Kontaktujte nás | Jazyk: čeština English
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 |