{"title":"Risk Spillover Network in Commodity Markets Under Climate Transition Risk","authors":"Zhihong Niu, Yan Wang","doi":"10.1002/for.70072","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>The article constructs a connectivity network based on the dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model and spillover index method, systematically revealing the risk contagion effects within China's commodity markets under the context of climate transition. In the long run, the energy and chemical markets act as risk spillover transmitters in the commodity system, while the metal and agricultural markets function as risk receivers. Gold typically passively absorbs risk spillovers from other markets during risk events, presenting a structure of “receiving more and spilling less.” However, in the short term, the risk roles of markets undergo shifts in response to changes in macroeconomic conditions. The risk roles of different markets exhibit significant time-varying characteristics, and the network structure experiences dynamic reconstruction under the impact of specific events. In terms of policy recommendations, for spillover industries, it is crucial to promptly identify potential systemic risk sources to prevent high-carbon industries from generating transition risks. For risk-receiving industries, their risk-mitigating role during market turbulence should be fully utilized. Additionally, attention should be given to the potential risks associated with the transformation of risk roles during extreme events or changes in macroeconomic policies.</p>\n </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"1036-1051"},"PeriodicalIF":2.6000,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/for.70072","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/12/1 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
The article constructs a connectivity network based on the dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model and spillover index method, systematically revealing the risk contagion effects within China's commodity markets under the context of climate transition. In the long run, the energy and chemical markets act as risk spillover transmitters in the commodity system, while the metal and agricultural markets function as risk receivers. Gold typically passively absorbs risk spillovers from other markets during risk events, presenting a structure of “receiving more and spilling less.” However, in the short term, the risk roles of markets undergo shifts in response to changes in macroeconomic conditions. The risk roles of different markets exhibit significant time-varying characteristics, and the network structure experiences dynamic reconstruction under the impact of specific events. In terms of policy recommendations, for spillover industries, it is crucial to promptly identify potential systemic risk sources to prevent high-carbon industries from generating transition risks. For risk-receiving industries, their risk-mitigating role during market turbulence should be fully utilized. Additionally, attention should be given to the potential risks associated with the transformation of risk roles during extreme events or changes in macroeconomic policies.
期刊介绍:
The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.