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Spanning Multi-Asset Payoffs With ReLUs

Sébastien Bossu (), Stéphane Crépey () and Hoang-Dung Nguyen ()
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Sébastien Bossu: Department of Mathematics and Statistics [Charlotte] - UNC - University of North Carolina [Charlotte] - UNC - University of North Carolina System
Stéphane Crépey: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, UPCité - Université Paris Cité
Hoang-Dung Nguyen: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, UPCité - Université Paris Cité

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Abstract: We propose a distributional formulation of the spanning problem of a multi-asset payoff by vanilla basket options. This problem is shown to have a unique solution if and only if the payoff function is even and absolutely homogeneous, and we establish a Fourier-based formula to calculate the solution. Financial payoffs are typically piecewise linear, resulting in a solution that may be derived explicitly, yet may also be hard to numerically exploit. One-hidden-layer feedforward neural networks instead provide a natural and efficient numerical alternative for discrete spanning. We test this approach for a selection of archetypal payoffs and obtain better hedging results with vanilla basket options compared to industry-favored approaches based on single-asset vanilla hedges.

Keywords: Carr-Madan spanning formula; basket options; Fourier transform; iterated integrals; measures; distributions; Cauchy principal value integral; one-hidden-layer feedforward ReLU neural network; dispersion call; static hedging; 62G08; 62M45; 42B10; 46A11; 91G20 (search for similar items in EconPapers)
Date: 2024-11-29
Note: View the original document on HAL open archive server: https://hal.science/hal-04505407v5
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