基于门限自回归条件异方差区间模型的大宗商品价格预测  

Commodity Price Forecasting Based on Heteroskedasticity Threshold Autoregressive Models for Interval Data

作　　者：包皓文 孙玉莹[1,2,3] 洪永淼 汪寿阳 BAO Haowen;SUN Yuying;HONG Yongmiao;WANG Shouyang(Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China;Center for Forecasting Science,Chinese Academy of Sciences,Beijing 100190,China;School of Economics and Management,and MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation,University of Chinese Academy of Sciences,Beijing 100190,China;School of Entrepreneurship and Management,ShanghaiTech University,Shanghai 201210,China)

机构地区：[1]中国科学院数学与系统科学研究院,北京100190 [2]中国科学院预测科学研究中心,北京100190 [3]中国科学院大学经济与管理学院,数字经济监测预测预警与政策仿真教育部哲学社会科学实验室,北京100190 [4]上海科技大学创业与管理学院,上海201210

出　　处：《计量经济学报》2024年第2期301-323,共23页China Journal of Econometrics

基　　金：国家自然科学基金(72322016,72073126,72091212,71973116);国家自然科学基金“计量建模与经济政策研究”基础科学中心项目(71988101)。

摘　　要：大宗商品是工业生产和金融投资中不可或缺的组成部分,准确的大宗商品价格预测对保障工业生产顺利进行和帮助投资者规避风险具有重要意义.但现有的大宗商品价格预测模型大多是基于收盘价构建的点值模型,忽略了价格的波动信息.因此,本文从区间价格预测的角度出发,提出了一个带有外生变量的门限自回归条件异方差区间模型(HTARIX),构建了一个基于区间型数据的检验统计量来检验模型是否存在条件异方差,进而提出了广义最小DK距离估计求解模型参数,并将其应用于大宗商品市场.HTARIX模型的优势在于能够捕捉区间型时间序列模型的条件异方差和非线性特征.相比于传统的点值数据模型,我们的方法能够更加充分地利用区间的内部信息.实证结果表明,本文提出的模型在大宗商品区间价格预测上的表现优于其他对比模型.Commodity is an important part of industrial production and financial investment,and accurate commodity price forecasting is of great significance to safeguard industrial production and help investors avoid risks.However,most of the existing commodity price forecasting models are point-value models based on closing prices,which ignores the volatility information.Therefore we propose a heteroskedasticity threshold autoregressive interval model with exogenous variables(HTARIX)and apply it to the commodity markets.We also construct a test statistic based on interval-valued data to test whether there is conditional heteroskedasticity in the model,and propose a generalized minimum DK distance estimation.The advantage of our model is that it can capture the conditional heteroskedasticity and nonlinear features of interval-valued time series models.Compared with the point-valued models,our method contains more information of the data.The empirical results imply that HTARIX model performs better than other comparative models in interval-valued commodity price forecasting.

关 键 词：大宗商品价格 区间数据 条件异方差 门限自回归区间模型 非线性 

分 类 号：F224[经济管理—国民经济] F426F764