文献类型：期刊文献

英文题名：The impact of the Russia-Ukraine conflict on the extreme risk spillovers between agricultural futures and spots

作者：Zhou, Wei-Xing[1,2,3]; Dai, Yun-Shi[1]; Duong, Kiet Tuan[4]; Dai, Peng-Fei[5,6]

机构：[1] School of Business, East China University of Science and Technology, Shanghai, 200237, China; [2] Research Center for Econophysics, East China University of Science and Technology, Shanghai, 200237, China; [3] School of Mathematics, East China University of Science and Technology, Shanghai, 200237, China; [4] School for Business and Society, University of York, York, YO10 5ZF, United Kingdom; [5] School of Management, Wuhan University of Technology, Wuhan, 430070, China; [6] Research Institute of Digital Governance and Management Decision Innovation, Wuhan University of Technology, Wuhan, 430070, China

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230398366)

语种：英文

外文关键词：Agriculture - Commerce

摘要：The ongoing Russia-Ukraine conflict between two major agricultural powers has posed significant threats and challenges to the global food system and world food security. Focusing on the impact of the conflict on the global agricultural market, we propose a new analytical framework for tail dependence, and combine the Copula-CoVaR method with the ARMA-GARCH-skewed Student-t model to examine the tail dependence structure and extreme risk spillover between agricultural futures and spots over the pre- and post-outbreak periods. Our results indicate that the tail dependence structures in the futures-spot markets of soybean, maize, wheat, and rice have all reacted to the Russia-Ukraine conflict. Furthermore, the outbreak of the conflict has intensified risks of the four agricultural markets in varying degrees, with the wheat market being affected the most. Additionally, all the agricultural futures markets exhibit significant downside and upside risk spillovers to their corresponding spot markets before and after the outbreak of the conflict, whereas the strengths of these extreme risk spillover effects demonstrate significant asymmetries at the directional (downside versus upside) and temporal (pre-outbreak versus post-outbreak) levels. Copyright ? 2023, The Authors. All rights reserved.