Forecasting the crude oil prices based on Econophysics and Bayesian approach

Leng, Na; Li, Jiang-Cheng

doi:10.1016/j.physa.2020.124663

Cited by 18 publications

(14 citation statements)

References 35 publications

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“…Moreover, it plays a key overriding role in the real-world economy. It is also the preponderant energy resource for expansion, growth, and industrialization in countries seeking sustainable and stable development ( Leng and Li, 2020 ). The crude oil market is complex and involves significant amounts of information.…”

Section: Introductionmentioning

confidence: 99%

“…Several other studies have employed different econometric tools to predict the crude oil price. For example, Leng and Li (2020) examined the dynamic forecasting structure of crude oil prices, employing econophysics and Bayesian frameworks. Their comparative analysis results showed that both approaches effectively quantified crude oil's predictability information.…”

Section: Introductionmentioning

confidence: 99%

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The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

Jabeur¹,

Khalfaoui

Arfi

2021

Journal of Environmental Management

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This study aims to predict oil prices during the 2019 novel coronavirus (COVID-19) pandemic by looking into green energy resources, global environmental indexes (ESG), and stock markets. The study employs advanced machine learning, such as the LightGBM, CatBoost, XGBoost, Random Forest (RF), and neural network models. An accurate forecasting framework can effectively capture the trend of the changes in oil prices and reduce the impact of the COVID-19 pandemic on such prices. Additionally, a large dataset with different asset classes was used to investigate the crash period. The research also introduced SHapely Additive exPlanations (SHAP) values for model analysis and interpretability. The empirical results indicate the superiority of the RF and LightGBM over traditional models. Moreover, this new framework provides favorable explanations of the model performance using the efficient SHAP algorithm. It also highlights the core features of predicting oil prices. The study found that high values of GER and ESG lead to lower crude oil prices. Our results are crucial for investors and policymakers in promoting climate change mitigation and sustained economic prosperity through green energy resources.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

Jabeur¹,

Khalfaoui

Arfi

2021

Journal of Environmental Management

View full text Add to dashboard Cite

show abstract

“…The first paper that used variables from the energy sector was published in 2011, which aimed to predict the gas price in England (Benth, 2011). Since then, the use of energy variables has increased significantly, but still with studies focused only on forecasting the price of energy sources, such as crude oil and gas (Dong et al, 2020;Hsu et al, 2017;Leng and Li, 2020).…”

Section: Main Information Of the Corpusmentioning

confidence: 99%

“…In Wollongong, the studies were developed by the school of mathematics and applied statistics to predict stock exchange assets and evaluate variance swaps (He and Chen, 2021;He and Zhu, 2016;Rujivan and Zhu, 2012;Zhu and Lian, 2011;2015). In China, most studies focused on application of Econophysics models to predict the price of energy-related assets (Ding et al, 2019;Dong et al, 2020;Leng and Li, 2020;Li et al, 2016;Zhong et al, 2019). The most relevant authors are also from these countries: Song-Ping Zhu from Australia and Jiang-Cheng LI from China.…”

Section: Main Information Of the Corpusmentioning

confidence: 99%

Can the Heston Model Forecast Energy Generation? A Systematic Literature Review

Reichert

Souza

2022

IJEEP

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The ability to predict the price of stock exchange assets has attracted the attention of economists and physicists around the world, as physical models are useful to predict volatility behaviors. Knowing that volatility is crucial for energy sector planning, the research aim was to investigate whether the Heston pricing model is useful to predict energy generation, trough the steps established by the systematic review protocol. In a corpus of 25 documents, it was possible to identify: lots of financial studies, energy and demography researches; a low level of interaction among universities; the largest number of publications from Australia and China; the most important journal; and the advantages of applying Econophysics models to solve volatility problems. In conclusion, the Heston model can be applied to predict energy generation, since it is a closed-form model and capable of modeling the stochastic volatility, reversing it to the predicted value of average energy generation.

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“…The distribution of income of societies as far ranging as ancient Egypt to 19th-century Europe and contemporary Japan exhibit a mathematical structure mirroring the Gibbs distribution from statistical mechanics (Sinha & Chakrabarti 2012, 53). Leng & Li (2020) analyze fluctuations in crude oil prices using a measure of information entropy in order to estimate the 'entropy', that is uncertainty, of pricing in the global crude oil market.…”

Section: The Status Of Mathematical Transfermentioning

confidence: 99%

Econophysics: making sense of a chimera

Yee

2021

Euro Jnl Phil Sci

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The history of economic thought witnessed several prominent economists who took seriously models and concepts in physics for the elucidation and prediction of economic phenomena. Econophysics is an emerging discipline at the intersection of heterodox economics and the physics of complex systems, with practitioners typically engaged in two overlapping but distinct methodological programs. The first is to export mathematical methods used in physics for the purposes of studying economic phenomena. The second is to export mechanisms in physics into economics. A conclusion is drawn that physics transfer is often justified at the level of mathematical transfer but unjustified at the level of mechanistic transfer.

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Forecasting the crude oil prices based on Econophysics and Bayesian approach

Cited by 18 publications

References 35 publications

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

Can the Heston Model Forecast Energy Generation? A Systematic Literature Review

Econophysics: making sense of a chimera

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