This paper aims to analyze the dynamic relationships between spot and futures prices of Chinese agricultural markets by considering different time scales and market conditions and explore the predictive ability of these futures products.
This paper proposes a new ensemble empirical mode decomposition (EEMD)-based quantile framework that integrates EEMD, quantile-on-quantile (QQ) regression and causality in quantiles (CQ) methods to analyze the dynamic relationships between the spot and futures prices of corn, soybean oil, cotton and live hogs.
Results show that QQ coefficients in cotton, live hog and soybean oil markets are significantly positive, indicating robust price dependence and hedging properties. In most cases, QQ coefficients in corn markets are close to 0, suggesting a weak hedging property. Furthermore, the dependence structure of spots and futures are more robust in average markets than in bullish and bearish markets and significantly more robust in the medium and long term than in the short term. The CQ results show the predictability of these four futures and further support the QQ conclusions. Futures are likely to be more predictive under average market conditions and this predictability is more significant in the medium and long term.
Relative to previous studies on a single time scale and market condition, our study provides new insights for investors seeking to better manage risk in agricultural commodity markets. The agricultural futures examined are suitable for hedging in certain market conditions for long-term investors with sufficient risk tolerance.
