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Option Pricing and Construction of Implied Volatility Surface based on Physics-Informed Neural Network
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dc.contributor.author배형옥-
dc.contributor.author강승구-
dc.contributor.author민찬호-
dc.contributor.author남상윤-
dc.date.issued2024-06-
dc.identifier.issn1738-124X-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/36031-
dc.description.abstractDeep 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.-
dc.language.isoEng-
dc.publisher한국금융공학회-
dc.titleOption Pricing and Construction of Implied Volatility Surface based on Physics-Informed Neural Network-
dc.title.alternativePINN을 활용한 옵션 평가와 변동성 곡면 구현-
dc.typeArticle-
dc.citation.endPage36-
dc.citation.number2-
dc.citation.startPage19-
dc.citation.title金融工學硏究-
dc.citation.volume23-
dc.identifier.bibliographicCitation金融工學硏究, Vol.23 No.2, pp.19-36-
dc.identifier.doi10.35527/kfedoi.2024.23.2.002-
dc.subject.keywordOption pricing-
dc.subject.keywordLocal volatility-
dc.subject.keywordNeural Network-
dc.subject.keywordBlack-Scholes equation (BSE)-
dc.subject.keywordPhysics-informed neural network (PINN)-
dc.subject.keyword옵션 가격 평가-
dc.subject.keyword국소 변동성-
dc.subject.keyword신경망-
dc.subject.keyword블랙-숄즈 방정식(BSE)-
dc.subject.keyword물리 정보 기반 신경망(PINN)-
dc.type.otherArticle-
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Bae, Hyeong Ohk배형옥
Department of Financial Engineering
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