Research On The Dynamic Correlation Of Asset Prices In Securities Markets

Financial markets are large scale complex systems with many-body interactions. The price fluctuation correlation in Securities market dynamics is always an important topic in econophysics. Currently, multivariate statistical methods as the traditional methods were used to study the fluctuation correlation among the limited stocks. However, every two stocks in financial markets are related. Based on this, I analyze the stock networks to reveal the dynamic fluctuation correlation of stock prcies through the introduction of complex network theory in this paper. Meanwhile, the way on the volatility spillover effects is also an important research direction in the volatility of the securities markets. In this paper, I will use the empirical mode decomposition method to analyze the long-term dynamic spillover effects through the phase relationship between Shanghai-Shenzhen stock markets. As a result, several productions of good theoretical values and contribution are as follows.(1) In this work, employing a moving window to scan through every stock price time series over a period from2January2001to7December2010, mutual information is used to measure the statistical interdependence between stock prices, and construct2000corresponding networks for501Shanghai stocks in every given window based on the complex network theory.(2) Based on the constructed networks, the variation of average degree, average cluster coefficient, power-law exponents,fitting error, and p-value over time is analyzed. The results obtained here indicate that the periods around Jul.1,2005and Oct.16,2007are turn points of Shanghai market, at the turn point the scalefreeness of the degree distribution for stock network is disrupted.(3) The timevarying relationships between the structure variation and fluctuations for the Shanghai stock market addressed. All the results obtained here indicate that the periods around1July2005,16October2007, and1December2008are turning points of the Shanghai market; and at turning points the growing independence of stocks causes the scalefreeness of the degree distribution to be disrupted, and that the Shanghai stock index has little volatility clustering. In contrast, under normality of the market, the stock networks have characteristics of scalefree degree distribution. Furthermore, the degree of volatility clustering is a little higher. In a sence scalefree-like structure is an indicator of normality.(4) Unlike the commonly used statistical test methods, selecting stock closing price time series over a period from30December1991to8October2007from Shanghai Composite Index and Shenzhen Composite Index, four trend components of return and volatility are obtained by the method of Empirical Mode Decomposition. Then the return’s or volatility’s time-varying characteristics of phase relationship for Shanghai-Shenzhen stock markets is generalized by Hilbert method.Based on the return’s or volatility’s time-varying characteristics of phase relationship for Shanghai-Shenzhen stock markets, long-term dynamic spillover effects between them are visually demonstrated. The results show that2001was a turning point year for China’s stock market.This paper’s research methodology and conclusion will help people to deepen the understanding of the Chinese stock market, providing an effective reference to the market macroscopic problem and investment portfolio. All these can reduce losses and maintain long-term stable and healthy development of China’s national economy.