arXiv cs.AI· Sourav Bose, Taoufik Bouraoui·· 13 小时前AI 评分18
超越漂绿：算法韧性与主权资本流动中的非线性治理溢价
Beyond Greenwashing: Algorithmic Resilience and the Non-Linear Governance Premium in Sovereign Capital Flows
AI 导读
研究用梯度提升架构对147个主权实体2010-2024年的前向链式全球面板数据建模，在极端宏观尾部事件期间将预测误差较L1正则化基线压缩超39%。双可解释性框架揭示严格的非线性“治理溢价”：环境与社会改善在监管门槛被突破前不带来任何资本优势。研究还建立制度条件下的95%不确定性边界，在ESG报告严重失效时仍保持有效的边际覆盖。
正文 · 原文
Abstract:Despite the proliferation of sovereign Environmental, Social, and Governance (ESG) metrics, traditional linear macro-financial models systematically misprice Foreign Direct Investment (FDI) tail risks during systemic shocks. This paper demonstrates that sovereign risk transmission is fundamentally non-linear. Using a forward-chained global panel of 147 sovereign entities (2010-2024), we deploy a gradient boosting architecture that compresses forecast errors by over 39% against L1-regularized baselines precisely during extreme macroeconomic tail events. Moving beyond opaque predictive accuracy, we apply dual interpretability frameworks to mathematically map the discrete threshold effects of institutional quality. We identify a strict, non-linear "Governance Premium": incremental environmental and social improvements yield zero capital advantage until a foundational regulatory threshold is breached. Finally, we establish regime-conditional 95% uncertainty bounds that maintain valid marginal coverage even under severe ESG reporting failures. This framework equips policymakers with a distribution-free tool to quantify sovereign tail-risk, proving that global capital markets price institutional resilience as a discrete boundary rather than a continuous premium.
| Subjects: | Applications (stat.AP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Econometrics (econ.EM) |
| Cite as: | arXiv:2610.35895 [stat.AP] |
| (or arXiv:2610.35895v1 [stat.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35895 arXiv-issued DOI via DataCite |
Submission history
From: Sourav Bose [view email]
[v1]
Sat, 19 Sep 2026 22:14:03 UTC (1,849 KB)
来源：arXiv cs.AI · arxiv.org