Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?
Resources Policy
; 75:102521, 2022.
Article
in English
| ScienceDirect | ID: covidwho-1569019
ABSTRACT
In this paper, we try to forecast the volatility of Chinese crude oil futures (COF) using multiple economic policy uncertainty indicators. MIDAS-RV model is combined with the principal component analysis (PCA), scaled PCA (SPCA) and partial least squares (PLS) techniques in this work, construct the newly MIDAS-RV-PCA, MIDAS-RV-PLS and MIDAS-RV-SPCA models, their prediction performance is compared with the common combination forecasting methods. The in-sample estimation analysis on MIDAS-RV-X models show the that it is necessary to consider multiple economic policy uncertainty indices when predicting the Chinese COF volatility and the in-sample analysis on dimensionality reduction model further demonstrate the rationality of using dimensionality reduction techniques. The out-of-sample evaluation results show that the MIDAS-RV-SPCA is a superior model when forecasting the short-term volatility of Chinese COF using multiple economic policy uncertainty indicators, especially during the periods of high volatility and COVID-19 pandemic. The results also indicates that the DMSPE(0.9) method in the combination forecasting method shows its superior forecasting ability in long-term volatility of Chinese COF, especially during the low volatility and pre-pandemic period.
Full text:
Available
Collection:
Databases of international organizations
Database:
ScienceDirect
Type of study:
Prognostic study
Language:
English
Journal:
Resources Policy
Year:
2022
Document Type:
Article
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