Carbon-Penalised Portfolio Insurance Strategies in a Stochastic Factor Model with Partial Information

TL;DR

This paper develops and analyzes a portfolio insurance strategy that penalizes carbon emissions, optimizing for utility under partial information and demonstrating its effectiveness in reducing carbon footprint without sacrificing returns.

Contribution

It introduces a novel carbon-penalized PPI strategy within a stochastic factor model, incorporating partial information and stochastic filtering techniques.

Findings

Strategy reduces carbon emission intensity

Utility loss due to partial information quantified

Numerical analysis confirms financial performance is maintained

Abstract

Given the increasing importance of environmental, social and governance (ESG) factors, particularly carbon emissions, we investigate optimal proportional portfolio insurance (PPI) strategies accounting for carbon footprint reduction. PPI strategies enable investors to mitigate downside risk while retaining the potential for upside gains. This paper aims to determine the multiplier of the PPI strategy to maximise the expected utility of the terminal cushion, where the terminal cushion is penalised proportionally to the realised volatility of stocks issued by firms operating in carbon-intensive sectors. We model the risky assets' dynamics using geometric Brownian motions whose drift rates are modulated by an unobservable common stochastic factor to capture market-specific or economy-wide state variables that are typically not directly observable. Using classical stochastic filtering theory, we formulate a suitable optimization problem and solve it for CRRA utility function. We characterise optimal carbon penalised PPI strategies and optimal value functions under full and partial information and quantify the loss of utility due incomplete information. Finally, we carry a numerical analysis showing that the proposed strategy reduces carbon emission intensity without compromising financial performance.