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Uncovering Nonlinear Structure In Real-Time Stock Market Indices

Posted: 24 Oct 1999  

Abhay Abhyankar

University of Exeter Business School, University of Exeter

Laurence Copeland

Cardiff University - Cardiff Business School

W. Wong

University of Stirling

Abstract

This paper tests for the presence of nonlinear dependence and chaos in real-time returns on four of the world's major stock market indices: the FTSE-100, the S&P 500, the Nikkei 225 and the DAX. Our results suggest that GARCH-type models can explain some but not all of the observed nonlinear dependence. The neural network-based test for nonlinearity introduced by Lee, White and Granger (1993) provides conclusive evidence of a persistent nonlinear structure in the series. We also estimate Lyapunov exponents in order to test directly for chaos using both the Nychka, Ellner, Gallant, McCaffrey (1992) and the Zeng, Pielke and Eyckholt (1992) methods. We find that none of the series seem to be characterised by a low-dimensional chaotic process. Instead, the Lyapunov exponent estimates appear to be extremely sensitive to the parameter values used in estimation, a fact which in itself may be an indication that the data are dominated by a stochastic component.

JEL Classification: C22, G12

Suggested Citation

Abhyankar, Abhay and Copeland, Laurence and Wong, W., Uncovering Nonlinear Structure In Real-Time Stock Market Indices. Available at SSRN: https://ssrn.com/abstract=6092

Abhay Abhyankar (Contact Author)

University of Exeter Business School, University of Exeter ( email )

Streatham Court
Exeter, EX4 4PU
United Kingdom

Laurence S. Copeland

Cardiff University - Cardiff Business School ( email )

Aberconway Building
Colum Drive
Cardiff, CF10 3EU
United Kingdom
+44 29 20875740 (Phone)
+44 29 20874419 (Fax)

W. Wong

University of Stirling ( email )

Stirling, Scotland FK9 4LA
United Kingdom
Not available (Phone)
Not available (Fax)

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