Effect of Weather on Agricultural Futures Markets on the Basis of DCCA Cross-Correlation Coefficient Analysis

Abstract

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.

Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis

“…Finally, we summarize and draw some general conclusions. In view of the abovementioned interdisciplinary research by other authors [22,24,25,[27][28][29][30], through the conclusions from our present work, we would like also to support advantages of multifractal detrended cross-correlation method and its wide applications to study any time series with nonlinear correlations, not only in the foreign exchange market but also across other fields of pure and applied sciences.…”

“…We would like to emphasize that our method based on detrended cross-correlation analysis is quite novel and only recently a plethora of applications started to emerge across many fields of nonlinear correlations studies, including meteorological data [22], electricity spot market [23], effects of weather on agricultural market [24], stock markets [25], cryptocurrency markets [26], electroencephalography (EEG) signals [27], electrocardiography (ECG) and arterial blood pressure [28] as well as air pollution [29,30]. Such wide interest across different fields of research in application of detrended cross-correlation analysis to nonlinear time series studies serves as an additional strong motivation for elucidating such analysis in terms of its potential and limitations.…”

Stock market efficiency: An intraday case of study about the G-20 group

“…Then, we generalized the DFA algorithm (six steps described above in DFA algorithm) to find (covariance of the residuals) and the detrended function by: But, for quantify the level of cross-correlation, the DCCA cross-correlation coefficient can defined as the ratio between the detrended cross-correlation function, , and the detrended auto-correlation function, and , for the time-series and , respectively: Some properties of naturally appear, the most important is that: In this case, means there is no cross-correlation between and , and it splits the level of cross-correlation between positive and the negative case. This coefficient has been tested on selected time-series [21] , [42] and proved to be quite robust, mainly for statistical analysis between non-stationary time-series [43] , [44] , [45] , [46] , [47] , [48] , among other cases. It is noteworthy that there is the generalization, for more than two time-series analysis, what we call multiple DCCA coefficient, …”

Dynamic Correlation between Weather Derivatives and Agricultural Commodity Derivatives: Evidence from the US

“…Weather futures, as one of the newest and fastest-growing kinds of derivatives, enable businesses to safeguard themselves against losses brought on by unforeseen changes in weather conditions. Non-catastrophic weather risk is becoming increasingly significant as climate change becomes more obvious and the economic downturn compels businesses to tighten their cost controls (Cao et al, 2016). Numerous factors including macroeconomic factors, supply and demand factors, and natural elements, have an impact on the markets for agricultural product futures.…”