Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/4032
Title: Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing
Authors: Arin, Efe
Özbayoğlu, Ahmet Murat
Keywords: Option pricing
Computational intelligence
Deep neural networks
Machine learning
Black Scholes
Issue Date: Nov-2020
Publisher: SPRINGER
Source: Arin, E., and Ozbayoglu, A. M. (2020). Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing. Computational Economics, 1-20.
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.
URI: https://hdl.handle.net/20.500.11851/4032
https://link.springer.com/article/10.1007/s10614-020-10063-9
ISSN: 0927-7099
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

Show full item record

CORE Recommender

SCOPUSTM   
Citations

1
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

3
checked on Sep 24, 2022

Page view(s)

268
checked on Nov 28, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.