In this contribution, we discuss how to handle financial and sustainable investment goals, focusing on greenness and ESG features. Sustainable investing has attracted increasing interest with an associated growing commitment to take an active part in investment choices. Among thematic investments, green and energy-related ones have emerged, capturing investors’ attention. Non-optimized strategies and traditional portfolio allocation models cannot guarantee the necessary flexibility. To answer this demand, ESG tailored-made allocations should be provided, with the aim of representing the preferences and commitments of investors adequately. This contribution introduces a novel ESG-focused tracking error model to optimize portfolio allocation. We consider two reference benchmarks, accounting for a financial target and an ESG one, respectively. The objective function results in a convex linear combination of the two goals where the parameter λ accounts for the investor’s financial and ESG preferences. A symmetric tracking error measure is proposed to replicate the financial benchmark passively, while an asymmetric measure is used to track and possibly outperform the thematic ESG benchmark. Identifying the benchmarks for the two components represents a crucial step and, jointly with the choice of the parameter λ, accounts for the portfolio’s overall risk-return and ESG profiles. In the model, the sustainability feature is handled not only with the presence of the ESG benchmark but also with the introduction of dedicated constraints. Namely, a desired minimum level of greenness and a maximum amount of carbon intensity can be accounted for. An application to the EUROSTOXX 600 equity market is presented and discussed for different choices of the parameter λ, representing different sustainability preferences and risk-return profiles. Furthermore, a discussion on the choice of the benchmarks is provided.

Tracking-Based Green Portfolio Optimization: Bridging Sustainability and Market Performance

Barro, Diana
;
Corazza, Marco
;
Filograsso, Gianni
2025-01-01

Abstract

In this contribution, we discuss how to handle financial and sustainable investment goals, focusing on greenness and ESG features. Sustainable investing has attracted increasing interest with an associated growing commitment to take an active part in investment choices. Among thematic investments, green and energy-related ones have emerged, capturing investors’ attention. Non-optimized strategies and traditional portfolio allocation models cannot guarantee the necessary flexibility. To answer this demand, ESG tailored-made allocations should be provided, with the aim of representing the preferences and commitments of investors adequately. This contribution introduces a novel ESG-focused tracking error model to optimize portfolio allocation. We consider two reference benchmarks, accounting for a financial target and an ESG one, respectively. The objective function results in a convex linear combination of the two goals where the parameter λ accounts for the investor’s financial and ESG preferences. A symmetric tracking error measure is proposed to replicate the financial benchmark passively, while an asymmetric measure is used to track and possibly outperform the thematic ESG benchmark. Identifying the benchmarks for the two components represents a crucial step and, jointly with the choice of the parameter λ, accounts for the portfolio’s overall risk-return and ESG profiles. In the model, the sustainability feature is handled not only with the presence of the ESG benchmark but also with the introduction of dedicated constraints. Namely, a desired minimum level of greenness and a maximum amount of carbon intensity can be accounted for. An application to the EUROSTOXX 600 equity market is presented and discussed for different choices of the parameter λ, representing different sustainability preferences and risk-return profiles. Furthermore, a discussion on the choice of the benchmarks is provided.
2025
Department of Economics Research Paper Series
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5105887
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