Notice of RetractionTime-dependent Hurst exponent in financial time series in China financial market

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

We calculate the Hurst exponent H(t) of several time series by dynamical implementation of a scaling technique: the detrending moving average (DMA). In order to assess the accuracy of the technique, we calculate the exponent H(t) for artificial series, simulating monofractal Brownian paths, with assigned Hurst exponents H. We next calculate the exponent H(t) for the return of high-frequency (tick-by-tick sampled every minute) series of the Shanghai stock market. We find a much more pronounced time-variability in the local scaling exponent of financial series compared to the artificial ones. The DMA algorithm allows the calculation of the exponent H(t), without any a priori assumption on the stochastic process and on the probability distribution function of the random variables, as happens, for example, in the case of the Kitagawa grid and the extended Kalmann filtering methods. The present technique examines the local scaling exponent H(t) around a given instant of time. This is a significant advance with respect to the standard wavelet transform or to the higher-order power spectrum technique, which instead operate on the global properties of the series by Legendre or Fourier transform of qth-order moments.