标题： 公共股权指数在复合结构新兴市场危机中能够多大程度上统计上对冲实际购买力损失？ 基于可解释机器学习的评估 显示英文标题

标题： To What Extent Can Public Equity Indices Statistically Hedge Real Purchasing Power Loss in Compounded Structural Emerging-Market Crises? An Explainable ML-Based Assessment

摘要： 本研究调查了在新兴市场复合结构性宏观金融崩溃期间，本地公共股权指数在统计上能够对冲实际购买力损失的程度。 我们采用与费雪平价逻辑一致的非线性乘法实际收益计算方法，针对国内和外国投资者进行原理性分位数回归、尾部依赖Copula分析以及Shapley加法解释（SHAP），以评估宏观变量的解释力。 分析聚焦于三个近期且数据可访问的典型崩溃事件：土耳其（2018年）、尼日利亚（2020年）和巴基斯坦（2021年）。 这些案例被选择以符合2018年后数据标准化和危机可比性的改进，涵盖了不同的货币制度和危机触发因素。 我们的尾部导向建模揭示了在宏观经济和货币失衡同时发生时，基于公共股权的购买力保护出现了系统性失效，而此时保护最为需要。 研究结果质疑了股权定价理论中传统的通胀和贬值对冲假设，强调了基于股权的保护的局限性，并在复合宏观金融困境期间提出了对情境敏感策略的必要性。 显示英文摘要

摘要： This study investigates the extent to which local public equity indices can statistically hedge real purchasing power loss during compounded structural macro-financial collapses in emerging markets. We employ a non-linear multiplicative real return calculations consistent with Fisher-parity logics for both domestic and foreign investors with a principled quantile regression, tail dependence copula analysis, and Shapley Additive Explanations (SHAP) to assess the explanatory power of macro variables. The analysis focuses on three recent and data-accessible exemplary collapse episodes: Turkey (2018), Nigeria (2020), and Pakistan (2021). Such cases, selected to align with post-2018 improvements in data standardization and crisis comparability, span varied monetary regimes and crisis triggers. Our tail-focused modeling reveals a systematic breakdown in public-equity-based purchasing power protection precisely during simultaneous macroeconomic and monetary dislocations when such protection is most needed. The findings call into question conventional inflation and devaluation hedge presumptions in equity pricing theory, emphasizing the limitations of equity-based protection and the need for context-sensitive strategies during compounded macro-financial distress.