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.

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.

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.

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.

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.

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.

Impacts of CME Changing Mechanism for Allowing Negative Oil Prices on Prices and Trading Activities in the Crude Oil Futures Market

This study investigates and compares the effects of the Coronavirus disease 2019(COVID-19)pandemic,the Chicago mercantile exchange(CME)'s negative price suggestion on prices and trading activities in the crude oil futures market to discuss the cause of negative crude oil futures prices.Through event studies,the empirical results show that the COVID-19 pandemic no longer impacts crude oil futures prices in April,2020 after controlled market risk,while the CME's negative prices suggestion can explain the crude oil futures price changes around and even after April 8,2020 to some degree.Moreover,this study uncovers anomalies in prices and trading activities by analyzing returns,trading volume,open interest,and illiquidity measures using vector autoregressive(VAR)models.The results imply that CME's allowing negative prices strengthens the price impact on trading volume and makes illiquidity risk matter.This study's results coincide with the following lawsuit evidence of market manipulation.

A Time-Varying Conditional Parameter Distributed Lag Model with an Application to Crude Oil Market

This paper proposes a new time-varying parameter distributed lag(DL)model.In contrast to the existing methods,which assume parameters to be random walks or regime shifts,our method allows time-varying coefficients of lagged explanatory variables to be conditional on past information.Furthermore,a test for constant-parameter DL model is introduced.The model is then applied to examine time-varying causal effect of inventory on crude oil price and forecast weekly crude oil price.Time-varying causal effect of US commercial crude oil inventory on crude oil price return is presented.In particular,the causal effect of inventory is occasionally positive,which is contrary to some previous research.It’s also shown that the proposed model yields the best in and out-of-sample performances compared to seven alternative models including RW,ARMA,VAR,DL,autoregressive-distributed lag(ADL),time-varying parameter ADL(TVP-ADL)and DCB(dynamic conditional beta)models.

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.

Exploring Long-Memory Process in the Prediction of Interval-Valued Financial Time Series and Its Application

Long-memory process has been widely studied in classical financial time series analysis,which has merely been reported in the field of interval-valued financial time series.The aim of this paper is to explore long-memory process in the prediction of interval-valued time series(IvTS).To model the long-memory process,two novel interval-valued time series prediction models named as interval-valued vector autoregressive fractionally integrated moving average(IV-VARFIMA)and ARFIMAX-FIGARCH were established.In the developed long-memory pattern,both of the short term and long-term influences contained in IvTS can be included.As an application of the proposed models,interval-valued form of WTI crude oil futures price series is predicted.Compared to current IvTS prediction models,IV-VARFIMA and ARFIMAX-FIGARCH can provide better in-sample and out-of-sample forecasts.