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Market-Informed Networks for Modeling and Forecast Evaluation of Financial Extremes — arXiv:2609.11575

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.11575
License (as archived): CC0-1.0
Archived: 2026-10-16T05:59:47.637Z
SHA-256 of the archived text: 52a9cffa56bdcccd796d576e8d88cb2a6d39cfa26dbcc208bcf51f77b990131d
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How to cite

Market-Informed Networks for Modeling and Forecast Evaluation of Financial Extremes — arXiv:2609.11575 — arXiv (en), https://arxiv.org/abs/2609.11575 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bd00ebebbb10 (Metraipolis archive dc_bd00ebebbb10, sha256 52a9cffa56bdcccd796d576e8d88cb2a6d39cfa26dbcc208bcf51f77b990131d)

BibTeX

@misc{dcbd00ebebbb10,
  title = {Market-Informed Networks for Modeling and Forecast Evaluation of Financial Extremes — arXiv:2609.11575},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.11575},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bd00ebebbb10; sha256 52a9cffa56bdcccd796d576e8d88cb2a6d39cfa26dbcc208bcf51f77b990131d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.11575. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-11 in stat.ME).
Categorie arXiv: Methodology (stat.ME) ; Econometrics (econ.EM); Risk Management (q-fin.RM); Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2609.11575 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T05:49:22.704Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).