This study investigates the correlation between weather and agricultural futures markets on the basis of detrended cross-correlation analysis (DCCA) cross-correlation coefficients and [Formula: see text]-dependent cross-correlation coefficients. In addition, detrended fluctuation analysis (DFA) is used to measure extreme weather and thus analyze further the effect of this condition on agricultural futures markets. Cross-correlation exists between weather and agricultural futures markets on certain time scales. There are some correlations between temperature and soybean return associated with medium amplitudes. Under extreme weather conditions, weather exerts different influences on different agricultural products; for instance, soybean return is greatly influenced by temperature, and weather variables exhibit no effect on corn return. Based on the detrending moving-average cross-correlation analysis (DMCA) coefficient and DFA regression results are similar to that of DCCA coefficient.
In this paper, we proposed multiscale cross-recurrence quantification analysis (MSCRQA) method to analyze the dynamic states of two time series at different time scales. We apply this method to model system (two coupled van der Pol oscillators) and real-world system (SSEC and SZSE). It demonstrates that the MSCRQA can show richer and more recognizable information compared with single time scale. The state of dynamics is different under different time scales. MSCRQA method shows another multiscale perspective to fully mine more hidden internal dynamic information of a time series. This method may provide another method reference for practical application to better explore the laws of the real world.
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