淡江大學機構典藏:Item 987654321/72276

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    题名: Uncovering Nonlinear Structure in Real-Time Stock Market Indices
    作者: 黃文光;Abyankar;Copeland
    贡献者: 淡江大學財務金融學系
    日期: 1997-01-01
    上传时间: 2011-10-24 10:19:34 (UTC+8)
    摘要: This article tests for nonlinear dependence and chaos in real-time returns on the world's four most important stock-market indexes. Both the Brock–Dechert–Scheinkman and the Lee, White, and Granger neural-network-based tests indicate persistent nonlinear structure in the series. Estimates of the Lyapunov exponents using the Nychka, Ellner, Gallant, and McCaffrey neural-net method and the Zeng, Pielke, and Eyckholt nearest-neighbor algorithm confirm the presence of nonlinear dependence in the returns on all indexes but provide no evidence of low-dimensional chaotic processes. Given the sensitivity of the results to the estimation parameters, we conclude that the data are dominated by a stochastic component.
    關聯: Journal of Business, Economics and Statistics 15, pp.14
    DOI: 10.1080/07350015.1997.10524681
    显示于类别:[財務金融學系暨研究所] 期刊論文

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