Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models
Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models
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通过自回归条件区间模型预测原油价格区间和回报波动率
DOI：
10.1080/07474938.2021.1889202
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发表时间：
2021-07
影响因子：
1.2
通讯作者：
Wang Shouyang
中科院分区：
文献类型：
--
作者：
He Yanan;Han Ai;Sun Yuying;Hong Yongmiao;Wang Shouyang
原油价格是市场参与者和政府制定能源政策和决策的重要依据。本文应用一种新提出的自回归条件区间（ACI）模型对原油价格进行预测。与现有的基于点的预测模型相比，基于区间的ACI模型可以在统一的框架内捕捉油价在水平和范围上的变化动态。可以同时利用区间值观测中包含的丰富信息，提高参数估计效率和模型预测精度。在预测月度西德克萨斯中质原油（WTI）价格时，我们发现ACI模型优于流行的基于点的时间序列模型。特别是，ACI模型比单变量ARMA模型和矢量误差修正模型（VECM）提供更好的预测。ACI模型的增益可以在样本外的月度价格区间预测以及点值高点、低点和区间预测中发现。与GARCH和条件自回归极差（CARR）模型相比，ACI模型在预测油价波动率（条件方差）方面也有较好的效果。利用月度高点和低点预测的交易策略进一步发展。在ACI模型下，这种交易策略通常比基于点的VECM产生更有利可图的交易回报。
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.
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DOI：
10.5547/01956574.39.si2.cbau
发表时间：
2017-02
期刊：
The Energy Journal
影响因子：
--
作者：
C. Baumeister;Lutz Kilian;Xiaoqing Zhou
通讯作者：
C. Baumeister;Lutz Kilian;Xiaoqing Zhou
股市区间模型中特朗普当选的不确定性冲击
影响因子：
1.3
作者：
Sun Yuying;Qiao Kenan;Wang Shouyang
通讯作者：
Wang Shouyang
DOI：
10.2139/ssrn.1866223
发表时间：
2011-06
期刊：
Econometrics: Econometric & Statistical Methods - General eJournal
影响因子：
--
作者：
M. Caporin;A. Ranaldo;Paolo Santucci de Magistris
通讯作者：
M. Caporin;A. Ranaldo;Paolo Santucci de Magistris
DOI：
10.1016/b978-0-444-62731-5.00016-6
发表时间：
2013
期刊：
--
影响因子：
--
作者：
Andrew J. Patton
通讯作者：
Andrew J. Patton
影响因子：
1.3
作者：
Wei Yang;Ai Han;Yongmiao Hong;Shouyang Wang
通讯作者：
Wei Yang;Ai Han;Yongmiao Hong;Shouyang Wang