Can neural networks learn the Black-Scholes model? A simplified approach

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dc.contributor.authorHamid, Shaikh A.
dc.contributor.authorHabib, Abraham
dc.date.accessioned2011-01-24T20:40:31Z
dc.date.available2011-01-24T20:40:31Z
dc.date.issued2005
dc.descriptionVersion of Record
dc.description.abstractNeural networks have been shown to learn complex relationships. It would be interesting to see if the networks can be trained to learn the nonlinear relationship underlying Black-Scholes type models. Interesting hypothetical questions that can be raised are: If option pricing model had not been developed, could a technique like neural networks have learnt the nonlinear form of the Black-Scholes type model to yield the fair value of an option? Could the networks have learnt to produce efficient implied volatility estimates? Our results from a simplified neural networks approach are rather encouraging, but more for volatility outputs than for call prices.en_US
dc.description.bibliographicCitationHamid, S. A. & Habib, A. (2005). Can neural networks learn the Black-Scholes model? A simplified approach (Working Paper No. 2005-01). Southern New Hampshire University, Center for Financial Studies.en_US
dc.digSpecsPDF/A-1ben_US
dc.format.extent415273 bytesen_US
dc.format.mediaTypeapplication/pdfen_US
dc.identifier.urihttps://hdl.handle.net/10474/1662
dc.language.isoen_USen_US
dc.publisherSouthern New Hampshire Universityen_US
dc.relation.requiresAdobe Acrobat Readeren_US
dc.rightsAuthors retain all ownership rights. Further reproduction in violation of copyright is prohibiteden_US
dc.subject.otherBlack-Scholes model
dc.subject.otherneural networks
dc.subject.otherimplied volatility
dc.titleCan neural networks learn the Black-Scholes model? A simplified approachen_US
dc.typeWorking Paperen_US

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