Time-dependent Hurst Exponent in Financial Time Series in China Financial Market

摘要：

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 timevariability 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.

关键词： Time-series analysis Hurst exponent DMA DFA

作者: Guozhi Wang

作者单位: Research Center of Financial Engineering South China University of Technology Guangzhou China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 87-89

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）