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Option Pricing and Construction of Implied Volatility Surface based on Physics-Informed Neural Network
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Publication Year
2024-06
Journal
金融工學硏究
Publisher
한국금융공학회
Citation
金融工學硏究, Vol.23 No.2, pp.19-36
Keyword
Option pricingLocal volatilityNeural NetworkBlack-Scholes equation (BSE)Physics-informed neural network (PINN)옵션 가격 평가국소 변동성신경망블랙-숄즈 방정식(BSE)물리 정보 기반 신경망(PINN)
Abstract
Deep learning, utilizing artificial neural networks, offers capabilities in solving parametric Black-Scholes Equations under local volatility models. This study introduces dual-training methodology to calculate option price and implied volatility simultaneously, utilizing market data. For that, we use the Physics-Informed Neural Network as a deep learning, a novel approach harnessing physical information to efficiently solve parametric partial differential equations. The network provides two key advantages: construction of implied volatility surface on continuous state set, and allowing predictions at unobserved market values. This study presents a refined tool to practitioners in analyzing the local volatility model.
ISSN
1738-124X
Language
Eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36031
DOI
https://doi.org/10.35527/kfedoi.2024.23.2.002
Type
Article
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Bae, Hyeong Ohk Image
Bae, Hyeong Ohk배형옥
Department of Financial Engineering
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