Paper: arXiv 2501.03269

Abstract

This study investigates the resilience of Environmental, Social, and Governance (ESG) investments during periods of financial instability, comparing them with traditional equity indices across major European markets-Germany, France, and Italy. Using daily returns from October 2021 to February 2024, the analysis explores the effects of key global disruptions such as the Covid-19 pandemic and the Russia-Ukraine conflict on market performance. A mixture of two generalised normal distributions (MGND) and EGARCH-in-mean models are used to identify periods of market turmoil and assess volatility dynamics. The findings indicate that during crises, ESG investments present higher volatility in Germany and Italy than in France. Despite some regional variations, ESG portfolios demonstrate greater resilience compared to traditional ones, offering potential risk mitigation during market shocks. These results underscore the importance of integrating ESG factors into long-term investment strategies, particularly in the face of unpredictable financial turmoil.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced econometric models like mixture of generalized normal distributions (MGND) and EGARCH-in-mean to identify turmoil and volatility dynamics, indicating high math complexity. It uses daily returns from specific European markets (Germany, France, Italy) over a defined period (Oct 2021–Feb 2024) with detailed analysis of resilience during crises, showing high data usage and backtest-ready methodology.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Assess Resilience of ESG vs Traditional<br>Equities in European Markets<br>During Turmoil"] --> B["Data & Period<br>Daily Returns (Oct 2021 - Feb 2024)<br>Markets: DE, FR, IT"]
  
  B --> C["Methodology"]
  
  subgraph C [" "]
      C1["MGND Model<br>Identify Turmoil Periods"] --> C2["EGARCH-in-Mean<br>Volatility Modeling"]
  end

  C2 --> D{"Key Findings & Outcomes"}

  D --> D1["Higher Volatility in<br>Germany & Italy"]
  D --> D2["ESG Outperforms<br>Traditional Indices"]
  D --> D3["Recommendation:<br>ESG as Risk Mitigation<br>in Long-Term Strategies"]

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0 of 83 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.