Market-Informed Networks for Modeling and Forecast Evaluation of Financial Extremes

2026-09-10

AI Summary

本文通过引入基于市场信息的网络Hüsler-Reiss模型，解决了高维金融时间序列中极端值联合分布建模难题，并在S&P 100三个板块的一分钟股票回报预测评估中提高了尾部预测准确性。

Abstract

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.

Problem

• extreme values
• high-dimensional financial time series
• joint distribution
• sparse

Innovation

• time-dependent network Hüsler-Reiss model
• market-informed adjacency matrices
• Joint Extremes Adjacency Matrix (JEAM)
• tail forecast evaluation