Design and hedging of unit linked life insurance with environmental factors
July 1, 2025·
Edoardo Lombardo
Equal contribution
,Katia Colaneri
Equal contribution
,Alessandra Cretarola
Equal contribution
,Daniele Mancinelli
Equal contribution
·
0 min readAbstract
We study the problem of designing and hedging unit linked life insurance 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 insurance company 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.
Type

Authors
Quantitative Researcher, Ph.D.
I am a Quantitative Researcher and Applied Mathematician with a Ph.D. from École des Ponts ParisTech and University of Roma Tor Vergata. My expertise lies at the intersection of stochastic calculus, high-performance computing, and numerical methods. I specialize in modeling complex stochastic dynamics and building high-performance numerical solutions in C++ and Python, transforming advanced mathematical theory into fast, accurate pricing and risk infrastructure. With prior industry experience as a Quantitative Analyst at Enel and Risk Analyst at AXA, I am passionate about applying advanced mathematical techniques, Monte Carlo simulations, and data-driven methods to solve complex pricing, risk, and alpha-generation problems in financial markets.