Application of reinforcement learning in valuing option강화학습을 활용한 옵션 가치 평가

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dc.contributor.advisorMartin, Dierker-
dc.contributor.advisor마틴, 디어커-
dc.contributor.authorPark, Jiwoo-
dc.date.accessioned2021-05-13T19:35:14Z-
dc.date.available2021-05-13T19:35:14Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=911495&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/284825-
dc.description학위논문(석사) - 한국과학기술원 : 경영공학부, 2020.2,[iii, 16 p. :]-
dc.description.abstractThis paper proposes a numerical method based on reinforcement learning that can value of option. In the celebrated option pricing model, Black Scholes, it assumes continuous hedging and zero transaction costs during hedging. This paper modifies these assumption as follows to make these assumptions as real as possible. 1) Non-zero transaction costs, 2) Discretized interval of re-hedging of an option replicating portfolio. With new methodology, this paper executed numerical experiments and examine the effects of the modified assumptions. Also, this research compares the results of the experiment with original researches of option pricing, such as Black Scholes Model and Leland Model.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectOption▼aTransaction Costs▼aSimulation-
dc.subject옵션-
dc.subject거래비용-
dc.subject시뮬레이션-
dc.titleApplication of reinforcement learning in valuing option-
dc.title.alternative강화학습을 활용한 옵션 가치 평가-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :경영공학부,-
dc.contributor.alternativeauthor박지우-
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