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

Ayla Jungbluth, Johannes Lederer and Simon Trimborn
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Ayla Jungbluth: Ruhr-University Bochum
Johannes Lederer: University of Hamburg
Simon Trimborn: University of Amsterdam

No 26-070/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

JEL-codes: C53 C58 G17 (search for similar items in EconPapers)
Date: 2026-09-13
New Economics Papers: this item is included in nep-ets and nep-inv
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