标题： 行为概率加权与半重尾下的投资组合优化 显示英文标题

标题： Behavioral Probability Weighting and Portfolio Optimization under Semi-Heavy Tails

摘要： 本文开发了一个统一的框架，通过从在高斯和正态逆高斯（NIG）收益率分布下建模的最优投资组合中提取隐含概率加权函数（PWFs），将行为扭曲整合到理性投资组合优化中。 使用道琼斯工业平均指数成分股，我们构建了均值-CVaR99前沿，以及最大化夏普比率和CVaR的投资组合，并估计了捕捉与恐惧和贪婪一致的非线性信念的PWFs。 我们表明，尾部肥厚的增加会加剧这些扭曲，并且无风险利率期限结构的变化会改变它们的曲率。 结果强调了在极端风险环境中进行投资组合风险管理与资本配置时，联合建模收益率不对称性和信念扭曲的重要性。 显示英文摘要

摘要： This paper develops a unified framework that integrates behavioral distortions into rational portfolio optimization by extracting implied probability weighting functions (PWFs) from optimal portfolios modeled under Gaussian and Normal-Inverse-Gaussian (NIG) return distributions. Using DJIA constituents, we construct mean-CVaR99 frontiers, alongwith Sharpe- and CVaR-maximizing portfolios, and estimate PWFs that capture nonlinear beliefs consistent with fear and greed. We show that increasing tail fatness amplifies these distortions and that shifts in the term structure of risk-free rates alter their curvature. The results highlight the importance of jointly modeling return asymmetry and belief distortions in portfolio risk management and capital allocation under extreme-risk environments.