We rely on bilevel programming to model the problem of financial service providers that, in order to meet stakeholders' demands and regulatory requirements, aim at incentivizing accounts' holders to construct ESG-oriented portfolios so that the overall ESG impact of the firm is optimized, while the preferences of accounts' owners are still satisfied. We analyze this complicated framework from a theoretical point of view and identify sufficient conditions that make it numerically tractable via a novel, specifically tailored algorithm, whose convergence properties are studied. Numerical testing on real-world data confirms the theoretical insights and shows that our model can be solved even when dealing with considerable problem sizes.

A bilevel approach to ESG multi-portfolio selection / Cesarone, F; Lampariello, L; Merolla, D; Ricci, Jm; Sagratella, S; Sasso, Vg. - In: COMPUTATIONAL MANAGEMENT SCIENCE. - ISSN 1619-697X. - 20:1(2023). [10.1007/s10287-023-00458-y]

A bilevel approach to ESG multi-portfolio selection

Merolla, D;Sagratella, S;Sasso, VG
2023

Abstract

We rely on bilevel programming to model the problem of financial service providers that, in order to meet stakeholders' demands and regulatory requirements, aim at incentivizing accounts' holders to construct ESG-oriented portfolios so that the overall ESG impact of the firm is optimized, while the preferences of accounts' owners are still satisfied. We analyze this complicated framework from a theoretical point of view and identify sufficient conditions that make it numerically tractable via a novel, specifically tailored algorithm, whose convergence properties are studied. Numerical testing on real-world data confirms the theoretical insights and shows that our model can be solved even when dealing with considerable problem sizes.
2023
Sustainable investment strategies; Multi-portfolio selection; ESG rating scores; Nash equilibrium problems; Bilevel optimization
01 Pubblicazione su rivista::01a Articolo in rivista
A bilevel approach to ESG multi-portfolio selection / Cesarone, F; Lampariello, L; Merolla, D; Ricci, Jm; Sagratella, S; Sasso, Vg. - In: COMPUTATIONAL MANAGEMENT SCIENCE. - ISSN 1619-697X. - 20:1(2023). [10.1007/s10287-023-00458-y]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1681852
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