(A) statistical linear prediction in stock price process = 주가 변동과정에 있어서 통계적 선형예측

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In viewing stock price process mathematically we need a model. The commonest mathematical model for stock price process is nonlinear stochastic Volterra integral equation. But differently from the linear case, nonlinear stochastic integral equations have few chances to obtain exact solutions. Hence for application it is general that prediction is made by approximating through numerical methods such as binomial scheme, finite difference method, Euler type iteration, Monte Carlo simulation, etc. In this thesis, we obtained the higher rate of convergence than the classical Euler type iteration method by linear prediction using statistical interpolation method.
Advisors
Lee, Sung-YunresearcherChoi, U-Jinresearcher이성연researcher최우진researcher
Description
한국과학기술원 : 수학전공,
Publisher
한국과학기술원
Issue Date
2001
Identifier
166242/325007 / 000993467
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 수학전공, 2001.2, [ vi, 23 p. ]

Keywords

stochastic; 주가 변동과정

URI
http://hdl.handle.net/10203/42024
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=166242&flag=dissertation
Appears in Collection
MA-Theses_Master(석사논문)
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