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arXiv · 2507.13055

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

Artem Alkhamov·Boris Kriuk
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Abstract

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.

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Artem Alkhamov, Boris Kriuk. 2025-07-17. To What Extent Can Public Equity Indices Statistically Hedge Real Purchasing Power Loss in Compounded Structural Emerging-Market Crises? An Explainable ML-Based Assessment. https://arxiv.org/abs/2507.13055

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