Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/11951
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gradojevic N. | en |
dc.contributor.author | Gençay R. | en |
dc.contributor.author | Kukolj, Dragan | en |
dc.date.accessioned | 2020-03-03T14:46:37Z | - |
dc.date.available | 2020-03-03T14:46:37Z | - |
dc.date.issued | 2009-03-10 | en |
dc.identifier.issn | 10459227 | en |
dc.identifier.uri | https://open.uns.ac.rs/handle/123456789/11951 | - |
dc.description.abstract | This paper investigates a nonparametric modular neural network (MNN) model to price the S&P-500 European call options. The modules are based on time to maturity and moneyness of the options. The option price function of interest is homogeneous of degree one with respect to the underlying index price and the strike price. When compared to an array of parametric and nonparametric models, the MNN method consistently exerts superior out-of-sample pricing performance. We conclude that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint). © 2009 IEEE. | en |
dc.relation.ispartof | IEEE Transactions on Neural Networks | en |
dc.title | Option pricing with modular neural networks | en |
dc.type | Journal/Magazine Article | en |
dc.identifier.doi | 10.1109/TNN.2008.2011130 | en |
dc.identifier.scopus | 2-s2.0-67349202752 | en |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/67349202752 | en |
dc.relation.lastpage | 637 | en |
dc.relation.firstpage | 626 | en |
dc.relation.issue | 4 | en |
dc.relation.volume | 20 | en |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Departman za računarstvo i automatiku | - |
crisitem.author.parentorg | Fakultet tehničkih nauka | - |
Appears in Collections: | FTN Publikacije/Publications |
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