Option pricing using RBFN인공신경망을 활용한 옵션가격결정

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dc.contributor.advisorKang, Wan-Mo-
dc.contributor.advisor강완모-
dc.contributor.advisorKil, Rhee-Man-
dc.contributor.advisor길이만-
dc.contributor.authorIn, Yeo-Ju-
dc.contributor.author인여주-
dc.date.accessioned2011-12-14T04:56:36Z-
dc.date.available2011-12-14T04:56:36Z-
dc.date.issued2009-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=308741&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/42208-
dc.description학위논문(석사) - 한국과학기술원 : 수리과학과, 2009.2, [ vii, 40 p. ]-
dc.description.abstractOption pricing may be one of the most important issues in the trading market. In their 1973 paper, The Pricing of Options and Corporate Liabilities, Fischer Black and Myron Scholes published an option valuation formula that today is known as the Black-Scholes model. It has become the standard method of pricing options. And the large number of market participants use it. It is regarded as the most effective parametric model, but there are some assumptions that is inconsistent with real market. So it`s impossible with BS model to predict exact option price. So in this thesis we get the RBFN learning how option price and the other inputs are related. And furthermore we suggest the hybrid model mixed up Black-Scholes and RBFN.eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectoption-
dc.subjectprice-
dc.subjectRBFN-
dc.subjectneural-
dc.subjectnetwork-
dc.subject옵션-
dc.subject가격-
dc.subject인공지능-
dc.subject알고리즘-
dc.subject네트워크-
dc.subjectoption-
dc.subjectprice-
dc.subjectRBFN-
dc.subjectneural-
dc.subjectnetwork-
dc.subject옵션-
dc.subject가격-
dc.subject인공지능-
dc.subject알고리즘-
dc.subject네트워크-
dc.titleOption pricing using RBFN-
dc.title.alternative인공신경망을 활용한 옵션가격결정-
dc.typeThesis(Master)-
dc.identifier.CNRN308741/325007 -
dc.description.department한국과학기술원 : 수리과학과, -
dc.identifier.uid020073461-
dc.contributor.localauthorKang, Wan-Mo-
dc.contributor.localauthor강완모-
dc.contributor.localauthorKil, Rhee-Man-
dc.contributor.localauthor길이만-
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