DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, KH | - |
dc.contributor.author | Kim, HC | - |
dc.contributor.author | Lee, J | - |
dc.date.accessioned | 2016-04-01T01:52:14Z | - |
dc.date.available | 2016-04-01T01:52:14Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2006-01 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.other | 2006-OAK-0000006127 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/23888 | - |
dc.description.abstract | Nonparametric approaches for option pricing have recently emerged as alternative approaches that complement traditional parametric approaches. In this paper, we propose a novel learning network for option-pricing, which is a nonparametric approach. The main advantages of the proposed method are providing a principled hyper-parameter selection method and the distribution of predicted target value. With these features, we do not need to adjust any parameters at hand for model learning and we can get confidence interval as well as strict predicted target value. Experiments are conducted for the KOSPI200 index daily call options and their results show that the proposed method works excellently to obtain prediction confidence interval and to improve the option-pricing accuracy. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.relation.isPartOf | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.subject | HEDGING DERIVATIVE SECURITIES | - |
dc.title | A novel learning network for option pricing with confidence interval information | - |
dc.type | Article | - |
dc.contributor.college | 산업경영공학과 | - |
dc.identifier.doi | 10.1007/11760191_72 | - |
dc.author.google | Jung, KH | - |
dc.author.google | Kim, HC | - |
dc.author.google | Lee, J | - |
dc.relation.volume | 3973 | - |
dc.relation.startpage | 491 | - |
dc.relation.lastpage | 497 | - |
dc.contributor.id | 10081901 | - |
dc.relation.journal | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Conference Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | LECTURE NOTES IN COMPUTER SCIENCE, v.3973, pp.491 - 497 | - |
dc.identifier.wosid | 000239485300072 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 497 | - |
dc.citation.startPage | 491 | - |
dc.citation.title | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.citation.volume | 3973 | - |
dc.contributor.affiliatedAuthor | Lee, J | - |
dc.identifier.scopusid | 2-s2.0-33745892369 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 2 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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