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.

Analysis on the spatial pattern and evolution of China's petroleum trade under the dual effect of international oil price and “Belt and Road” Framework

“Belt and Road” is the important origin of oil import in China. Based on social network analysis and stochastic frontier gravity model, this paper studied the characteristic evolution and influence factor of oil import network between China and “Belt and Road” countries. Then by constructing a stochastic frontier gravity model including the crude oil future price and oil importing price, it found that the international crude oil future price, the oil importing price, the political situation, the trade agreements have the effects on the China's oil import from “Belt and Road” region. It provided suggestions for improving the spatial pattern of China's petroleum trade.

Dynamic interacting relationships among international oil prices, macroeconomic variables and precious metal prices

From the perspective of long-term and short-term, the methods of TY causality test, generalized impulse response function, variance decomposition were used to investigate the impacts of international oil prices and macroeconomic variables on Chinese gold, silver and platinum prices, but also the feedback effects of Chinese precious metal prices under this impact. The results show that international oil prices play an important role in precious metal price variation both in long-term and short-term, and exchange rate only has an effect in short-term, while interest rate is ineffective in predicting precious metal prices. In addition, precious metal prices have some feedback effects on international oil prices and interest rate in short-term.

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.

The Effects of Oil Price Volatility on Some Macroeconomic Variables in Nigeria： Application of Garch and Var Models

The price of Nigeria＇s premium crude, the Bonny light has declined by about 51.8 percent between September 2014 and January 2015. Given that this resource is the major source of revenue for Nigeria, the possible effects on the economy of these continuing shocks in oil prices are definitely of prime interest in order to predict the effects of a drastic change in oil prices, on the Nigerian economy as a whole. This study investigates the impact of oil price shocks on the Nigerian economy using quarterly time series data from 1985Q2-2014Q3. The study employed GARCH model and a multivariate VAR analysis using impulse response functions and variance decompositions tests to examine the interrelationship among the variables. The impulse response functions show that oil price shocks have immediate and prolonged effect on all the macroeconomic variables considered. Thus, we conclude that oil price shocks have a direct impact on real GDP, total monetary assets and credit to private sector and as such urgent and serious efforts should be made to cut back on government expenditure, increase the tax base, diversify the economy and improve the overall efficiency and scope of other existing non-oil revenue sources, so as to ameliorate the impact of falling oil prices.

Dynamics of oil price shocks and stock market behavior in Pakistan:evidence from the 2007 financial crisis period

Background:The aim of this study is to investigate the effect of the oil price and its volatility on the stock market of Pakistan before and after the 2007 financial crisis period.Methods:The analyses are carried out on daily data for the period from July 31,2000 to July 31,2014.This study uses several econometric techniques for the analyses,namely,the Johansen-Juselius cointegration test,generalized autoregressive conditional heteroskedasticity(GARCH)model,exponential generalized autoregressive conditional heteroskedasticity(EGARCH)model,variance decomposition method,and impulse response function.Results:The results of the cointegration method indicate a significant long-run association between stock market and oil prices in the pre-crisis period.The EGARCH model shows that oil price returns have a significant effect on stock market returns in both sub-periods,while the result for the GARCH model is significant only in the postcrisis period.We find a significant effect of oil price volatility on the stock market in both sub-periods from the GARCH model.Furthermore,the EGARCH model shows an asymmetric effect of oil price volatility on the stock market in the pre-crisis period.Variance decomposition shows that stock market variations are mostly explained by selfinnovation.Moreover,the impulse response function results show that oil price shocks affected the stock market adversely in the pre-crisis period but positively in the postcrisis period.Conclusions:This study suggests that economic policymakers and investors should consider the oil price as an important factor affecting stock market returns.

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.

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.

The time‑varying effects of oil prices on oil-gas stock returns of the fragile five countries

This study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020.The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in the oil and stock markets over the period under study.Moreover,the time-varying estimates indicate that the oil–gas sectors of these countries are riskier than the overall stock market.The results further suggest that,except for Indonesia,oil prices have a positive impact on the sectoral returns of all markets,whereas the impact of the exchange rates on the oil–gas sector returns varies across time and countries.

Accuracy comparison of short-term oil price forecasting models

A forecasting model of the monthly crude oil price is investigated using the data between 1988 and 2009 from U. S. Energy Information Administration. First generalized auto-regressive condi- tional beteroskedasticity （GARCH） is applied to a state space model, a hybrid model （SS-GARCH） is proposed. Afterwards by computing a special likelihood function with two weak assumptions, model parameters are estimated by means of a faster algorithm. Based on the SS-GARCH model with the identified parameters, oil prices of next three months are forecasted by applying a Kalman filter. Through comparing the results between the SS-GARCH model and an econometric structure model, the SS-GARCH method is shown that it improves the forecasting accuracy by decreasing the index of mean absolute error （ RMSE ） from 7. 09 to 2.99, and also decreasing the index of MAE from 3. 83 to 1.69. The results indicate that the SS-GARCH model can play a useful role in forecasting short-term crude oil prices.

Low cost development strategy for oilfields in China under low oil prices

By reviewing the challenges in the development of oilfields in China under low oil prices,this study analyzes the root causes of cost rising,put forwards the low cost oilfield development strategy and specific paths to realize the strategy,and predicts the development potential and prospect of oilfields in China.In addition to the low grade of the reservoir and high development maturation,the fundamental reasons of development full cost rising of oilfields in China are as follows:(1)Facing the problem of resources turning poorer in quality,we have built production capacity at a pace too fast before making enough technical and experimental preparation;(2)technical engineering service model leads to high service cost;(3)team of oil development expertise and matched engineering system cannot satisfy the technical requirements of stabilizing oil production,controlling water cut and fine development.To realize development at low cost,the core is to increase economic recoverable reserves.The concrete paths include:(1)to explore the"Daqing oilfield development culture",improve the ability of leaders in charge of development,and inspire potential of staff;(2)to improve the ability of reservoir dynamics control,and implement precise development by following scientific principles;(3)to speed up integration of water flooding and enhanced oil recovery(EOR)and technological upgrading in order to enhance oil recovery;(4)to innovate key techniques in gas flooding and accelerate the industrial popularization of gas flooding;(5)to break the related transaction barriers and create new management models;and(6)to collaboratively optimize strategic layout and cultivate key oil bases.Although oilfield development in China faces huge challenges in cost,the low-cost development strategy will succeed as long as strategic development of mature and new oil fields is well planned.The cores to lower cost are to control decline rate and enhance oil recovery in mature oil fields,and increase single well productivity through technical innovation and improve engineering service efficiency through management innovation in new oil fields.

Has COVID‑19 changed the stock return‑oil price predictability pattern?

In this paper,we examine if COVID-19 has impacted the relationship between oil prices and stock returns predictions using daily Japanese stock market data from 01/04/2020 to 03/17/2021.We make a novel contribution to the literature by testing whether the COVID-19 pandemic has changed this predictability relationship.Employing an empirical model that controls for seasonal effects,return-related control variables,heteroskedasticity,persistency,and endogeneity,we demonstrate that the influence of oil prices on stock returns declined by around 89.5%due to COVID-19.This implies that when COVID-19 reduced economic activity and destabilized financial markets,the influence of oil prices on stock returns declined.This finding could have implications for trading strategies that rely on oil prices.

Empirical Analysis of ARCH Family Models on Oil Price Fluctuations

This paper selects the daily data of national oil prices from January 2, 2014 to February 28, 2019, establishes an ARMA (2, 0) model, and tests its residuals for ARCH effects. Finally, the TARCH (1, 1) model is determined to quantitatively analyze the volatility of the crude oil market.

Impact of Oil Price Fluctuations on Returns of Different Sectors of Malaysian Stock Market

Oil price fluctuations affect equity values in North American, European, and Gulf Cooperation Council （GCC） stock markets, as evidenced by prior studies. However, they only focus on market-wide level analysis. This study, through both market level and sector level analyses, examines the sensitivity of Malaysian stock returns to oil price fluctuations over the period from January 2000 to March 2014. A multifactor market model has been employed to capture this sensitivity. The regression results show a positive impact of oil price changes on the Financial Times Stock Exchange Kuala Lumpur Composite Index （FTSE KLCI） market return. Consumer staples and energy sector index returns were also positively affected by oil price changes. On the other hand, utilities and telecom services were negatively affected over the study period. Moreover, Granger causality analysis was performed to see if oil price fluctuations Granger cause the stock indices to change. With one month lag period, oil price fluctuations Granger cause consumer staple, energy, industrials, and telecommunication services return. Relevant policymakers and market caretakers （Ministry of Finance, Central Bank, and Security Commission） may use the fmdings of this study to develop and incorporate a preventive mechanism to minimize the unfavorable impacts of oil price fluctuations on different sectors of stock market, and Malaysian economy in general.

The Effect of Oil Price Shocks on the Saudi Manufacturing Sector

This paper aims to examine the effects of oil price shocks on the manufacturing sector in Saudi Arabia during the period 2002-2014, using quarterly data. The paper has conducted a unit root test. The data are shown to be non-stationary in the level, and they became stationary in the first difference for all variables. The co-integration model was applied, and the results indicated that no co-integrating equation exists, which means that there is no long run effect of oil price shocks on the manufacturing sector. Therefore, the paper estimated a VAR model, the results of which implied that oil price shocks do not affect the manufacturing sector in the short run, and it may have an effect on the manufacturing sector after 10 quarters according to the Impulse Response Function~ The recent fall of oil prices since June 2014 is just one round of a series of fluctuations, in the form of shocks, in oil prices. Nevertheless, a debate has arisen about the effect of this price falls on the world economy in general and on oil exporting countries in particular. The economy of Saudi Arabia, the major oil exporting country, is not an exception in this matter of course. The main objective of this paper is to estimate quantitatively, in the economy of Saudi Arabia, whether there exists an impact of oil price shockson the output of the manufacturing sector, and whether it is a positive （direct） or a negative （inverse） relationship. The focus on the manufacturing sector here is for two reasons. First, the Saudi economic planning and policy have long targeted to diversify the sources of generating GDP. The manufacturing sector is, no doubt, a very important element in this diversification process. Second, the increase in manufacturing sector （as a percentage of GDP） is one of the important measures and/or indicators of economic development. For both reasons, the researchers have chosen to study ＂the effect of oil price shocks on the Saudi manufacturing sector＂ in this paper.

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.

The Changes in World Oil Prices, Monetary Factors, and Foreign Index Toward Composite Index Movement： Indonesian Case for the Period of 2005-2011

Capital market is one of the drivers of the economy through the formation of capital investor excess as well as an indicator of a country＇s economy. Movement of stock price index is often influenced by many factors, derived from the company＇s performance, monetary factor, and changes in world oil prices. This study highlights the problem in world oil prices due to political turmoil in the Middle East. The samples are taken from the Jakarta Composite Stock Price Index （JCI）, oil prices, Indonesian inflation rate, Certificate of Bank Indonesia＇s （CBI） rate, and the reserve assets, during the period from January 2005 to December 2011 （84 months）. Using the data published by the Bank of Indonesia, reports of the Central Bureau of Statistics （Biro Pusat Statistik, BPS）, and other relevant sources, the data analyzed through the Eviews 7.1. The main objective of this study is to examine the effect of oil prices, foreign stock price index, and monetary variables （inflation rate, CBI rate, country＇s foreign reserves, and others） toward the JCI analyzed through the error correction model （ECM）. Hypothesis testing with the F-test for the 95% confidence level indicates that the oil price, exchange rate （Indonesian Rupiah （IDR）/United States Dollar （USD））, CBI rate, foreign exchange reserves, the Dow Jones Index, and the Taiwan stock index, both simultaneously as well as partially have a significant influence on the JCI.

Non-Linear Dependence in Oil Price Behavior

In this paper, the authors analyze the adequacy of GARCH-type models to analyze oil price behavior by applying two types of non-parametric tests, the Hinich portmanteau test for non-linear dependence and a frequency-dominant test of time reversibility, the reverse test based on the bispectrum, to explore the high-order spectrum properties of the Mexican oil price series. The results suggest strong evidence of a non-linear structure and time irreversibility. Therefore, it does not comply with the i.i.d （independent and identically distributed） property. The non-linear dependence, however, is not consistent throughout the sample period, as indicated by a windowed test, suggesting episodic nonlinear dependence. The results imply that GARCH models cannot capture the series structure.

Strategy for China's Natural Gas Industry in the Low Oil Price Environment

In regard to the continuous decline of international oil prices in recent years, how should China's natural gas industry seize the opportunity brought by this round of energy price declines, and how should we meet the challenges posed to the development of the domestic natural gas industry caused by changes in the international energy pattern? These questions deserve serious consideration. Through analysis on the situation of the domestic and overseas oil and gas market, and in combination with China's 13 th Five-Year Plan for energy development, the article proposes the development strategy in response to the low oil prices, aiming to provide countermeasures and suggestions for the long-term stable development of China's natural gas industry.

Opportunities and Risks of Natural Gas Investment in China under the Circumstance of Low Oil Prices

After more than 30 years of rapid growth, the Chinese economy has entered the "new normal" of moderately high growth. Due to the effects of multiple factors, the international oil price has remained consistently low. The low oil price has exerted critical effects on international natural gas investment. At the same time, the market-oriented price mechanism of natural gas in China is gradually taking shape; the concept of low carbon development is widely advocated; and the use of natural gas gains popularity in the city. Such factors provide great opportunities for investment in the natural gas market of China, including boiler coal-to-gas transformation, natural gas distributed energy and natural gas vehicles. However, risks also exist, such as the lower competitiveness of natural gas, its excess production capacity and dwindling consumption in some gas consumption industries, an insufficient driving force for facilitating the coal-to-gas transformation of industrial fuel users, reverse substitution of "coal in place of gas" in some enterprises, nontransparent costs of the downstream link of the natural gas price chain, and mismatches and nonsynchronous adjustments in natural gas prices and electricity prices.