Analysis of the efficiency of the Shanghai stock market: A volatility perspective

By applying the rolling window method, we investigate the efficiency of the Shanghai stock market through the dynamic changes of local Hurst exponents based on multifractal detrended fluctuation analysis. We decompose the realized volatility into continuous sample paths and jump components and analyze their long-range correlations of decomposing components. Our results reveal that the efficiency of the Shanghai stock market improved greatly based on the time-varying Hurst exponents.

[1]  Yan Wang,et al.  The properties and mechanism of long-term memory in nonparametric volatility , 2010 .

[2]  Rongbao Gu,et al.  Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis , 2009 .

[3]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[4]  Wei‐Xing Zhou,et al.  Scaling and memory in the return intervals of realized volatility , 2009, 0904.1107.

[5]  N. Shephard,et al.  Power and bipower variation with stochastic volatility and jumps , 2003 .

[6]  B. M. Tabak,et al.  The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient , 2004 .

[7]  Yu Wei,et al.  Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective , 2010 .

[8]  Jose Alvarez-Ramirez,et al.  Short-term predictability of crude oil markets: A detrended fluctuation analysis approach , 2008 .

[9]  Li Liu,et al.  Analysis of market efficiency for the Shanghai stock market over time , 2010 .

[10]  R. Cont.  Empirical properties of asset returns: stylized facts and statistical issues , 2001 .

[11]  Ivo Grosse,et al.  ARCH–GARCH approaches to modeling high-frequency financial data , 2004 .

[12]  Fulvio Corsi,et al.  A Simple Approximate Long-Memory Model of Realized Volatility , 2008 .

[13]  Alejandra Figliola,et al.  A multifractal approach for stock market inefficiency , 2008 .

[14]  D. Dijk,et al.  Measuring volatility with the realized range , 2006 .

[15]  Markku Lanne,et al.  Forecasting realized exchange rate volatility by decomposition , 2007 .

[16]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[17]  J. Álvarez-Ramírez,et al.  Time-varying Hurst exponent for US stock markets , 2008 .

[18]  Kim Christensen,et al.  Realized Range-Based Estimation of Integrated Variance , 2006 .

[19]  George Tauchen,et al.  Cross-Stock Comparisons of the Relative Contribution of Jumps to Total Price Variance , 2012 .

[20]  F. Diebold,et al.  Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility , 2005, The Review of Economics and Statistics.

[21]  A. Lo.  Long-Term Memory in Stock Market Prices , 1989 .