Multivariate correlation analysis of agricultural futures and spot markets based on multifractal statistical methods

Abstract

Agricultural commodity futures are the earliest listed futures in the world. The rapid development of their markets has greatly affected the world agricultural production and circulation. Thereby, the volatility characteristics of agricultural futures markets and the cross-correlations between the futures and spot markets have attracted extensive attention from investors, regulators and researchers. Two new fractal statistical methods are firstly developed in this paper. Then, from the perspective of system theory, some multifractal analysis methods, including the two newly developed methods, are used to study the autocorrelation, cross-correlation and coupling correlation for the return series of five agricultural futures and their corresponding spot varieties in CBOT. The empirical results show that the autocorrelations and cross-correlations of both the futures and spot systems are multifractal and present different dynamic fluctuation characteristics at different time scales. The long-term co-movement between the two systems is not strong and the overall risk spillover effect is not obvious. In addition, the coupling cross-correlations are found between the corresponding components, but the strengths are distinct due to the different influences of long-range correlations and fat-tailed distributions.

A mixed multifractal analysis for quasi Ahlfors vector-valued measures

“…The MVMA shows that image intensity may be subject to fluctuated regularity. In [66], MVMA has been applied for characterizing volatility and cross-correlation for agricultural futures' markets. Several statistical measures such as autocorrelation, coupling correlation as well as cross-correlation for some Chicago board of trade series have been estimated via the MVMA to decide about the state of markets.…”

Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

“…In his empirical analysis of the employment situation Ruolei selected the data of the individual employment number of private enterprises in the region, used IBM SPSS to cluster the individual employment number of private enterprises in 31 provinces and cities, and then used the discrimination method to verify the clustering results. Simple statistics and research on the number of private enterprises and individual employed in different regions of the country, and find some characteristics of the number of people employed in different industries in different regions from the statistical perspective, so as to understand the distribution of resources in different regions in China [1].…”