This paper proposes a long memory analysis based on wavelet transform of financial data. This method treats return series and volatility series in the stock market as a fractional differenced noise process, and analyz...This paper proposes a long memory analysis based on wavelet transform of financial data. This method treats return series and volatility series in the stock market as a fractional differenced noise process, and analyzes it by MODWT（maximal overlap discrete wavelet transform）. The result shows there is a lineal relationship between wavelet variance logarithm and scale logarithm, so a long memory parameter can be obtained by using the relationship. This method is proved to be effective and feasible by analyzing the return series and volatility series of composite indexes of Shanghai and Shenzhen stock market.展开更多
文摘This paper proposes a long memory analysis based on wavelet transform of financial data. This method treats return series and volatility series in the stock market as a fractional differenced noise process, and analyzes it by MODWT（maximal overlap discrete wavelet transform）. The result shows there is a lineal relationship between wavelet variance logarithm and scale logarithm, so a long memory parameter can be obtained by using the relationship. This method is proved to be effective and feasible by analyzing the return series and volatility series of composite indexes of Shanghai and Shenzhen stock market.
