{"title":"Overnight Trading Matters!—Volatility Forecast in the Crude Oil Futures Market","authors":"Jing Hao, Feng He, Liyuan Qin","doi":"10.1002/for.70089","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This study primarily explores the potential of night-time trading information to enhance the prediction accuracy of WTI crude oil futures price volatility within the HAR framework. Out-of-sample analysis reveals that integrating daily, monthly, or weekly overnight information into the HAR models notably improves their capacity to forecast crude oil futures price volatility. This suggests that overnight information effectively improves forecasting performance. Moreover, we demonstrate that short-term overnight information possesses predictive power surpassing that of long-term information. Finally, these findings are robust to the application of the recursive window method and the HAR-CJ model.</p>\n </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 4","pages":"1511-1528"},"PeriodicalIF":2.6000,"publicationDate":"2026-06-02","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.70089","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/18 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
This study primarily explores the potential of night-time trading information to enhance the prediction accuracy of WTI crude oil futures price volatility within the HAR framework. Out-of-sample analysis reveals that integrating daily, monthly, or weekly overnight information into the HAR models notably improves their capacity to forecast crude oil futures price volatility. This suggests that overnight information effectively improves forecasting performance. Moreover, we demonstrate that short-term overnight information possesses predictive power surpassing that of long-term information. Finally, these findings are robust to the application of the recursive window method and the HAR-CJ model.
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.