Disaggregated ESG Risk in European Asset Pricing Based on ESG Leaders Data

Background: This study investigates the conditional pricing of environmental, social, governance (ESG)-related risk exposures – specifically ESG, carbon intensity, and controversy – using portfolio-level data from firms in the Morgan Stanley Capital International Europe ESG Leaders Index (2018–2024). The sample comprises nine sector-neutral portfolios, double-sorted by ESG and Controversy scores, ensuring balanced exposure across Europe’s leading ESG-rated firms. Aim: This study evaluates how factor decomposition, macro-regime sensitivity, and time-varying risk exposure affect ESG integration in multifactor pricing models. It also assesses the effectiveness of Kalman filtering in stabilizing ESG beta estimates under data limitations. Methodology: A two-stage Fama-MacBeth approach estimates ESG, carbon, and controversy betas using rolling regressions and Kalman filtering. These betas are then incorporated into fixed-effect panel regressions with macroeconomic volatility controls and regime interaction terms for the 2020–2021 regulatory and financial stress periods. Results: Disaggregated E, S, and G exposures exhibit significant positive return premia, particularly under stress. Carbon and controversy factors display conditional pricing effects that intensify under transition regimes. Kalman filtering yields smoother, more interpretable beta estimates than rolling regression, enhancing model robustness. Recommendation: ESG pricing models should incorporate factor decomposition, regime dynamics, and dynamic beta estimation, particularly Kalman filters – when working with quarterly or constrained datasets. Replicating this approach using data from multiple professional ESG providers would be valuable to assess the robustness of the pricing effects under rating divergence and disclosure heterogeneity. Practical relevance/social implications: This study offers a replicable framework for ESG researchers and investment practitioners seeking to identify time-varying, regime-sensitive, sustainable premiums for asset pricing. Originality/value: This study is among the first to combine ESG factor decomposition with Kalman-filtered beta estimation in a regime-augmented panel model using European portfolio data. Unlike the dominant United States-focused literature, it applies double-sorted, sector-neutral portfolios based on ESG and controversy scores. The findings demonstrate that robust ESG pricing signals can be uncovered even in small, high-quality European samples when the models are specified dynamically and contextually.