We investigate the dynamic evolution of the price discovery function in Chinese agricultural futures markets using a newly developed rolling window cointegration approach. The results show that, compared with wheat and rice, the futures-spot cointegration relationship in the soybean and corn markets tends to be more durable and frequent. Dynamic cointegration analysis indicates that the recent market-oriented reforms in China have boosted the price discovery function of soybean and corn futures markets, whereas price stabilization policies tend to weaken the price discovery function of futures markets. The difference in price discovery function is attributed to differences in market mechanisms and Chinese agricultural policies.

This study examines the price impact of intraday trading activity and daily market liquidity of Chinese agricultural futures by analyzing continuous intraday 15-min and daily trading datasets, respectively. Corn and soybean, the necessity of the nation and people’s survival in China, are taken as case studies. Our main findings are threefold. Firstly, there is evidence of the presence of informed trading through persistent effects of trade size for both purchases and sales. The magnitude of effects and the seasonality of informed trading vary among varieties, which support the importance of night trading for price smoothing. Secondly, the impact of liquidity costs on returns does not permanently persist. For example, there appears a significant Friday effect with a linear negative relationship in the soybean market, while an exact opposite effect can be found in the corn market for Monday. Thirdly, while the results show no effect of holding position on asset returns in the corn market, the market size of soybean futures exerts a positive Thursday effect, which is prior to the Friday effect of transaction cost. A better understanding of liquidity costs and liquidity pricing is of great significance to a sustainable development of the agricultural commodity market in China.

PurposeThis paper aims to present the first empirical liquidity measurement of China’s agricultural futures markets and study time-varying liquidity dependence across markets.Design/methodology/approachBased on both high- and low-frequency trading data of soybean and corn, this paper evaluates short-term liquidity adjustment in Chinese agricultural futures market measured by liquidity benchmark and long-term liquidity development measured by liquidity proxies.FindingsBy constructing comparisons, the authors identify the seminal paper of Fong, Holden and Trzcinka (2017) as the best low-frequency liquidity proxy in China’s agricultural futures market and capture similar historical patterns of the liquidity in soybean and corn markets. The authors further employ Copula-generalized autoregressive conditional heteroskedasticity models to investigate liquidity dependence between soybean and corn futures markets. Results show that cross-market liquidity dependence tends to be dynamic and asymmetric (in upper versus lower tails). The liquidity dependence becomes stronger when these markets experience negative shocks than positive shocks, indicating a concern on the contagion effect of liquidity risk under negative financial situations.Originality/valueThe findings of this study provide useful information on the dynamic evolution of liquidity pattern and cross-market dependence of fastest-growing agricultural futures in the largest emerging economy.

This study examines the impact of international soybean price and energy price on Chinese soybean price. Applied to monthly data over the period of 2007-2017, results show that both international soybean price and energy price have significant impacts on Chinese soybean price, while the impact from global soybean market tends to be more profound. First, we find that in the long run the cumulative pass-through elasticity of Chinese soybean price to international soybean price is greater than the elasticity to international energy price. Second, in the short run, international soybean price shocks transmit more quickly to Chinese soybean price. Our results shed new light on the determinants of soybean price volatility in China, and provide meaningful implications on the price risk management for market participants and policy makers.