主成分分析
可预测性
计量经济学
期货合约
水准点(测量)
计算机科学
原油
异方差
差异(会计)
经济
统计
人工智能
数学
财务
工程类
地理
会计
石油工程
大地测量学
作者
Yaojie Zhang,Yudong Wang
标识
DOI:10.1016/j.ijforecast.2022.01.010
摘要
To improve the predictability of crude oil futures market returns, this paper proposes a new combination approach based on principal component analysis (PCA). The PCA combination approach combines individual forecasts given by all PCA subset regression models that use all potential predictor subsets to construct PCA indexes. The proposed method can not only guard against over-fitting by employing the PCA technique but also reduce forecast variance due to extensive forecast combinations, thus benefiting from both the combination of information and the combination of forecasts. Showing impressive out-of-sample forecasting performance, the PCA combination approach outperforms a benchmark model and many related competing models. Furthermore, a mean–variance investor can realize sizeable utility gains by using the PCA combination forecasts relative to the competing forecasts from an asset allocation perspective.
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