KMS Of Academy of mathematics and systems sciences, CAS
| Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models | |
| He, Yanan1; Han, Ai2,3,4; Hong, Yongmiao2,3,5; Sun, Yuying2,3,5; Wang, Shouyang2,3,5 | |
| 2021-07-03 | |
| Source Publication | ECONOMETRIC REVIEWS
 |
| ISSN | 0747-4938 |
| Volume | 40Issue:6Pages:584-606 |
| Abstract | 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. |
| Keyword | ACI model interval-valued crude oil prices range trading strategy volatility forecast |
| DOI | 10.1080/07474938.2021.1889202 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | China NNSF[71703156] ; China NNSF[72073126] ; China NNSF[72091212] ; China NNSF[71403231] ; China NNSF[71671183] ; China NNSF[71988101] |
| WOS Research Area | Business & Economics ; Mathematics ; Mathematical Methods In Social Sciences |
| WOS Subject | Economics ; Mathematics, Interdisciplinary Applications ; Social Sciences, Mathematical Methods ; Statistics & Probability |
| WOS ID | WOS:000681583400003 |
| Publisher | TAYLOR & FRANCIS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/59030 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Hong, Yongmiao; Sun, Yuying |
| Affiliation | 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 |
| Recommended Citation 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. |
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