Recurrence Quantification Analysis of Wavelet Pre-Filtered Index Returns
University of Durham Economics and Finance Working Paper
Posted: 28 Jul 2003
Date Written: June 10, 2003
We investigate the issue of deterministic vs. stochastic dynamics in financial time series. We demonstrate a way to to reveal nonstochastic dynamical structures in daily stock market index returns, combining Recurrence Quantification Analysis (RQA) and wavelet filtering. Assuming a dynamical system generating the returns sequences, we reproduce its dynamics from the data with minimum assumptions. We reconstruct the phase-space dynamics by time-delay embedding of the wavelet denoised returns, in order to apply the RQA. The results indicate that through wavelet pre-filtering we can obtain very "clean" sequences and reveal nonstochastic dynamics. Our results also suggest the existence of chaos. We provide both quantitative and qualitative evidence supporting our findings.
Keywords: Recurrence plots, Recurrence Quantification Analysis, Wavelets, Financial time-series analysis, Chaos
JEL Classification: G14, G15, C29, Z00
Suggested Citation: Suggested Citation