揭示中美动态风险溢出效应：来自农产品期货市场的证据
Uncovering the Sino-US dynamic risk spillovers effects: Evidence from agricultural futures markets
AI总结：
本文运用TVP-VAR-DY模型与分位数方法研究中美11种农产品期货的风险溢出，发现CBOT玉米大豆等为主要风险传递者，极端情况下CZCE硬麦和长粒稻成风险中心，为决策和投资提供依据。
摘要：
农产品在人类发展中扮演关键角色。随着经济全球化与农产品金融化不断推进，不同农产品期货间的联系日益紧密。我们运用TVP-VAR-DY模型结合分位数方法，测度了2014年7月9日至2022年12月31日期间中美期货交易所11种农产品期货间的风险溢出。本研究得出几项重要发现。首先，CBOT玉米、大豆和小麦被识别为主要风险传递者，而DCE玉米和大豆是主要风险接收者。其次，突发事件或经济不确定性增加会提升整体风险溢出。第三，基于动态定向溢出结果，农产品期货间的风险溢出存在聚集性。最后，条件均值下的核心农产品期货为CBOT玉米和大豆，而根据溢出网络和最小生成树结果，在极端情况下CZCE硬麦和长粒稻是两个风险溢出中心。基于这些结果，建议决策者防范突发事件下农产品期货的价格风险，投资者也可利用该结果，根据不同情况将不同农产品期货作为风险领先指标，从而构建更优的投资组合。
英文摘要：
Agricultural products play a critical role in human development. With economic globalization and the financialization of agricultural products continuing to advance, the interconnections between different agricultural futures have become closer. We utilize a TVP-VAR-DY model combined with the quantile method to measure the risk spillover between 11 agricultural futures on the futures exchanges of US and China from July 9,2014, to December 31,2022. This study yielded several significant findings. Firstly, CBOT corn, soybean, and wheat were identified as the primary risk transmitters, with DCE corn and soybean as the main risk receivers. Secondly, sudden events or increased economic uncertainty can increase the overall risk spillovers. Thirdly, there is an aggregation of risk spillovers amongst agricultural futures based on the dynamic directional spillover results. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean, while CZCE hard wheat and long-grained rice are the two risk spillover centers in extreme cases, as per the results of the spillover network and minimum spanning tree. Based on these results, decision-makers are advised to safeguard against the price risk of agricultural futures under sudden economic events, and investors can utilize the results to construct a superior investment portfolio by taking different agricultural product futures as risk-leading indicators according to various situations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
