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.
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Volume (Year): 15 (1997) Issue (Month): 1 (January) Pages: 1-14 Download reference. The following formats are available: HTML
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