Estimation and prediction under local volatility jump-diffusion model
DC Field | Value | Language |
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dc.contributor.author | Kim, Namhyoung | - |
dc.contributor.author | Lee, Younhee | - |
dc.date.available | 2020-02-27T11:42:20Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2018-02-01 | - |
dc.identifier.issn | 0378-4371 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4058 | - |
dc.description.abstract | Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance. (C) 2017 Elsevier B.V. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | - |
dc.subject | STOCHASTIC VOLATILITY | - |
dc.subject | RISK PREMIA | - |
dc.subject | OPTIONS | - |
dc.subject | IMPLICIT | - |
dc.subject | ASSET | - |
dc.title | Estimation and prediction under local volatility jump-diffusion model | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000417661500064 | - |
dc.identifier.doi | 10.1016/j.physa.2017.09.035 | - |
dc.identifier.bibliographicCitation | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v.491, pp.729 - 740 | - |
dc.identifier.scopusid | 2-s2.0-85031114991 | - |
dc.citation.endPage | 740 | - |
dc.citation.startPage | 729 | - |
dc.citation.title | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | - |
dc.citation.volume | 491 | - |
dc.contributor.affiliatedAuthor | Kim, Namhyoung | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Option pricing | - |
dc.subject.keywordAuthor | Local volatility model | - |
dc.subject.keywordAuthor | Jump-diffusion model | - |
dc.subject.keywordAuthor | KOSPI 200 index option | - |
dc.subject.keywordPlus | STOCHASTIC VOLATILITY | - |
dc.subject.keywordPlus | RISK PREMIA | - |
dc.subject.keywordPlus | OPTIONS | - |
dc.subject.keywordPlus | IMPLICIT | - |
dc.subject.keywordPlus | ASSET | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Physics, Multidisciplinary | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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