ESG Portfolio Optimization: The Relevance of Higher Order Moments

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Abstract
Environmental, social, and governance (ESG) factors have become key factors in modern portfolio management, shaping how investors think and how they allocate their assets. At the same time, the presence of asymmetric and heavy‐tailed return distributions highlights the necessity of moving beyond the classical mean–variance (MV) framework by incorporating higher‐order moments, such as skewness and kurtosis, into portfolio optimization. To address this issue, we introduce a unified mean–variance‐skewness‐kurtosis‐ESG (MVSK‐ESG) optimization model. This model uses different ESG score thresholds and focuses on ESG leaders within the Dow Jones Industrial Average and the Nasdaq 100. This model incorporates ESG scores into the objective function using a difference‐of‐convex programming framework to address the model's inherent nonconvexity. Empirical results show that MVSK‐ESG portfolios consistently outperform traditional MV and ESG‐constrained portfolios as well as their benchmarks, with higher risk‐adjusted returns. The proposed framework provides a robust approach for integrating sustainability considerations into portfolio construction.
Funding Information
  • Junta de Castilla y León (Ref.SA094P24)

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