A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

The oil industries are an important part of a country’s economy.The crude oil’s price is influenced by a wide range of variables.Therefore,how accurately can countries predict its behavior and what predictors to employ are two main questions.In this view,we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance.The suggested method is based on a deep learning snapshot ensemble method of the Transformer model.To examine the superiority of the proposed model,this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries(OPEC)oil price forecasting.Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods.More precisely,the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA(1,1,1),ARIMA(0,1,1),autoregressive moving average(ARMA)(0,1),vector autoregression(VAR),random walk(RW),support vector machine(SVM),and random forests(RF)models by 99.94%,99.62%,99.87%,99.65%,7.55%,98.38%,and 99.35%,respectively,according to mean square error metric.

The relationship between international crude oil prices and China＇s refined oil prices based on a structural VAR model

With the frequent fluctuations of international crude oil prices and China＇s increasing dependence on foreign oil in recent years, the volatility of international oil prices has significantly influenced China domestic refined oil price. This paper aims to investigate the transmission and feedback mechanism between international crude oil prices and China＇s refined oil prices for the time span from January 2011 to November 2015 by using the Granger causality test, vector autoregression model, impulse response function and variance decomposition methods. It is demonstrated that variation of international crude oil prices can cause China domestic refined oil price to change with a weak feedback effect. Moreover, international crude oil prices and China domestic refined oil prices are affected by their lag terms in positive and negative directions in different degrees. Besides, an international crude oil price shock has a signif- icant positive impact on domestic refined oil prices while the impulse response of the international crude oil price variable to the domestic refined oil price shock is negatively insignificant. Furthermore, international crude oil prices and domestic refined oil prices have strong historical inheri- tance. According to the variance decomposition analysis, the international crude oil price is significantly affected by its own disturbance influence, and a domestic refined oil price shock has a slight impact on international crude oil price changes. The domestic refined oil price variance is mainly caused by international crude oil price disturbance, while the domestic refined oil price is slightly affected by its own disturbance. Generally, domestic refined oil prices do not immediately respond to an international crude oil price change, that is, there is a time lag.

Economics,fundamentals,technology,finance,speculation and geopolitics of crude oil prices:an econometric analysis and forecast based on data from 1990 to 2017

It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencing international crude oil prices and to establish crude oil price models to forecast long-term international crude oil prices. Six explanatory influential variables, namely Dow Jones Indexes, the Organization for Economic Cooperation and Development oil stocks, US rotary rig count, US dollar index, total open interest, which is the total number of outstanding contracts that are held by market participants at the end of each day, and geopolitical instability are specified, and the samples, from January 1990 to August 2017, are divided into six sub-periods. Moreover, the co-integration relationship among variables shows that the contribution ratios of all the variables influencing Brent crude oil prices are in accordance with the corresponding qualitative analysis. Furthermore, from September 2017 to December 2022 outside of the sample, the Vector Autoregressive forecasts show that annually averaged Brent crude oil prices for 2017-2022 would be $53.0, $61.3, $74.4, $90.0, $105.5, and $120.7 per barrel, respectively. The Vector Error Correction forecasts show that annual average Brent crude oil prices for 2017-2022 would be $53.0, $56.5, $58.5, $60.7, $63.0 and $65.4 per barrel, respectively.

Improved Crude Oil Price Forecasting With Statistical Learning Methods

Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. This paper presents a statistical learning method （SLM） based on combined fuzzy system （FS）, artificial neural network （ANN）, and support vector regression （SVR） to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. A number of quantitative factors were discovered from this model and used as the input. For verification and testing, the West Texas Intermediate （WT1） crude oil spot price is used to test the effectiveness of the proposed learning methodology. Empirical results reveal that the proposed SLM-based forecasting can model the nonlinear relationship between the input variables and price very well. Furthermore, in-sample and out-of-sample prediction performance also demonstrates that the proposed SLM model can produce more accurate prediction results than other nonlinear models.

RESEARCH ARTICLE Sensitivity of Crude Oil Price Change to Major Global Factors and to Russian-Ukraine War Crisis

To assess the elasticity of crude oil price to global factors related to supply of crude oil and the US dollar exchange rate, the authors employed nonlinear models including flexible least squares, and maximum likelihood estimator, in addition to OLS regression mode;using yearly data from 1965 to 2021. The findings indicate change in oil prices due to 1% change in any of the explanatory variables, as follows: the effect of the US dollar depreciation rate, raise crude oil price/barrel by 71 US cents;and increase in OPEC production, decrease crude oil price by 82 US cents;a decrease in non-OPEC production, raise oil price by 4.78 US$. These results imply that, if a ban imposed on Russian crude oil export, and no increase in OPEC production to compensate Russian oil loss in the international markets, global crude oil price expected to rise by 88 US$ above its level before Russian-Ukraine crisis, meaning that crude oil price expected to rise at 160 US$ pbab. However, if OPEC members increase their output level by 10 million barrels per day to compensate the Russian oil loss, then global crude oil price is expected to stay at 102 US$ pb.

The interactions between Chinese local corn and WTI crude oil prices:an empirical analysis

This paper investigates the relationship between China’s fuel ethanol promotion plan and food security based on the interactions between the crude oil market, the fuel ethanol market and the grain market. Based on the US West Texas Intermediate(WTI) crude oil spot price and Chinese corn prices from January 2008 to May 2018, this paper applies Granger causality testing and a generalized impulse response function to explore the relationship between world crude oil prices and Chinese corn prices. The results show that crude oil prices are not the Granger cause of China’s corn prices, but changes in world crude oil prices will have a long-term positive impact on Chinese corn prices. Therefore, the Chinese government should pay attention to changes in crude oil prices when promoting fuel ethanol. Considering the conduction e ect between fuel ethanol and the food market, the government should also take some measures to ensure food security.

Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.

DID SPECULATIVE ACTIVITIES CONTRIBUTE TO HIGH CRUDE OIL PRICES DURING 1993 TO 2008?

By applying two nonlinear Granger causality testing methods and rolling window strategy to explore the relationship between speculative activities and crude oil prices, the unidirectional Granger causality from speculative activities to returns of crude oil prices during the high price phase is discovered. It is proved that speculative activities did contribute to high crude oil prices after the Asian financial crisis and OPEC＇s output cut in 1998. The unidirectional Granger causality from returns of crude oil prices to speculative activities is significant in general. But after 2000, with the sharp rise in crude oil prices, this unidirectional Granger causality became a complex nonlinear relationship, which cannot be detected by any linear Granger causaIity test.

CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms

Crude oil price prediction is a challenging task in oil producing countries.Its price is among the most complex and tough to model because fluctuations of price of crude oil are highly irregular,nonlinear and varies dynamically with high uncertainty.This paper proposed a hybrid model for crude oil price prediction that uses the complex network analysis and long short-term memory(LSTM)of the deep learning algorithms.The complex network analysis tool called the visibility graph is used to map the dataset on a network and K-core centrality was employed to extract the non-linearity features of crude oil and reconstruct the dataset.The complex network analysis is carried out in order to preprocess the original data to extract the non-linearity features and to reconstruct the data.Thereafter,LSTM was employed to model the reconstructed data.To verify the result,we compared the empirical results with other research in the literature.The experiments show that the proposed model has higher accuracy,and is more robust and reliable.

Brent vs.West Texas Intermediate in the US petro derivatives price formation

In this paper,we apply the spatial panel model to explore the relationship between the dynamic of two types of crude oil prices(WTI and Brent crude oil)and their refined products over time.Considering the turbulent months of 2011,when Cushing Oklahoma had reached capacity and the crude oil export ban removal in 2015 as breakpoints,we apply this method both in the full sample and the three resultant regimes.First,results suggest our results show that both WTI and Brent display very similar behaviour with the refined products.Second,when attending to each regime,results derived from the first and third regimes are quite similar to the full sample results.Therefore,during the second regime,Brent crude oil became the benchmark in the petrol market,and it influenced the distillate products.Furthermore,our model can let us determine the price-setters and price-followers in the price formation mechanism through refined products.These results possess important considerations to policymakers and the market participants and the price formation.

Attention Matters: An Exploration of Relationship Between Google Search Behaviors and Crude Oil Prices

Extant studies have suggested that Google search volume data can serve as a new and direct measure of investor attention in various research fields such as economy, financial and energy markets. However, it is not clear that whether investor attention influences prices in the same direction in different market states(prices increase or decrease). In this paper, the authors propose a measure of speculative attention, demonstrate its advantages by comparing it with several existing ones, and then adopt a Markov switching autoregressive model and an EGARCH model to examine its influences on crude oil prices in two market states. It is argued that the responses of crude oil prices to investor attention are asymmetrical in the two states of crude oil prices. The empirical study shows that one increase in searches causes a significant positive increase in crude oil prices during oil price surges, and a more significant reduction of prices during oil price collapses. The authors also conduct robustness checks by limiting the sample periods and using other measures, and the results support the asymmetric effect of web search behaviors on crude oil prices.

Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches

An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. In this study, two artificial intelligence approaches, has been used namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). We employed in-sample forecasting on daily free-on-board CPO prices in Malaysia and the series data stretching from a period of January first, 2004 to the end of December 2011. The predictability power of the artificial intelligence approaches was also made in regard with the statistical forecasting approach such as the autoregressive fractionally integrated moving average (ARFIMA) model. The general findings demonstrated that the ANN model is superior compared to the ANFIS and ARFIMA models in predicting the CPO prices.

Low Oil Prices Drive up China's Crude Oil Imports

China’s crude oil imports hit a record high in the first half of 2016 despite an economic slowdown,and analysts largely attributed the surge to low prices,not strategic maneuvering.The country imported 186.5 million tons of crude oil in the first half of the year,23.15 million

Inflationary effects of oil prices and domestic gasoline prices:Markov-switching-VAR analysis

The purpose of this study is to contribute to the literature by studying the effects of sudden changes both on crude oil import price and domestic gasoline price on inflation for Turkey, an emerging country. Since an inflation targeting regime is being carried out by the Central Bank of Turkey, determination of such effects is becoming more important. Therefore empirical evidence in this paper will serve as guidance for those countries, which have an in- flation targeting regime. Analyses have been done in the period of October 2005-December 2012 by Markovswitching vector autoregressive （MS-VAR） models which are successful in capturing the nonlinear properties of variables. Using MS-VAR analysis, it is found that there are 2 regimes in the analysis period. Furthermore, regime changes can be dated and the turning points of economic cycles can be determined. In addition, it is found that the effect of the changes in crude oil and domestic gasoline prices on consumer prices and core inflation is not the same under different regimes. Moreover, the sudden increase in gasoline price is more important for consumer price infla- tion than crude oil price shocks. Another finding is the presence of a pass-through effect from oil price and ga- soline price to core inflation.

An Empirical Study of Asian Crude Oil Premiums

The price of Middle East crude oil exported to Asian countries has been higher than that to Europe and America for a long period, and this price differential made Asian countries pay more than European and American countries. Prior investigations found that ＂Asian Crude Oil Premium＂ did exist at a relatively low oil price level. However, world oil price soared after 2003, making the price of Middle East crude oil exported to European countries or America rise quickly, sometimes even higher than that to Asia. Under this situation, this paper uses the price of Middle East crude oil sold to Europe or America or Asia to test if the premium exists at a high oil price level and concludes that the crude oil price premium of Asia against America does not exist, but the premium of Asia against Europe still exists.

2024年国际原油价格分析与趋势预测

2023年,全球经济增速放缓,在加息、减产、冲突等各因素叠加影响下,全年油价跌宕起伏,回吐2022年的风险溢价。展望2023年,从基本面和非基本面着手,分析全球经济、能源转型、供应、库存、美元、市场投机、黄金和地缘政治等因素未来动向,结合预测模型客观计算和专家的主观判断,对2024年国际原油价格走势进行整体展望和预测。预计2024年国际原油价格进一步下移,国际原油市场供需偏宽松,原油投资者信心不足,地缘冲突、极端天气等事件频发,非基本面扰动因素在短期内放大油价震荡区间,Brent、WTI原油均价将在73~83美元/桶和68~78美元/桶。

原油价格不确定性测度及其对宏观经济的非对称影响研究

有效测度原油价格不确定性并研究其对我国宏观经济的影响,具有重要的理论和现实意义。本文基于最小生成树模型,从全局视角衡量原油多元定价机制中195个影响因素的相对重要程度,并采用高维因子非预期条件波动率模型测度国际原油价格的不确定性。基于收敛交叉映射模型,本文对原油价格不确定性与宏观经济间的非线性因果关系进行识别,研究发现仅存在原油价格不确定性对固定资产投资、产出增速的单向因果关系。进一步地,在厘清原油价格不确定性对宏观经济影响机制的基础上,利用包含马尔可夫区制转移特征的向量自回归模型考察原油价格不确定性对我国宏观经济的影响,理论与实证结果均表明原油价格不确定性对我国宏观经济具有显著负向影响;在不同经济状态下,原油价格不确定性对投资、产出增速的影响具有非对称性。本研究为有效防范原油市场风险、保障能源安全和推动经济稳定增长提供了理论支持。

美联储降息对国际油价的历史影响与现实启示

在利率机制、汇率机制及信息效应与市场预期等传导作用下,美联储降息政策对国际石油市场产生溢出效应,造成国际油价波动。通过构建VAR模型和脉冲响应分析美联储降息政策与国际原油价格的关系。考虑到WTI原油期货合约于1983年上市,以1983年3月30日—2024年1月23日WTI原油日度价格的时间序列数据为样本,结合期间6次美联储降息周期,进行历史规律分析。美联储的“缓步式降息”“骤降式降息”“预防式降息”策略对国际油价的影响各不相同。当前,在美国通胀放缓、美国经济存在潜在衰退风险的背景下,2024年内美联储可能开启降息周期,短期内国际油价可能先跌后涨,长期将逐步恢复到正常区间。建议多措并举应对油价波动风险,依托上海原油期货规避汇率风险。

成品油“期货稳价订单”模式的套期保值效率研究——以柴油和汽油为例

“期货稳价订单”模式对于稳定能源化工产业链的大宗商品价格、保障能源化工供应链安全具有重要意义。研究“期货稳价订单”模式下的上海原油期货和柴油、汽油产品的跨品种套期保值效率,以2018年9月到2022年7月上海原油期货价格和柴油、汽油现货价格为样本,使用OLS、B-VAR、ECM、BEKK-GARCH模型计算套期保值效率,得到最优套期保值比率。研究发现,风险管理子公司可以通过参与“期货稳价订单”降低风险;柴油、汽油日数据中基于BEKK-GARCH模型计算得出的动态套期保值比率效果最优,周度、月度数据中基于OLS模型的效果最优;套期保值效率随着样本期限的拉长会逐渐提高,套期保值期限管理是降低成品油价格波动风险的重要渠道;通过汽油和柴油的对比研究,增强了回归结果的可信度,为该模式未来扩充油气业务品种提供了参考依据。