我们提出了一种新的概率模型，以便于学习多个金融资产的多变量尾部相关性。我们的方法允许从已知的随机向量（例如，标准正态、复杂的联合重尾随机向量）构造，这些随机向量不仅具有明显的边缘尾重，而且具有灵活的尾相关结构。新颖之处在于，任何两个维度之间的成对尾部依赖性都是与它们的相关性分开建模的，并且可以根据其自身的参数而不是相关性参数分别变化，这是相对于多变量$t$或椭圆分布等许多常用方法的一个本质优势。与copula方法相比，它还具有直观的解释、易于跟踪和简单的采样功能。通过仿真，我们展示了它灵活的尾部依赖结构。结合一个GARCH模型来消除每个资产收益序列的序列依赖性，我们使用这种新方法来建模和预测股票收益的多元条件分布，并在多维覆盖测试中取得了显著的性能改进。此外，我们关于特质成分和市场成分尾部不对称的实证发现是有趣的，值得在未来进行深入研究。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
--

---
PDF下载：
-->