Bounds on Multi Asset Derivatives via Neural Networks

Carole Bernard, Luca De Gennaro

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Using neural networks, we compute bounds on the prices of multi-asset derivatives given information on prices of related payoffs. As a main example, we focus on European basket options and include information on the prices of other similar options, such as spread options and/or basket options on subindices. We show that, in most cases, adding further constraints gives rise to bounds that are considerably tighter. Our approach follows the literature on constrained optimal transport and, in particular, builds on the work of Eckstein & Kupper (2018) [Computation of optimal transport and related hedging problems via penalization and neural networks, Appl. Math. Optimiz. 1-29].

Original languageEnglish
Article number2050050
Number of pages20
JournalInternational Journal of Theoretical and Applied Finance
Issue number8
Publication statusPublished - Dec 2020


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