Do U.S.economic conditions at the state level predict the realized volatility of oil‑price returns?A quantile machine‑learning approach

Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

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.

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.

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.

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.

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.

A spillover network analysis of the global crude oil market:Evidence from the post-financial crisis era

The frequent occurrence of geopolitical crises in the post-financial crisis era is driving the rethinking behind whether the global crude oil market is still a highly connected"great pool".Using the spillover network model suggested by Baruník and Krehlík(2018),and the daily data of 31 global crude oil markets from 2009 to 2019,this study examines the return and volatility spillover effects and their timevarying behavior in six crude oil market segments at different timescales.The findings indicate that heterogeneity exists in the co-movements between global crude oil markets in the post-financial crisis era.In the medium term,both return and volatility spillover effects are not significant,which makes the diversified portfolio strategy useful.Prices in the Europe and Central Asian regions take the lead in return spillovers.In contrast,Asia-Pacific regional prices contribute the most in terms of volatility spillovers.Long-term volatility spillovers increase sharply when confronted with oil-related events in the postfinancial crisis era.Therefore,policymakers should take effective measures to prevent any large-scale risk transmission in the long run.

Status, trends and enlightenment of global oil and gas development in 2021

By analyzing the distribution of global oil and gas fields and the reasons why some oil and gas fields are not in production, the distribution characteristics of oil and gas remaining recoverable reserves and their year-on-year changes, the distribution characteristics of oil and gas production and their year-on-year changes, and the development potential of oil and gas to be tapped in 2021, this paper sorts out systematically the current status and characteristics of global oil and gas development, summaries the major trends of global oil and gas development, puts forward enlightenment for international oil and gas cooperation. In 2021, oil and gas fields were widely distributed, the number of non-producing oil and gas fields was large;the whole oil and gas remaining recoverable reserves declined slightly, unconventional oil and gas remaining recoverable reserves dropped significantly;the overall oil and gas production continuously increased, the outputs of key resource-host countries kept year-on-year growth;undeveloped oilfields had abundant reserves and great development potential. Combined with global oil and gas geopolitics, oil and gas industry development trends, oil and gas investment intensity, and the tracking and judgment of hotspot fields, the major trends of global oil and gas development in 2021 are summarized. On this basis, the four aspects of enlightenment and suggestions for international oil and gas cooperation and development strategies are put forward: attach great importance to the obligation of marine abandonment to ensure high-quality and long-term benefit development of offshore oil and gas;adhere to the principle of not going to dangerous and chaotic places, strengthen the concentration of oil and gas assets, and establish multi stable supply bases;based on the multi-scenario demand of natural gas, realize the transformation from integrated collaboration to full oil and gas industry chain development;increase the acquisition of high-quality large-scale assets, and pay attention to the continuous optimization of the shareholding ratio of projects at different stages.

Spillover of international crude oil prices on China's refined oil wholesale prices and price forecasting:Daily-frequency data of private enterprises and local refineries

Compared with retail prices of state-owned companies used in almost all existing studies,China’s refined oil wholesale prices of private enterprises and local refineries are more affected by the market and better reflect the real supply-demand situation.For the first time,this paper applies own-monitored dailyfrequency wholesale prices of China’s private enterprises and local refineries during 2013-2020 to derive spillover effects of international crude oil prices on China’s refined oil prices through the VAR-BEKKGARCH(vector autoregression-Baba,Engle,Kraft,and Kroner-generalized autoregressive conditional heteroscedasticity)model,and then tries to forecast wholesale prices through the PCA-BP(principal component analysis-back propagation)neural network model.Results show that international crude oil prices have significant mean spillover and volatility spillover effects on China’s refined oil wholesale prices.Changes in crude oil prices are the Granger cause of changes in refined oil wholesale prices.With the improvement of China’s oil-pricing mechanism in 2016,the volatility spillover from the international crude oil market to China’s refined oil market gradually increases,and the BRENT price variation has an increasing impact on the refined oil wholesale price variation.The PCA-BP model could serve as a candidate tool for forecasting China’s refined oil wholesale 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.

Brent prices and oil stock behaviors: evidence from Nigerian listed oil stocks

Background:Given the shale oil glut that culminated in the most recent and continuing oil price drop from June 2014 and the global financial crisis of 2008 that triggered a cyclical downturn in oil prices and stock market activity,this study investigates the impact of Brent oil price shocks on oil related stocks in Nigeria.Methods:This study uses a vector autoregressive(VAR)model with the impulse response function and the forecast variance decomposition error.Findings:The empirical evidence reveals that oil price shocks have a negative impact on Nigerian oil and gas company stocks.In theory,this situation should apply to oil importing countries and is therefore uncharacteristic of an oil exporting country like Nigeria.Conclusions:The findings suggest that oil companies operating in Nigeria should diversify their investments to protect their business from single-sector market forces,and can also embrace the advantages of outsourcing some of their operations to specialist providers to increase flexibility and reduce operating costs.Finally,for vertically integrated oil and gas companies,oil price hedging and energy risk management will be beneficial because it will mean that these companies will take a position in the crude oil futures market.This will allow for better cash flow management and flexibility.Originality/value:This study extends the existing literature in two distinct ways.First,it provides,to the best of our knowledge,the first examination of the impact of oil price shocks on stock market activities with a focus on the market returns of oil and gas companies listed in the Nigerian Stock Exchange.Second,this study uses daily data because high frequency data contain more information than lower frequency data does,and lower frequency data average out too much important information.

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.

Investment in deepwater oil and gas exploration projects:a multi-factor analysis with a real options model

Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.

Markov-Switching Time-Varying Copula Modeling of Dependence Structure between Oil and GCC Stock Markets

This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing precrisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre- and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching time- varying copula model performs better than the time-varying copula model.

Oil-Price Forecasting Based on Various Univariate Time-Series Models

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

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.

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.