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Compare of linear and neural networks models for estimating and forecasting Black-Scholes option pricing model

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dc.title Compare of linear and neural networks models for estimating and forecasting Black-Scholes option pricing model en
dc.contributor.author Benda, Radek
dc.relation.ispartof Finance and the Performance of Firms in Science, Education and Practice 2013
dc.identifier.isbn 978-80-7454-246-6
dc.date.issued 2013
dc.citation.spage 72
dc.citation.epage 84
dc.event.title 6th International Scientific Conference on Finance and the Performance of Firms in Science, Education, and Practice
dc.event.location Zlín
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2013-04-25
dc.event.edate 2013-04-26
dc.type conferenceObject
dc.language.iso en
dc.publisher Univerzita Tomáše Bati ve Zlíně (UTB) cs
dc.publisher Tomas Bata University in Zlín en
dc.relation.uri http://www.ufu.utb.cz/sbornik/proceedings2013.pdf
dc.subject Black-Scholes model en
dc.subject Artificial Neural Networks (ANN) en
dc.subject Implied volatilities en
dc.subject Option pricing en
dc.subject Hedging en
dc.subject Statistical inference en
dc.description.abstract The Black-Scholes formula is a well-known model for pricing and hedging derivative securities. 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? The results in this article from a simplified neural networks approach are rather encouraging, but more for volatility outputs than for call prices. This article will evaluate the performance of alternative neural network models relative to the standard linear model for forecasting relatively complex artificially generated time series. The article shows that relatively simple feedforward neural nets outperform the linear models in some cases, or do not do worse than the linear models. en
utb.faculty Faculty of Management and Economics
dc.identifier.uri http://hdl.handle.net/10563/1003651
utb.identifier.obdid 43871036
utb.identifier.wok 000329435800006
utb.source d-wok
dc.date.accessioned 2014-02-12T16:15:54Z
dc.date.available 2014-02-12T16:15:54Z
utb.identifier.utb-sysno 57048
utb.contributor.internalauthor Benda, Radek
utb.fulltext.affiliation Benda Radek Ing. Radek Benda, Ph.D. Department of Statistics and Quantitative Methods, Faculty of Management and Economics, Tomas Bata University in Zlín, Mostní 5139, Zlín 760 01, The Czech Republic. Email: benda@fame.utb.cz
utb.fulltext.dates -
utb.fulltext.sponsorship -
utb.fulltext.projects -
utb.fulltext.faculty Faculty of Management and Economics
utb.fulltext.ou Department of Statistics and Quantitative Methods
utb.identifier.jel C39
utb.identifier.jel C49
utb.identifier.jel C59
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