Ayla Jungbluth, Johannes Lederer, Simon Trimborn
arXiv 10 Sep 2026 · Statistics — Methodology
arXiv:2609.11575 · PDF · Extracted main text
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.
appendix boundary found by appendix_command · 91% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lederer, J. and Oesting, M (2024) Extremes in high dimensions: Methods and scalable algorithms self | 0.874 | 7 | 2 | 100% |
| 2 | Longin, F. and Solnik, B (2001) Extreme correlation of international equity markets | 0.843 | 3 | 3 | 100% |
| 3 | Engelke, S., Malinowski, A., Kabluchko, Z., and Schlather, M (2015) Estimation of hüsler–reiss distributions and brown–resnick processes | 0.737 | 3 | 2 | 100% |
| 4 | Rachev, S. T. and Mittnik, S (2000) Stable Paretian Models in Finance | 0.511 | 2 | 1 | 100% |
| 5 | Engelke, S. and Hitz, A. S (2020) Graphical models for extremes | 0.511 | 2 | 1 | 100% |
| 6 | Hüsler, J. and Reiss, R.-D (1989) Maxima of normal random vectors: between independence and complete dependence | 0.511 | 2 | 1 | 100% |
| 7 | Davis, R. A. and Mikosch, T (2009) The extremogram: A correlogram for extreme events | 0.405 | 1 | 1 | 100% |
| 8 | Mandelbrot, B (1963) The variation of certain speculative prices | 0.405 | 1 | 1 | 100% |
| 9 | Nolan, J. P (2020) Univariate Stable Distributions: Models for Heavy Tailed Data | 0.405 | 1 | 1 | 100% |
| 10 | Audrino, F., Sigrist, F., and Ballinari, D (2020) The impact of sentiment and attention measures on stock market volatility | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 31 scored citations.