European and American Options Valuation by Unsupervised Learning with Artificial Neural Networks
| Autor: | Cornelis W. Oosterlee, Remco van der Meer, Beatriz Salvador |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Mathematical optimization Partial differential equation Computer science lcsh:A Black–Scholes model Linear complementarity problem Finite element method (Non)linear PDEs loss function multi-asset options Unsupervised learning Minification (non)linear PDEs lcsh:General Works artificial neural network Valuation (finance) |
| Zdroj: | Proceedings, Vol 54, Iss 14, p 14 (2020) RUC. Repositorio da Universidade da Coruña instname RUC: Repositorio da Universidade da Coruña Universidade da Coruña (UDC) |
| ISSN: | 2504-3900 |
| Popis: | [Abstract] Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs). In this work, the classical problem of pricing European and American financial options, based on the corresponding PDE formulations, is studied. Instead of using numerical techniques based on finite element or difference methods, we address the problem using ANNs in the context of unsupervised learning. As a result, the ANN learns the option values for all possible underlying stock values at future time points, based on the minimization of a suitable loss function. For the European option, we solve the linear Black–Scholes equation, whereas for the American option, we solve the linear complementarity problem formulation. |
| Databáze: | OpenAIRE |
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