Analysis and Bayes statistical probability inference of crude oil price change point

Chai, Jian; Lu, Quanying; Hu, Yi; Wang, Shouyang; Lai, Kin Keung; Liu, Hongtao

doi:10.1016/j.techfore.2017.09.007

Cited by 18 publications

(5 citation statements)

References 52 publications

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“…In addition to predicting research trends, the prediction of breakpoints and turning points also deserves scholarly attention. At present, scholars have performed change point analysis and statistical inference, and have carried out a probabilistic simulation of this mutation rule, in order to quickly identify and even predict the mutation point [99]. Successfully predicting the turning point can help stakeholders to identify and prevent risks, and achieve sustainable economic development [100].…”

Section: Conclusion and Future New Research Directionmentioning

confidence: 99%

Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis

Zhang

Jiao

et al. 2022

Sustainability

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As the supply of commodities forms essential lifelines for modern society, commodity price fluctuations can significantly impact the operation and sustainable development of macroeconomics, production activities, and people’s security and well-being. The commodity trading market also plays a pivotal role in the competition of the international industrial chain and the sustainable development of the industry. The method of bibliometrics was used in this study to trace the development of research on commodity prices, and statistical and co-citation analyses were conducted on selected literature samples. It was found that the research hotspots in this field are concentrated on four aspects: factors influencing commodity prices, the impact of price fluctuations on the macroeconomy, forecasts of commodity prices, and the financialization of commodities. A current commodity price research network centered on oil prices has formed. Future directions in this field, which developed out of oil shock research 40 years ago, can further investigate the impact of factors and the responses that can be made to emergencies on commodity prices, as well as continuing to develop better methods of commodity price forecasting.

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Section: Conclusion and Future New Research Directionmentioning

confidence: 99%

Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis

Zhang

Jiao

et al. 2022

Sustainability

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“…Our study determined that the predictive accuracy of crude oil prices was opposite to the prediction length in Section 4.2, Comparisons of different models, and Section 5.1, The Different predictive horizons [41]. As seen earlier, in the four predictive horizons of the three models, the one-step prediction error was the least, followed by the two-step prediction, the four-step prediction, and finally, the eight-step prediction, which indicated that the longer the predictive horizon, the larger the predictive error.…”

Section: Discussion Of the Results And Conclusionmentioning

confidence: 97%

Forecasting Crude Oil Prices with a WT-FNN Model

Wang

Fang

2022

Energies

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In order to improve the accuracy of forecasting crude oil prices, a new crude oil price forecasting method is introduced in the paper that is a combination of the FNN model and the stochastic time effective function—namely, the WT-FNN model. The FNN model keeps track of the historical values of crude oil prices and predicts future crude oil prices, and the stochastic time effective function gives greater weight to recent information and smaller weight to old information, thus making the prediction of crude oil prices more reasonable. We selected the daily data of Brent crude oil prices from 4 January 2000 to 30 September 2021 as research objects and then used the WT-FNN model to train and predict the research objects. By comparing it to the benchmark model, we found that the predictive effect of the WT-FNN model was better than the FNN model and the no-change model. The results also passed a robustness test.

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“…To sum up, the desired properties of an econometric model for commodity prices include the following features: the ability to handle a large number of variables on a theoretical basis; "adaptability", meaning that model coefficients are continuously reestimated (updated) as new market information becomes available; the ability to minimise the bias towards human decisions, as human subjectivity can influence model outcomes; and the ability to capture the time-varying importance of different explanatory variables (Huang et al 2021). Indeed, addressing these objectives is crucial in developing a robust econometric approach that can effectively forecast commodities prices in the presence of complex and dynamic data environments (Chai et al 2018;Yin et al 2018;Zhao et al 2017).…”

Section: Forecasting Methods Challengesmentioning

confidence: 99%

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

Drachal,

Pawłowski

2024

IJFS

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This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities’ prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on genetic programming, was also used as a benchmark tool. Secondly, various other econometric methods dealing with variable uncertainty were estimated including Bayesian Model Averaging, Dynamic Model Averaging, LASSO, ridge, elastic net, and least-angle regressions, etc. Therefore, this study reports a concise and uniform comparison of an application of several popular econometric models to forecasting the prices of numerous commodities. Robustness checks and statistical tests were performed to strengthen the obtained conclusions. Monthly data beginning from January 1988 and ending in August 2021 were analysed.

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Analysis and Bayes statistical probability inference of crude oil price change point

Cited by 18 publications

References 52 publications

Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis

Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis

Forecasting Crude Oil Prices with a WT-FNN Model

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

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