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

Abyankar, A., Copeland, Laurence Sidney and Wong, Woon K. ORCID: https://orcid.org/0000-0001-6892-9965 1997. Uncovering nonlinear structure in real-time stock-market indexes: the S&P 500, the DAX, the Nikkei 225, and the FTSE-100. Journal of Business & Economic Statistics 15 (1) , pp. 1-14. 10.1080/07350015.1997.10524681

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > HF Commerce H Social Sciences > HG Finance
Uncontrolled Keywords:	Brock–Dechert–Scheinkman test, Chaos, GARCH models, Lyapunov exponent, Nearest-neighbor method, Neural net, Nonparametric, Stock index futures, Stock returns
Publisher:	Taylor & Francis
ISSN:	0735-0015
Last Modified:	24 Oct 2022 11:44
URI:	https://orca.cardiff.ac.uk/id/eprint/49181

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