A Comprehensive Framework for Uncovering Non-Linearity and Chaos in Financial Markets: Empirical Evidence for Four Major Stock Market Indices

• Published in: Entropy (Basel, Switzerland) Vol. 22; no. 12; p. 1435
• Main Author: Inglada-Perez, Lucia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.12.2020; MDPI
• Subjects: chaos; Chaos theory; Efficient markets; Empirical analysis; Linearity; Lyapunov; Methods; Noise; nonlinear dynamics; Nonlinearity; recurrence plots; Securities markets; Stochastic models; stock exchange market; Stock exchanges; Time series; time series analysis; Volatility; BDS; time series analysis; recurrence plots; chaos; correlation dimension; stock exchange market; GARCH model; nonlinear dynamics; Lyapunov
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e22121435

The presence of chaos in the financial markets has been the subject of a great number of studies, but the results have been contradictory and inconclusive. This research tests for the existence of nonlinear patterns and chaotic nature in four major stock market indices: namely Dow Jones Industrial Average, Ibex 35, Nasdaq-100 and Nikkei 225. To this end, a comprehensive framework has been adopted encompassing a wide range of techniques and the most suitable methods for the analysis of noisy time series. By using daily closing values from January 1992 to July 2013, this study employs twelve techniques and tools of which five are specific to detecting chaos. The findings show no clear evidence of chaos, suggesting that the behavior of financial markets is nonlinear and stochastic.