Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/4032
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Arin, Efe | - |
dc.contributor.author | Özbayoğlu, Ahmet Murat | - |
dc.date.accessioned | 2021-01-25T11:28:54Z | - |
dc.date.available | 2021-01-25T11:28:54Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Arin, E., and Ozbayoglu, A. M. (2020). Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing. Computational Economics, 1-20. | en_US |
dc.identifier.issn | 0927-7099 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/4032 | - |
dc.identifier.uri | https://link.springer.com/article/10.1007/s10614-020-10063-9 | - |
dc.description.abstract | Options are commonly used by traders and investors for hedging their investments. They also allow the traders to execute leveraged trading opportunities. Meanwhile accurately pricing the intended option is crucial to perform such tasks. The most common technique used in options pricing is Black-Scholes (BS) formula. However, there are slight differences between the BS model output and the actual options price due to the ambiguity in defining the volatility. In this study, we developed hybrid deep learning based options pricing models to achieve better pricing compared to BS. The results indicate that the proposed models can generate more accurate prices for all option classes. Compared with BS using annualized 20 intraday returns as volatility, 94.5% improvement is achieved in option pricing in terms of mean squared error. | en_US |
dc.language.iso | en | en_US |
dc.publisher | SPRINGER | en_US |
dc.relation.ispartof | Computational Economics | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Option pricing | en_US |
dc.subject | Computational intelligence | en_US |
dc.subject | Deep neural networks | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Black Scholes | en_US |
dc.title | Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Artificial Intelligence Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.department | Fakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümü | tr_TR |
dc.relation.tubitak | info:eu-repo/grantAgreement/TÜBİTAK/EEEAG/215E248 | en_US |
dc.authorid | 0000-0001-7998-5735 | - |
dc.identifier.wos | WOS:000585155100001 | en_US |
dc.identifier.scopus | 2-s2.0-85094903719 | en_US |
dc.institutionauthor | Özbayoğlu, Ahmet Murat | - |
dc.identifier.doi | 10.1007/s10614-020-10063-9 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.1. Department of Artificial Intelligence Engineering | - |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering |
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