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Neural network pricing of american put options

dc.contributor.authorGaspar, Raquel M.
dc.contributor.authorLopes, Sara D.
dc.contributor.authorSequeira, Bernardo
dc.date.accessioned2024-07-02T14:23:00Z
dc.date.available2024-07-02T14:23:00Z
dc.date.issued2020
dc.description.abstractIn this study, we use Neural Networks (NNs) to price American put options. We propose two NN models—a simple one and a more complex one—and we discuss the performance of two NN models with the Least-Squares Monte Carlo (LSM) method. This study relies on American put option market prices, for four large U.S. companies—Procter and Gamble Company (PG), Coca-Cola Company (KO), General Motors (GM), and Bank of America Corp (BAC). Our dataset is composed of all options traded within the period December 2018 until March 2019. Although on average, both NN models perform better than LSM, the simpler model (NN Model 1) performs quite close to LSM. Moreover, the second NN model substantially outperforms the other models, having an RMSE ca. 40% lower than the presented by LSM. The lower RMSE is consistent across all companies, strike levels, and maturities. In summary, all methods present a good accuracy; however, after calibration, NNs produce better results in terms of both execution time and Root Mean Squared Error (RMSE).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGaspar, Raquel, Sara D. Lopes and Bernardo Sequeira (2020). "Neural network pricing of american put options". MDPI. Risks, Vol. 8, No. 3: pp. 1-24. (Search PDF in 2024)pt_PT
dc.identifier.doidoi.org/10.3390/risks8030073pt_PT
dc.identifier.eissn2227-9091
dc.identifier.urihttp://hdl.handle.net/10400.5/31225
dc.language.isoengpt_PT
dc.publisherMDPI - Academic Open Acess Publishingpt_PT
dc.subjectMachine Learningpt_PT
dc.subjectNeural Networkspt_PT
dc.subjectAmerican Put Optionspt_PT
dc.subjectLeast-Squares Monte Carlopt_PT
dc.titleNeural network pricing of american put optionspt_PT
dc.typejournal article
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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