Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models

doi:10.1080/07474938.2021.1889202

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	Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models
	He, Yanan 1; Han, Ai 2,3,4; Hong, Yongmiao 2,3,5; Sun, Yuying 2,3,5; Wang, Shouyang 2,3,5
	2021-07-03
发表期刊	ECONOMETRIC REVIEWS
ISSN	0747-4938
卷号	40 期号:6 页码:584-606
摘要	Crude oil prices are of vital importance for market participants and governments to make energy policies and decisions. In this paper, we apply a newly proposed autoregressive conditional interval (ACI) model to forecast crude oil prices. Compared with the existing point-based forecasting models, the interval-based ACI model can capture the dynamics of oil prices in both level and range of variation in a unified framework. Rich information contained in interval-valued observations can be simultaneously utilized, thus enhancing parameter estimation efficiency and model forecasting accuracy. In forecasting the monthly West Texas Intermediate (WTI) crude oil prices, we document that the ACI models outperform the popular point-based time series models. In particular, ACI models deliver better forecasts than univariate ARMA models and the vector error correction model (VECM). The gain of ACI models is found in out-of-sample monthly price interval forecasts as well as forecasts for point-valued highs, lows, and ranges. Compared with GARCH and conditional autoregressive range (CARR) models, ACI models are also superior in volatility (conditional variance) forecasts of oil prices. A trading strategy that makes use of the monthly high and low forecasts is further developed. This trading strategy generally yields more profitable trading returns under the ACI models than the point-based VECM.
关键词	ACI model interval-valued crude oil prices range trading strategy volatility forecast
DOI	10.1080/07474938.2021.1889202
收录类别	SCI
语种	英语
资助项目	China NNSF[71703156] ; China NNSF[72073126] ; China NNSF[72091212] ; China NNSF[71403231] ; China NNSF[71671183] ; China NNSF[71988101]
WOS研究方向	Business & Economics ; Mathematics ; Mathematical Methods In Social Sciences
WOS类目	Economics ; Mathematics, Interdisciplinary Applications ; Social Sciences, Mathematical Methods ; Statistics & Probability
WOS记录号	WOS:000681583400003
出版者	TAYLOR & FRANCIS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59030
专题	中国科学院数学与系统科学研究院
通讯作者	Hong, Yongmiao; Sun, Yuying
作者单位	1.Xiamen Univ, Wang Yannan Inst Studies Econ, Xiamen, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China 4.JD Com, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China
推荐引用方式 GB/T 7714	He, Yanan,Han, Ai,Hong, Yongmiao,et al. Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models[J]. ECONOMETRIC REVIEWS,2021,40(6):584-606.
APA	He, Yanan,Han, Ai,Hong, Yongmiao,Sun, Yuying,&Wang, Shouyang.(2021).Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models.ECONOMETRIC REVIEWS,40(6),584-606.
MLA	He, Yanan,et al."Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models".ECONOMETRIC REVIEWS 40.6(2021):584-606.