Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?

Xiang, Yan; Bai, Jiancheng; Li, Xiafei; Chen, Zhonglu

doi:10.1016/j.resourpol.2021.102521

Cited by 19 publications

(6 citation statements)

References 41 publications

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“…( 2022 ) and Yan et al. ( 2022 ). The forecast combinations employed all the predictive information from each predictor (Set U ) and combined them to obtain the final prediction.…”

Section: Methodsmentioning

confidence: 97%

“…( 2022 ) and Yan et al. ( 2022 ) confirmed that the s-PCA-based PU index exhibits more powerful predictability on crude oil volatility compared with other competing methods. Moreover, s-PCA is also employed to extract predictive information from macro variables (Huang et al.…”

Section: Introduction and Literature Reviewmentioning

confidence: 88%

“…( 2022 ) and Yan et al. ( 2022 ) argued that the s-PCA method outperforms other competing models, the valid evidence to demonstrate why the s-PCA method is better is still rare, and we will attempt to fill this gap.…”

Section: Introduction and Literature Reviewmentioning

confidence: 98%

“…This approach is more comprehensive compared with that which was adopted by Yan et al. ( 2022 ) and Guo et al. ( 2022 ), who developed a composite index employing the s-PCA method on policy-related indices only.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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Uncertainty index and stock volatility prediction: evidence from international markets

et al. 2022

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This study investigates the predictability of a fixed uncertainty index (UI) for realized variances (volatility) in the international stock markets from a high-frequency perspective. We construct a composite UI based on the scaled principal component analysis (s-PCA) method and demonstrate that it exhibits significant in- and out-of-sample predictabilities for realized variances in global stock markets. This predictive power is more powerful than those of two commonly employed competing methods, namely, PCA and the partial least squares (PLS) methods. The result is robust in several checks. Further, we explain that s-PCA outperforms other dimension-reduction methods since it can effectively increase the impacts of strong predictors and decrease those of weak factors. The implications of this research are significant for investors who allocate assets globally.

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“…( 2022 ) and Yan et al. ( 2022 ). The forecast combinations employed all the predictive information from each predictor (Set U ) and combined them to obtain the final prediction.…”

Section: Methodsmentioning

confidence: 97%

Section: Introduction and Literature Reviewmentioning

confidence: 88%

Section: Introduction and Literature Reviewmentioning

confidence: 98%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Uncertainty index and stock volatility prediction: evidence from international markets

et al. 2022

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“…The MIDAS-RV-PLS model can be defined as: where denotes the key information/common factors (here, we use the first principal component of all predictors) extracted from all additional predictors by PLS. For details about the PCA and PLS technique, see Yan et al ( 2022 ) and He et al ( 2021b ). The parameters of MIDAS-RV-X, MIDAS-RV-PCA and MIDAS-RV-PLS models are still estimated by the maximum likelihood estimation method.…”

Section: Methodsmentioning

confidence: 99%

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model

Liang

2022

Ann Oper Res

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This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.

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Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large‐scale variables

Qiao,

Pan,

Liang

et al. 2024

Journal of Forecasting

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This paper aims to study the volatility forecasting of Chinese crude oil futures from the large‐scale variables perspective by considering both the information on international futures markets volatility and technical indicators of Chinese crude oil futures. We employ the dual feature processing method (LASSO‐PCA) by integrating least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) to extract important factors of the large‐scale exogenous variables. Besides the traditional ordinary least squares (OLS) estimation, the nonlinear support vector regression (SVR) approach is employed to integrate with the LASSO‐PCA method. The empirical results show that both the OLS and SVR combined with LASSO‐PCA can improve the forecasting accuracy, especially SVR‐LASSO‐PCA owns the best forecasting performance. The analysis of the selected variables finds international futures volatility is chosen more frequently. These results are further validated through a series of robust experiments. Finally, the time difference between China and the United States is also considered in order to obtain more reasonable out‐of‐sample forecasting.

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Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?

Cited by 19 publications

References 41 publications

Uncertainty index and stock volatility prediction: evidence from international markets

Uncertainty index and stock volatility prediction: evidence from international markets

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model

Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large‐scale variables

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