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Statements

Subject Item
n2:RIV%2F70883521%3A28120%2F13%3A43870718%21RIV14-MSM-28120___
rdf:type
n9:Vysledek skos:Concept
rdfs:seeAlso
http://www.ufu.utb.cz/sbornik/proceedings2013.pdf
dcterms:description
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. 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.
dcterms:title
Compare Of Linear And Neural Networks Models For Estimating And Forecasting Black-Scholes Option Pricing Model Compare Of Linear And Neural Networks Models For Estimating And Forecasting Black-Scholes Option Pricing Model
skos:prefLabel
Compare Of Linear And Neural Networks Models For Estimating And Forecasting Black-Scholes Option Pricing Model Compare Of Linear And Neural Networks Models For Estimating And Forecasting Black-Scholes Option Pricing Model
skos:notation
RIV/70883521:28120/13:43870718!RIV14-MSM-28120___
n9:predkladatel
n13:orjk%3A28120
n3:aktivita
n11:V
n3:aktivity
V
n3:dodaniDat
n8:2014
n3:domaciTvurceVysledku
n15:4627563
n3:druhVysledku
n4:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
66103
n3:idVysledku
RIV/70883521:28120/13:43870718
n3:jazykVysledku
n6:eng
n3:klicovaSlova
Black-Scholes model||Artificial Neural Networks (ANN)||Implied volatilities||Option pricing||Hedging||Statistical inference
n3:klicoveSlovo
n18:Black-Scholes%20model%7C%7CArtificial%20Neural%20Networks%20%28ANN%29%7C%7CImplied%20volatilities%7C%7COption%20pricing%7C%7CHedging%7C%7CStatistical%20inference
n3:kontrolniKodProRIV
[DE261FB15BFA]
n3:mistoKonaniAkce
Zlín
n3:mistoVydani
Zlín
n3:nazevZdroje
Proceedings of the 6th International Scientific Conference Finance and the performance of firms in science, education, and practice
n3:obor
n22:AE
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:rokUplatneniVysledku
n8:2013
n3:tvurceVysledku
Benda, Radek
n3:typAkce
n7:WRD
n3:wos
000329435800006
n3:zahajeniAkce
2013-04-25+02:00
s:numberOfPages
13
n17:hasPublisher
Univerzita Tomáše Bati ve Zlíně. Fakulta managementu a ekonomiky
n19:isbn
978-80-7454-246-6
n21:organizacniJednotka
28120