Green ETF Returns across the Conditional Distribution: Tail Asymmetry, Low-Frequency Market Stress, and Dynamic Dependence

Abstract

We revisit return-distribution and volatility differences between a broad U.S. ESG ETF and the market, and between clean- and fossil-energy ETFs, using a fully reproducible daily sample from January 2017 through July 2026. The design separates two economically different spreads: ESGU minus SPY and ICLN minus XLE. Conditional quantile regressions show near-zero median spreads but substantial dispersion in both tails. Importantly, negative lower-tail estimates denote relative underperformance, not downside protection. A custom GARCH-MIDAS model with twelve strictly lagged monthly VIX observations yields positive long-run market-stress loadings for all four ETFs; all solutions are internal, although ICLN is comparatively ill-conditioned. Formal dynamic-correlation tests around 24 February 2022 show opposite shifts: ESGU-SPY dependence rises, whereas ICLN-XLE dependence falls. These findings reject a single, universal “green premium” narrative. Broad ESG tilting behaves differently from thematic clean-energy exposure, and conditional-distribution evidence cannot by itself identify an ESGpricing mechanism. The paper also documents how sign errors, unavailable ESG-score histories, and boundary solutions can generate misleading interpretations in small-sample sustainable-finance applications.

Author abstract from the research manuscript. Typography and line breaks have been normalized; the linked source may show a different manuscript version.