Paper: arXiv 2610.18332

Authors: Olivier Féron, Ruben Haalebos

Abstract

We study dynamic portfolio decarbonization under incomplete information about firms’ carbon intensities and the underlying transition state. The investor maximizes expected utility of terminal wealth net of a penalty on perceived cumulative financed emissions. Asset returns and carbon intensities depend on a stochastic transition factor, while the investor may need to infer the current transition state, its long-run mean and firms’ carbon intensities from market observations and periodic noisy disclosures. The optimal strategy combines a Merton-type component with climate-dependent expected-return adjustments, a direct carbon intensity penalty and intertemporal hedging demands associated with stochastic carbon intensities and learning. In a numerical application, incomplete information has limited effects on welfare and target satisfaction when prior beliefs are well specified. By contrast, biased beliefs generate welfare losses in either direction but asymmetric environmental consequences: optimistic and pessimistic beliefs induce opposite portfolio tilts and therefore affect the probability of exceeding the cumulative emissions target differently.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a sophisticated mathematical framework for portfolio decarbonization under uncertainty, incorporating stochastic control and Bayesian learning. While the numerical application demonstrates empirical relevance, the primary contribution lies in the theoretical model’s novelty and complexity. The findings on biased beliefs offer interesting practical implications.

Research Flowchart

  flowchart TD
    A[Research Goal: Dynamic Portfolio Decarbonization under Incomplete Carbon & Transition Info] --> B{Methodology: Stochastic Control & Optimal Portfolio Theory};
    B --> C{Inputs: Asset Returns, Carbon Intensities, Stochastic Transition Factor, Noisy Disclosures};
    C --> D[Computational Process: Optimal Strategy Derivation (Merton + Climate Adj. + Penalty + Hedging)];
    D --> E{Key Findings: Optimal Strategy Components};
    E --> F[Outcome 1: Limited Welfare/Target Effects with Well-Specified Priors];
    E --> G[Outcome 2: Biased Beliefs -> Welfare Losses & Asymmetric Environmental Impact];