Paper: arXiv 2604.11143

Authors: Michele Azzone, Carlo Bechi, Gabriele Sbaiz

Abstract

Driven by the increasing frequency and intensity of natural disasters and chronic climate threats, we investigate the impact of physical climate risk on global equity portfolios. By employing a panel regression analysis on sectoral returns, we provide statistical evidence that extreme temperature events exert a negative effect on most sectors. We introduce two novel metrics based on these temperature anomalies, Climate Risk Exposure and Climate Exposure Volatility, in order to measure the environmental vulnerability of a portfolio. Unlike available static country-level indices, these metrics incorporate the time varying probability of extreme events and their relations with firm-specific asset intensity. We integrate these measures into a multi-objective portfolio optimization framework. This approach extends the traditional Mean-Variance paradigm, allowing investors to construct portfolios that are resilient to physical climate shocks without sacrificing diversification. Finally, we conduct a backtesting analysis to show the practical benefits of incorporating these climate risk metrics into the investment process, evaluating how climate-aware strategies perform relative to traditional benchmarks.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 6.0/10 (capped from 6.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel approach to incorporating time-varying physical climate risk into a multi-objective portfolio optimization framework beyond static ESG ratings. It bridges panel regression methodologies with practical portfolio construction, supported by backtesting on global equities.

Research Flowchart

  flowchart TD
    A["Research Goal: Assess & mitigate physical climate risk in equity portfolios"] --> B["Inputs: Sectoral returns & extreme temperature anomaly data"]
    B --> C["Panel Regression Analysis: Assess negative impact on sectors"]
    C --> D["Construct Metrics: Climate Risk Exposure & Exposure Volatility"]
    D --> E["Multi-Objective Optimization: Integrate metrics into Mean-Variance framework"]
    E --> F["Backtesting Analysis: Evaluate climate-aware strategies vs. benchmarks"]
    F --> G["Key Outcome: Resilient portfolios against climate shocks without losing diversification"]

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2 of 46 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.