Measurement of international crude oil price cyclical fluctuations and correlation with the world economic cyclical changes

Nan, Yu; Sun, Renjin; Zhao, Zijian; Fangjing, Chu

doi:10.1016/j.energy.2022.124946

Cited by 9 publications

(2 citation statements)

References 44 publications

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“…Therefore, the relationship between oil price and GDP has always been a popular research topic, and oil price is also regarded as one of the predictor variables of GDP [2]. Previous studies have also shown that there is a positive correlation between the world economic growth rate and oil price fluctuations [3]. The stock market has always been regarded as closely related to economic development, and the changes in the stock market reflect the development trend of the macroeconomy to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Oil Prices, Gold Prices, the Stock Market, and U.S. GDP

2024

HBEM

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The forecast of GDP has always been a popular research topic today, and its related influencing factors are also complicated. This paper selects three key factors in economic development: gold price, crude oil price, and stock market index, explores their relationship with U.S. GDP, and then uses these three factors as predictive variables to obtain a multiple linear regression equation related to GDP. The research methods of this paper are as follows: Firstly, monthly U.S. GDP, monthly crude oil price, monthly gold price, and monthly S&P 500 index were collected. Secondly, correlation analysis was carried out on these data, including calculation of correlation coefficient and cross-correlation analysis. Correlation analysis showed that GDP had a significant positive correlation with other variables. Then, a multiple linear regression model was established with monthly U.S. GDP as the predicted variable, monthly crude oil price, monthly gold price, and monthly S&P 500 index as the predictor variable. Finally, multiple regression equations are obtained through testing. This multiple regression equation can be used to predict GDP further.

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Section: Introductionmentioning

confidence: 99%

The Relationship between Oil Prices, Gold Prices, the Stock Market, and U.S. GDP

2024

HBEM

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“…Much of the earlier paper has analyzed the role of the oil market shocks on economies around the world [11][12][13][14]. The perspectives mainly include the implications of oil volatility on micro-firms [15] and macroeconomic indicators [16].…”

Section: Introductionmentioning

confidence: 99%

Extreme risk measurement for the oil and China’s sectors system—network-based approach and machine learning methods

Fang,

Wang,

Lin

et al. 2023

Front. Phys.

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China is a large oil-consuming country, and sharp fluctuations in oil prices are bound to be detrimental to the stable growth of its economy. Therefore, accurately grasping the impact of the oil market on China’s sectors is the key to ensuring its healthy economic development. The article aims to explore the extreme risk transfer link of the oil and China’s sectors system, focusing on uncovering the risk spillover mechanism of the oil and providing early warning on it. We apply the TENET method to discuss risk propagation relationships within the oil and sectors system at three levels. The TVP-VAR model is brought in to recognize the factors affecting risk spillover in the oil market from the network correlation perspective. Finally, early warning of oil risk spillover is provided by incorporating the influencing factors into a machine learning model. The outcomes indicate that the risk connectivity of the oil and China’s sectors system is highly correlated with extreme events. There are variations in the spillover effects of oil market risk on different sectors, with Telecommunication Services, Utilities, Financials and Major Consumer sectors being the main bearers of the oil risk shocks. Overall, oil risk spillovers are mainly driven by economic policy and geopolitics, but oil price uncertainty is found to have a persistent impact on oil market risk spillovers in the dynamic analysis. Random forest model can provide effective early warning of oil risk spillovers. In addition, the significance analysis shows that oil price uncertainty and inflation are important factors affecting oil risk spillovers and are nonlinearly correlated with them.

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Exploring pyrolysis of the aromatics in shale oil by experimental study and kinetic modelling

Wang

Han

Jiang

2023

Energy

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Measurement of international crude oil price cyclical fluctuations and correlation with the world economic cyclical changes

Cited by 9 publications

References 44 publications

The Relationship between Oil Prices, Gold Prices, the Stock Market, and U.S. GDP

The Relationship between Oil Prices, Gold Prices, the Stock Market, and U.S. GDP

Extreme risk measurement for the oil and China’s sectors system—network-based approach and machine learning methods

Exploring pyrolysis of the aromatics in shale oil by experimental study and kinetic modelling

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