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Bounds on Multi-asset Derivatives via Neural Networks

Luca De Gennaro Aquino and Carole Bernard

Papers from arXiv.org

Abstract: 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 and discuss the maximizing/minimizing copulas achieving such bounds. Our approach follows the literature on constrained optimal transport and, in particular, builds on a recent paper by Eckstein and Kupper (2019, Appl. Math. Optim.).

Date: 2019-11, Revised 2020-02
New Economics Papers: this item is included in nep-cmp
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