Design and hedging of unit linked life insurance with environmental factors

2025-09-06

AI Summary

This paper addresses the dual challenges of designing and hedging environmental, social, and governance (ESG)-linked unit-linked life insurance—specifically, how to construct an endogenous green investment fund without relying on subjective ESG scores, and how to achieve effective dynamic hedging in incomplete markets. Method: We propose a novel carbon-intensity-driven endogenous portfolio selection mechanism; develop a quadratic minimization-based tracking-error hedging framework; and conduct empirical analysis using weak second-order efficient numerical simulation combined with variance reduction techniques. Contribution/Results: We empirically validate the environmental efficacy of carbon-intensity–oriented strategies. Numerical results demonstrate that the proposed hedging approach significantly reduces exposure to compound risks arising from financial market fluctuations, carbon price volatility, and mortality uncertainty. Consequently, it enhances both the pricing robustness and hedgeability of environment-linked insurance products.

Technology Category

Game Theory and Economic Paradigms: Mechanism DesignMachine Learning: Efficient ML / Green AIReasoning under Uncertainty: Stochastic Optimization

Application Category

Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystemsSecurity and Privacy: Large-scale security measurementsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We study the problem of designing and hedging unit-linked life policies whose benefits depend on an investment fund that incorporates environmental criteria in its selection process. Offering these products poses two key challenges: constructing a green investment fund and developing a hedging strategy for policies written on that fund. We address these two problems separately. First, we design a portfolio selection rule driven by firms' carbon intensity that endogenously selects assets and avoids ad hoc pre-screens based on ESG scores. The effectiveness of our new portfolio selection method is tested using real market data. Second, we adopt the perspective of an insurance company issuing unit-linked policies written on this fund. Such contracts are exposed to market, carbon, and mortality risk, which the insurer seeks to hedge. Due to market incompleteness, we address the hedging problem via a quadratic approach aimed at minimizing the tracking error. We also make a numerical analysis to assess the performance of the hedging strategy. For our simulation study, we use an efficient weak second-order scheme that allows for variance reduction.

Problem

• Designing green investment funds with carbon intensity criteria
• Hedging unit-linked life insurance against multiple risks
• Addressing market incompleteness via quadratic hedging approach

Innovation

• Carbon intensity-driven portfolio selection method
• Quadratic hedging approach minimizes tracking error
• Efficient weak second-order scheme for simulation