Based On Wavelet Analysis Of Stock Price Index Volatility

Recently years, wavelet analysis has begun to be used in the field of economy and finance to deal with the data of time series. This paper mainly study the Shanghai stock market, selected January 5,1998 to December 31,2009 to 2901 trading days closing price data and the January 6,1998 to December 31,2009 to 2900 a daily return data, using wavelet transform to analyze the stock market volatility. The contents and viewpoints are summarized as follows:Content of this paper:This paper begins by the introduction of the research background, the practical significance of the research and the research status at home and abroad; continues by the description of the wavelet analysis theory associated with this topic; then, carries out the wavelet transform, multiresolution analysis, singularity detection and the similarity detection to analyze the stock index and stock return; after that, uses the decomposed signals to conduct the denoising reconstruction processing; finally, estimate the reconstructed signals.Conclusion of this paper:Empirical analysis shows that the signals have better singularity detection and the similarity detection; the similarity of stock index has a volatility clustering and the volatility of past effect the future fluctuations; the denoising reconstruction signals have better stability and trend.