Uncovering Nonlinear Structure in Real-Time Stock-Market Indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100

A Abhyankar, Laurence Copeland () and W Wong
Authors registered in the RePEc Author Service: Wing-Keung Wong and Woon K. Wong ()

Journal of Business & Economic Statistics, 1997, vol. 15, issue 1, 1-14

Abstract: This article tests for nonlinear dependence and chaos in real-time returns on the world's four most important stock-market indexes. Both 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, the authors conclude that the data are dominated by a stochastic component.

Date: 1997
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Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:15:y:1997:i:1:p:1-14

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