DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, GS | - |
dc.contributor.author | Lee, J | - |
dc.date.accessioned | 2016-04-01T01:16:32Z | - |
dc.date.available | 2016-04-01T01:16:32Z | - |
dc.date.created | 2009-04-01 | - |
dc.date.issued | 2008-07 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.other | 2008-OAK-0000007944 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/22645 | - |
dc.description.abstract | Gaussian process (GP) model is a Bayesian kernel-based learning machine. In this paper, we propose a GP model with a various mixed kernel for pricing and hedging ELWs (equity linked warrants) traded at KRX with predictive distribution. We experiment with daily market data relevant to KOSPI200 call ELWs from March 2006 to July 2006, comparing the performance of the GP model with those of various neural network (NN) models to show its effectiveness. The applied NN models contain early stopping, regularized NN, and bagging. The proposed GP model shows that its forecast capability outperforms those of the three NN models in terms of both pricing and hedging errors, thereby generating consistent results. (c) 2007 Elsevier Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.subject | equity linked warrants | - |
dc.subject | Gaussian processes | - |
dc.subject | derivatives | - |
dc.subject | hedging | - |
dc.subject | neural networks | - |
dc.subject | DERIVATIVE SECURITIES | - |
dc.subject | NEURAL-NETWORKS | - |
dc.subject | CLASSIFICATION | - |
dc.title | Prediction of pricing and hedging errors for equity linked warrants with Gaussian process models | - |
dc.type | Article | - |
dc.contributor.college | 산업경영공학과 | - |
dc.identifier.doi | 10.1016/j.eswa.2007.07.041 | - |
dc.author.google | Han, GS | - |
dc.author.google | Lee, J | - |
dc.relation.volume | 35 | - |
dc.relation.issue | 1-2 | - |
dc.relation.startpage | 515 | - |
dc.relation.lastpage | 523 | - |
dc.contributor.id | 10081901 | - |
dc.relation.journal | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.35, no.1-2, pp.515 - 523 | - |
dc.identifier.wosid | 000257617100052 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 523 | - |
dc.citation.number | 1-2 | - |
dc.citation.startPage | 515 | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 35 | - |
dc.contributor.affiliatedAuthor | Lee, J | - |
dc.identifier.scopusid | 2-s2.0-44949214945 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 15 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | DERIVATIVE SECURITIES | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordAuthor | equity linked warrants | - |
dc.subject.keywordAuthor | Gaussian processes | - |
dc.subject.keywordAuthor | derivatives | - |
dc.subject.keywordAuthor | hedging | - |
dc.subject.keywordAuthor | neural networks | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
library@postech.ac.kr Tel: 054-279-2548
Copyrights © by 2017 Pohang University of Science ad Technology All right reserved.