- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
The stock, bond and fund markets are three important components of a financial market, and the volatility of the markets and correlations between the markets have been paid extensive attention by researchers and investors. In this paper, we devote our efforts to studying the Shanghai financial market, while the return series of Shanghai Composite Index, Shanghai Bond Index and Shanghai Fund Index are considered. Statistical tests are used to detect the nonlinear auto-correlated structures and long-range cross-correlations of the three time series. The multifractal detrended fluctuation analysis and multifractal spectrum analysis methods are applied, by which the existence of multifractality in these three return series are revealed and the sources of multifractality are explored. In particular, the multiscale multifractal detrended cross-correlation analysis method is employed for the first time to generate the Hurst surfaces, which can be used to visualize the dynamic behaviors of cross-correlations among the markets. Empirical results show that the cross-correlations among the markets present different fractal features at different time scales. Further, our study finds that the correlation between the stock and fund markets is stronger than that of the other two groups, and the correlation between the stock and bond markets is unstable. These findings can help to better understand the dynamic mechanisms that govern the volatility of security markets and aid in performing better financial risk assessment and management.
In this paper, multifractal analysis methods were mainly applied to study the multifractal properties of China’s stock, bond and fund markets. Different from some published papers, such as references [1–3,6–9], this paper studied the correlations among three financial markets instead of two, and focused mainly on their multifractal auto-correlations and cross-correlations, and emphasized more on different dynamic behaviors of the three markets at different time scales. Our findings can be summarized as follows. Firstly, the descriptive statistics showed that the return series of the three markets disobey the Gaussian distribution. That is, the distributions of the three return series are non-normal due to their sharp peaks and fat-tailed statistical features. Moreover, the Ljung–Box and cross-correlation tests indicate that there exist nonlinear auto-correlated structures and longrange cross-correlations in the three series. Then, the multifractal characteristics of three return series were analyzed by using the MF-DFA and multifractal spectrum analysis methods. The results showed that the auto-correlations of the three return series have multifractality. In addition, the sources of multifractality were explored. We found that both the long-range correlations and fat-tailed distributions are common causes for the multifractality, and fat-tailed distributions have major effect on the multifractality.