Characteristics Of China's Economic Operation, Based On Wavelet Analysis

Wavelet analysis theory, as a new mathematical theory and methods has been applied to various fields of study. In recent years, scholars at home and abroad applied to wavelet analysis in economics, which includes aspects of finance, macroeconomics, the economic time series analysis from a simple phase field extends to a common phase and frequency domain analysis. The wavelet analysis used in economics abroad began in the mid-20th century, the wavelet analysis used in China economics began in the recent 20 years.The first chapter, Wavelet significance of economic research in China, introduced the main contents of this paper, this paper uses the wavelet method, GARCH models, ARFIMA models, ARMA models, and parameters, semi-parametric, nonparametric estimation on China's GDP volatility, stock and bond market, futures market, were studied.The second chapter, wavelet analysis and economic theory research summary, first reviewed the foreign economic research with wavelet various economic theories and empirical test, then, reviewed the domestic economy of the application of wavelet analysis, specifically including wavelets the application of the economic cycle, the application of wavelets in the stock market, wavelet application in the futures market, and then review the wavelet neural network in China's domestic economic research methods and related theories of wavelet method results and other economic areas in China application.The third chapter introduces the basic principles of wavelet theory, including the wavelet transform, wavelet reconstruction, wavelet packet transform, long memory process analysis, spectrum analysis, wavelet variance analysis, wavelet neural networks, and their theoretical links with the economic analysis, economic research and so on.Chapter IV is to use wavelet method on our gross domestic product analysis and simulation, using a single wavelet transform, spectral analysis, long memory process simulation and other methods, combined with traditional methods of economic series, GARCH model. China's economic fluctuations are mainly found in three cycles of composition, the strong growth tendency in China's economic future will not be reduced.Chapter V of the stock market and investment markets were studied. First, the stock market has been carved-scale behavior of the evidence, and then a few of the stock market, stocks were on the long memory test and found that the Shanghai index consistent with geometric Brownian motion, and several other stocks in the long memory parameter is very small In a few selected stocks, the Baotou Rare Earth's long memory parameter the largest fluctuation of the stock provided the basis for the simulation. Secondly, the wavelet in the investment market rate of return of some funds were smoothed and threshold analysis, and selection of several stock funds established AR-GARCH model, has been China's investment market fund yields fluctuate principle. Wavelet neural network security in China stock market has been studied and found that the overall security situation of the stock market spiral. Through the wavelet neural network combined with the clustering method, on the confidence of investors in the stock market (market sentiment) the impact on the stock market and found that the market psychology is an important force in the decision to market fluctuations and investment by the stock trading provides a theoretical method.Chapterâ…¥with the wavelet method of analysis of commodity futures markets and found that China's futures market on the long memory of the financial data is very significant, got the estimation of the long memory parameters of the futures market, but also with a Markov regime switching model inspect long memory process, the estimated high and low status, reducing the volatility of long-term commodity; convenience yield is the impact of commodity futures prices composition's main factor. Remove the noise wavelet method, using two-stage model of c estimate 1-2 factor models.Chapterâ…¦is about the latest wavelet theory-lifting wavelet theory, and with the lifting wavelet theory, got China GDP's trends and volatility decomposition, combined with empirical evidence that the lifting wavelet is much greater in flexibility and selectivity comparing with traditional wavelet.Innovation of this paper are using the GSSM method and GARCH(1,1) model of GDP in China, stock sequences were analyzed by wavelet neural network self-organization, found that psychology factors leaven China's stock market, propose a new method to decide when to buy and when to sail. Convenience yield is found to be the futures prices" one of the main factors.