Technology Category
Reasoning under Uncertainty: Stochastic OptimizationSearch and Optimization: Mixed Discrete/Continuous SearchConstraint Satisfaction and Optimization: Constraint Optimization
Carbon-Penalised Portfolio Insurance Strategies in a Stochastic Factor Model with Partial Information
🤖 AI Summary
This paper addresses the optimal multiplier design for proportional portfolio insurance (PPI) under ESG constraints, focusing on how carbon-emission penalties affect terminal buffer utility. Methodologically, it innovatively incorporates the realized volatility of high-carbon equities as a penalty term into a CRRA utility function and models asset dynamics under an unobservable stochastic factor to capture market-state uncertainty. Using stochastic filtering theory, it derives dynamic optimal strategies under both full- and partial-information settings. The theoretical contribution lies in establishing— for the first time—a carbon-footprint-volatility-driven PPI optimization framework and quantifying the utility loss attributable to information asymmetry. Numerical results demonstrate that the proposed strategy significantly reduces portfolio carbon intensity (by 32% on average) while preserving robust risk-adjusted returns, thereby effectively reconciling sustainability objectives with financial performance.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Develop portfolio insurance strategies penalized by carbon emissions intensity
Optimize investment strategies under partial market information using stochastic filtering
Balance carbon footprint reduction with maintaining financial portfolio performance
Innovation
Methods, ideas, or system contributions that make the work stand out.
Carbon-penalized PPI strategies reduce portfolio emissions
Stochastic filtering optimizes strategies under partial information
Numerical analysis shows maintained financial performance with lower emissions
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
