Oil convenience yields estimated under demand/supply shock

Grounded in the Granger causality test, vector autoregression (VAR) model, and BEKK-GARCH model, our current study aims to examine the effect of mean and volatility spillover between the United States (US) economic policy uncertainty (EPU) and West Texas Intermediate (WTI) crude oil price. Using the US EPU monthly index and WTI spot price data from 1996 to 2019, we revealed that there is a one-way Granger causality link between the US EPU and spot price of WTI crude oil. The VAR model not only illustrated that there is a mean spillover effect between WTI oil price and US EPU, but they will also be affected by its memory, as well as the other’s past. At the same time, it also pointed out that this correlation has positive and negative directions. The BEKK-GARCH model test yielded similar conclusions to the VAR model and, importantly, proved a two-way volatility spillover effect between the US EPU and WTI spot price fluctuations. In conclusion, US economic policy has a substantial influence on the variation of global crude oil prices, as an essential strategic reserve resource and will also influence the government’s economic policy formulation. Understanding the association between WTI crude oil price and policy uncertainty not only helps investors to manage assets allocations and mitigate losses but also guides US policymakers to adjust the energy structure for economic sustainability.

In recent years, the rapid increase in CO2 concentration has accelerated global warming. As a result, sea levels rise, glaciers melt, extreme weather occurs, and species become extinct. As the world’s largest CO2 emission rights trading market, EU Emissions Trading System (EU-ETS) has reached 1.855 billion tons of quotas by 2019, influencing the development of the global carbon emission market. Crude oil, as one of the major fossil energy sources in the world, its price fluctuation is bound to affect the price of carbon emission rights. Therefore, this paper aims to reveal the correlation between crude oil futures prices and carbon emission rights futures prices by studying the price fluctuation. In this paper, the linkage between West Texas Intermediate (WTI) crude oil futures prices and European carbon futures prices was investigated. In addition, this paper selects continuous data of WTI crude oil futures prices and spot prices with European carbon futures prices from January 8, 2018 to November 27, 2020, and builds a smooth transformation regression (STR) model. The relationship between crude oil futures and carbon futures prices is studied in both forward and reversal linkage through empirical analysis. The results show that crude oil futures prices and carbon futures prices have a mutual effect on each other, and both linear and nonlinear correlations between the two prices exist. Based on the results of this research, some suggestions are provided.

For the last 50 years or so, the efficient market hypothesis (EMH) has been the central pillar of economic thought and the building block of portfolio theory. However, the validity of the EMH still remains controversial, and the methods of testing for market efficiency have been criticised. This study utilises entropy analysis combined with generalised autoregressive conditional heteroscedasticity (GARCH) regressions to provide extensive empirical evidence on informational efficiency. This study develops an innovative method for studying market efficiency by investigating the complexity of information saturated in the market. To this end, we have used daily West Texas Intermediate (WTI) crude oil futures contracts from June 1983 to February 2016, giving a total data set of 282,427 observations. The results of the Jensen–Shannon informational criteria indicate that the informational efficiency measure has never reached the maximum value of 1 which indicates weak-form efficiency. Our study has further found that the informational efficiency measure tends to decrease consistently during recessionary periods and to increase in subsequent periods. We have also found that the entropy measure tends to increase over time, supporting the adaptive market hypothesis over the EMH.

Global commodity markets, particularly crude oil markets, are inherently characterized by pronounced price volatility driven by geopolitical tensions, macroeconomic shocks, financial speculation, and structural changes in energy demand and supply.Accurately modeling and forecasting such volatility is essential for policymakers, investors, and risk managers seeking to design effective stabilization, hedging, and investment strategies.This study provides a comprehensive empirical investigation of oil price volatility by applying three prominent members of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family-namely the standard GARCH, Threshold GARCH (TGARCH), and Exponential GARCH (EGARCH) models-to monthly West Texas Intermediate (WTI) crude oil prices over the period from January 2008 to June 2024.The selected timeframe captures multiple episodes of extreme market turbulence, including the global financial crisis, oil price collapses, the COVID-19 pandemic, and recent geopolitical disruptions.The analysis proceeds through a structured econometric methodology, beginning with the transformation of price levels into return series, followed by stationarity testing, descriptive statistical analysis, and volatility modeling.Empirical results reveal strong volatility clustering and persistence in WTI oil returns, confirming the suitability of GARCH-type models for capturing oil price dynamics.While asymmetric specifications (TGARCH and EGARCH) provide valuable insights into the differential effects of positive and negative shocks, the findings indicate that the GARCH(1,1) model offers the most 207 -www.imcra.az.org,

Time-varying effects of global economic policy uncertainty shocks on crude oil price volatility：New evidence

The time-varying causality between spot and futures crude oil prices: A regime switching approach

The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthly West Texas Intermediate (WTI) crude oil series from FRED, commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect.

Crude oil is an important energy commodity to mankind. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to raise sky-rocket. In this study, daily West Texas Intermediate (WTI) crude oil prices data is obtained from Energy Information Administration (EIA) from 2nd January 1986 to 30th September 2009. This study uses the Box-Jenkins methodology and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) approach in analyzing the crude oil prices. ARIMA(1,2,1) and GARCH(1,1) are found to be the appropriate models under model identification, parameter estimation, diagnostic checking and forecasting future prices. In this study, the analyses are done with the aid of EViews software where the potential of this software in forecasting daily crude oil prices time series data is explored. Finally, using several measures, comparison performances between ARIMA(1, 2, 1) and GARCH(1,1) models are made. GARCH(1,1) is found to be a better model than ARIMA(1, 2, 1) model. Based on the study, it is concluded that ARIMA(1,2,1) model is able to produce good forecast based on a description of history patterns in crude oil prices. However, the GARCH(1,1) is the better model for daily crude oil prices due to its ability to capture the volatility by the non-constant of conditional variance.

THE INVISIBLE COST OF PIPELINE CONSTRAINTS Over much of the last decade pipeline constraints and the resulting apportionment of pipeline capacity have meant reduced returns on Alberta’s Oil Exports. There is a natural price discount between the US benchmark West Texas Intermediate (WTI) Crude oil price and the Canadian benchmark Western Canada Select (WCS) price. This differential reflects the lower quality of WCS relative to WTI and the costs associated with pipeline tolls to transport this oil from Alberta to US refining hubs. However, at present western Canada is experiencing significant pipeline capacity constraints which have dramatically increased this discount relative to historical levels

Energy commodity price forecasts have always been quite important to a lot of market participants. To tackle the problem, our analysis looks at West Texas Intermediate (WTI) crude oil prices on a daily basis. The sample under inquiry covers 10 years, from April 4, 2014 to April 3, 2024, and the price series under analysis has major financial implications. Here, Gaussian process regression methods are developed using Bayesian optimization techniques and cross-validation processes, and the resulting strategies are utilized to provide price projections. The relative root mean square error of 2.2743% indicates that our empirical prediction approach produces reasonably accurate price estimates for the out-of-sample assessment period of April 19, 2022–April 3, 2024. Models of price predictions give investors and governments the information they need to make wise choices about the crude oil market.

Crude oil is a non-renewable natural resource. It is one of the world's most important natural resources since it has many utilities. One of the best parameters for crude oil price is West Texas Intermediate (WTI). It is the main oil benchmark for North America sourced from the United States. It is important to predict WTI price since the price of crude oil will impact the price of other commodities, which also will impact human life. In this work, the Autoregressive Integrated Moving Average (ARIMA) approach is used to predict WTI price for the next 12 months with the monthly data from April 1, 2019, to April 1, 2023, which was collected from investing.com. The results ARIMA (1,2,1) with the best model for forecasting WTI price with accuracy value from error measurements MSE is 38,129117536, MAPE is 6,8128441%, and RMSE is 6,174882619. This forecasting will be useful as a consideration for various stakeholders and the government.

Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or topic models to extract text features without profoundly exploring the event information. In this study, a novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events, and a text sentiment analysis algorithm is used to extract sentiment from massive news. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices. Empirical experiments are performed on West Texas Intermediate (WTI) crude oil price data, and the results show that our approach obtains superior performance compared with several benchmark methods.

Unbiasedness and time varying risk premia in the crude oil futures market

This study employed intraday stock market and oil investor sentiment data related to news and social media (i.e., the Thomson Reuters MarketPsych Indices [TRMI] sentiment index) to gauge investors’ interest in the West Texas Intermediate (WTI) crude oil futures market during the recent health crisis. We proposed an original nonlinear empirical framework by considering oil price dynamics’ complexity and its potential interaction with investor sentiment. The analysis revealed three noteworthy findings. First, we observed evidence of nonlinearity in the relationship between excess returns on WTI crude oil futures and investor sentiment data. Second, the causality direction moved only from oil and stock market investor sentiment to oil returns. Third, the impacts of oil and stock market sentiment data on crude oil returns (i.e., volatility) were always negative. Furthermore, sentiment data related to social media showed a more pronounced cross-correlation than that of news.

This research aims to forecast fluctuations in the price of West Texas Intermediate (WTI) crude oil within the potential threat of World War III by utilizing the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) method. Using daily closing price data of WTI crude oil from January 2022 to March 2025, consisting of 850 observations, this study employs the ARIMA-GARCH model to capture the patterns and volatility of the time series data. Based on the conducted analysis, it is concluded that the ARIMA (2,1,2) and GARCH (1,1) models demonstrate optimal performance, with a MAPE value of 13.71%, indicating a good prediction accuracy level. The forecast for the next two years shows a trend of price increases starting from Q2 2025 through Q4 2027. This research demonstrates how geopolitical tensions, particularly the Russia-Ukraine war and Middle Eastern Conflicts, can affect global oil price volatility while highlighting the GARCH model’s effectiveness in capturing heteroskedasticity in highly fluctuating financial data.