BOUNDS ON MULTI-ASSET DERIVATIVES VIA NEURAL NETWORKS

Luca de Gennaro Aquino and Carole Bernard
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Luca de Gennaro Aquino: Grenoble Ecole de Management, Department of Accounting, Law and Finance, 12 Rue Pierre Sémard, F-38000 Grenoble, France
Carole Bernard: Grenoble Ecole de Management, Department of Accounting, Law and Finance, 12 Rue Pierre Sémard, F-38000 Grenoble, France†Department of Economics and Political Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium

International Journal of Theoretical and Applied Finance (IJTAF), 2020, vol. 23, issue 08, 1-31

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. 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].

Keywords: Neural networks; arbitrage bounds; multivariate options; optimal transport (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1142/S0219024920500508

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