Uncovering nonlinear structure in real-time stock-market indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100

被引:114
作者
Abhyankar, A [1 ]
Copeland, LS [1 ]
Wong, W [1 ]
机构
[1] UNIV WALES COLL CARDIFF,CARDIFF BUSINESS SCH,CARDIFF CF1 3NS,S GLAM,WALES
关键词
Brock-Dechert-Scheinkman test; chaos; GARCH models; Lyapunov exponent; nearest-neighbor method; neural net; nonparametric; stock index futures; stock returns;
D O I
10.2307/1392068
中图分类号
F [经济];
学科分类号
02 ;
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.
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页码:1 / 14
页数:14
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