Portfolio Risk Bounds without Cross-Asset Return Covariances: Distributional Fields from Language-Model Representations

  • 类型：arxiv
  • 标识：2608.29692
  • 链接：https://arxiv.org/abs/2608.29692
  • 主分类：risk
  • 形态：method
  • 被引：2
  • 被引来源：Semantic Scholar
  • S2被引：2
  • 影响力被引：0
  • TLDR：Portfolio risk assessment ordinarily relies on reliable estimates of cross-asset return covariances, which are difficult to obtain in short, high-dimensional panels. We show that firm-level distribution-valued characteristics can instead provide one-sided certificates of portfolio risk. Under maintained links from characteristics to systematic exposures and from exposures to returns, multi-firm Wasserstein-2 dispersion yields a sharp upper bound on systematic portfolio variance and a corresponding bound for standardized returns. A weighted pairwise relaxation produces an objective that is conv
  • 待LLM分类：否
  • 标题中文：无需跨资产收益协方差的投资组合风险边界：来自语言模型表征的分布场
  • TLDR中文：投资组合风险评估通常依赖可靠的跨资产收益协方差估计，而在短、高维面板中难以获得。我们表明公司层面的分布型特征可提供投资组合风险的单边证书。在"从特征到系统性暴露、从暴露到收益"的既定联系下，多公司 Wasserstein-2 离散度对系统性投资组合方差给出紧上界，并对标准化收益给出相应边界。加权成对松弛产生一个目标函数……
  • 成熟度：research
  • 场景：投资组合、风险评估、资产收益
  • 来源文件：
  • /inbox/tom/_candidates/2026-09-04-agent-rag-longcontext-candidates.json
  • [S2 enrich]