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

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dc.contributor.author Hamid, Shaikh A.
dc.contributor.author Habib, Abraham
dc.date.accessioned 2011-01-24T20:40:31Z
dc.date.available 2011-01-24T20:40:31Z
dc.date.issued 2005
dc.identifier.uri http://hdl.handle.net/10474/1662
dc.description Version of Record
dc.description.abstract Neural 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.format.extent 415273 bytes en_US
dc.language.iso en_US en_US
dc.publisher Southern New Hampshire University en_US
dc.relation.requires Adobe Acrobat Reader en_US
dc.rights Authors retain all ownership rights. Further reproduction in violation of copyright is prohibited en_US
dc.subject.other Black-Scholes model
dc.subject.other neural networks
dc.subject.other implied volatility
dc.title Can neural networks learn the Black-Scholes model? A simplified approach en_US
dc.type Working Paper en_US
dc.description.bibliographicCitation Hamid, 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.digSpecs PDF/A-1b en_US
dc.format.mediaType application/pdf en_US


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