{"title":"Can inflation predict energy price volatility?","authors":"Jonathan A. Batten, Di Mo, Armin Pourkhanali","doi":"10.1016/j.eneco.2023.107158","DOIUrl":null,"url":null,"abstract":"<div><p>Fluctuations in energy prices impact production costs and inflation. This study examines whether inflation data can predict volatility in energy markets. Both inflation and energy market volatility exhibit complex behaviour over time, including structural shifts due to demand and supply shocks. Accounting for differences in data frequencies, we use an extended GARCH model (MIDAS) with Laguerre polynomials for time-varying parameters. The empirical results demonstrate that including low-frequency inflation data enhances energy model predictability particularly during periods of high volatility and extreme price fluctuations. Considering inflation improves forecasting for energy market models, benefiting portfolio management and helping policymakers manage inflation.</p></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"129 ","pages":"Article 107158"},"PeriodicalIF":13.6000,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0140988323006564/pdfft?md5=d2e75f3444df669230dc4112f2b8fb5c&pid=1-s2.0-S0140988323006564-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Economics","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140988323006564","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Fluctuations in energy prices impact production costs and inflation. This study examines whether inflation data can predict volatility in energy markets. Both inflation and energy market volatility exhibit complex behaviour over time, including structural shifts due to demand and supply shocks. Accounting for differences in data frequencies, we use an extended GARCH model (MIDAS) with Laguerre polynomials for time-varying parameters. The empirical results demonstrate that including low-frequency inflation data enhances energy model predictability particularly during periods of high volatility and extreme price fluctuations. Considering inflation improves forecasting for energy market models, benefiting portfolio management and helping policymakers manage inflation.
期刊介绍：
Energy Economics is a field journal that focuses on energy economics and energy finance. It covers various themes including the exploitation, conversion, and use of energy, markets for energy commodities and derivatives, regulation and taxation, forecasting, environment and climate, international trade, development, and monetary policy. The journal welcomes contributions that utilize diverse methods such as experiments, surveys, econometrics, decomposition, simulation models, equilibrium models, optimization models, and analytical models. It publishes a combination of papers employing different methods to explore a wide range of topics. The journal's replication policy encourages the submission of replication studies, wherein researchers reproduce and extend the key results of original studies while explaining any differences. Energy Economics is indexed and abstracted in several databases including Environmental Abstracts, Fuel and Energy Abstracts, Social Sciences Citation Index, GEOBASE, Social & Behavioral Sciences, Journal of Economic Literature, INSPEC, and more.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
