English(EN) ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall

新的ReSGA模型以数百万个参数增强金融风险预测能力

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-03 08:11

研究人员开发了ReSGA，一个旨在提高在险价值（VaR）和预期损失（ES）预测准确性的新型超大尾部风险模型。该模型拥有数百万个参数，利用了美国股票的大量横截面和时间数据。在样本外测试中，ReSGA的表现优于其他十二个模型，证明了其在金融风险管理方面的有效性。 AI

影响通过提高VaR和ES预测的准确性，该模型可能带来更稳健的金融风险管理策略。

排序理由该集群包含一篇详细介绍用于金融风险评估的新机器学习模型的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Yichi Zhang, Ke Zhu, Zhoufan Zhu · 2026-06-04 04:00

ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall

arXiv:2606.04576v1 Announce Type: new Abstract: Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively. Existing approaches with limited parameters are vulnerable to model misspecification in the era of big data. To address …
arXiv stat.ML TIER_1 English(EN) · Zhoufan Zhu · 2026-06-03 08:11

ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall

Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively. Existing approaches with limited parameters are vulnerable to model misspecification in the era of big data. To address this limitation, we propose a large tail risk mo…