市场信息网络用于金融极值的建模与预测评估

中文摘要 AI 辅助

在高维金融时间序列中，对极值的联合分布进行建模具有挑战性，因为极值是稀疏的，且局部极端的观测值相对于其完整的边际分布而言，未必是极端的。为解决这一问题，我们引入了一种时变网络Hüsler-Reiss模型，其中市场信息邻接矩阵决定了观测值对估计的贡献强度。我们提出了二元和加权两种规格，包括联合极值邻接矩阵（JEAM），该矩阵将个体极端性信息与联合极端变动的历史模式相结合。在预测评估部分，覆盖标普100指数三个板块的一分钟股票收益率，JEAM在两个尾部方向均取得了最佳的样本外对数分数；在下尾提高了12.5%-13.6%，在上尾提高了11.4%-14.9%。结果表明，在估计中纳入市场信息网络结构，可改善跨时间序列的极值预测评估。

英文摘要

Modeling the joint distribution of extreme values in high-dimensional financial time series is challenging because extremes are sparse and locally extreme observations are not necessarily extreme relative to their full marginal distribution. To address this, we introduce a time-dependent network Hüsler-Reiss model in which market-informed adjacency matrices determine how strongly observations contribute to the estimation. We propose binary and weighted specifications, including the Joint Extremes Adjacency Matrix (JEAM) which combines information about individual extremeness with historical patterns of joint extreme movements. In the forecasting evaluation part, covering one-minute stock returns from three sectors of the S&P 100, JEAM achieves the best out-of-sample log scores for both tail directions; improving scores by 12.5-13.6% in the lower tail and 11.4-14.9% in the upper tail. The results show that incorporating market-informed network structures in the estimation, improves forecast evaluation of extremes across time series.

发表机构

• Ruhr-University Bochum（波鸿鲁尔大学）
• University of Hamburg（汉堡大学）
• University of Amsterdam（阿姆斯特丹大学）

机构由 AI 辅助整理，请以论文原文为准。