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Option Pricing and Local Volatility Surface by Physics-Informed Neural Network
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Publication Year
2024-11-01
Publisher
Springer
Citation
Computational Economics, Vol.64, pp.3143-3159
Keyword
68T0791G20Artificial neural networkBlack–Scholes equation (BSE)C63Constant elasticity of variance (CEV)G13Option pricing, Local volatilityPhysics-informed neural network (PINN)
All Science Classification Codes (ASJC)
Economics, Econometrics and Finance (miscellaneous)Computer Science Applications
Abstract
We use an artificial neural network for finance in two directions: to estimate prices and Greeks based on the geometric Brownian motion and the constant elasticity of variance model for European options, and to construct a local volatility surface. To show the efficiency and successful usage of the network, we compare prices and Greeks obtained by a solution formula and by the artificial neural network when there is a solution formula is known. Then, we calculate Dupire’s equations to construct a local volatility surface by the network.
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/33957
DOI
https://doi.org/10.1007/s10614-024-10551-2
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Type
Article
Funding
Bae (NRF-2021R1A2C109338) have been partially supported by Basic Science Research Progream through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology, and by Ajou Research Fund.
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Bae, Hyeong Ohk Image
Bae, Hyeong Ohk배형옥
Department of Financial Engineering
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