Reforming the Dated Brent Oil Benchmark

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,

This paper extends the call option model of Milonas and Thomadakis (1997) to estimate oil convenience yields with futures prices. We define the business cycle of a seasonal commodity with demand/supply shocks and find that the convenience yield for crude oil exhibits seasonal behavior. The convenience yield for West Texas Intermediate (WTI) crude oil is the highest in the summer, while that for Brent crude oil is the highest in the winter. This implies that WTI crude oil is more sensitive to high summer demand and that Brent crude oil is more sensitive to shortages in winter supply. Convenience yields are negatively related to the inventory level of the underlying crude oil and positively related to interest rates due to the business cycle. We also show that convenience yields may explain price spread between WTI crude oil and Brent crude oil. Our computed convenience yields are consistent with Fama and French (1988) in that oil prices are more volatile than futures prices at low inventory level, verifying the Samuelson (1965) hypothesis that future prices are less variables than spot prices at lower inventory levels.

Assessment of the relationship between oil prices and US oil stocks

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.

Modeling and forecasting crude oil markets using ARCH-type models

In energy economics literature, the controversial assertion of whether crude oil markets are regionalized (Weiner; 1991) or behave as one unified entity (Adelman; 1984) is still an unsettled debate.While the proponents of globalization hypothesis trust that the crude oil markets behave as one big pool, the advocates of regionalization believe that they are segmented.The recent years' experience with the growing price spread between West Texas Intermediate (WTI) and Brent Crude, the two most influential light crude oil benchmarks of North America and Europe respectively, the debate has specifically become a point of discussion and deliberation within international energy forums.. Globalization in crude oil markets refers to the presence of strong co-movements.On the contrary, if the markets are fragmented and display least association, they are considered to be regionalised.An insight into the nature of the global oil market is critical in understanding the dynamics of international crude oil prices as well as its economic and financial implications.

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.

The next year could spell trouble for operators producing oil in west Texas and southeastern New Mexico. The Permian Basin currently has more crude than it can handle, and production there continues to grow at a steady rate month-over-month after surpassing 3 million B/D earlier this year, according to data from the US Energy Information Administration. Pipeline takeaway capacity isn’t sufficient and won’t be until the second half of 2019. There aren’t nearly enough trucks or truck drivers to make up the difference, and any meaningful expansion of crude-by-rail transport is restricted by infrastructure and business-related constraints. Both options are already competing with movement of equipment and supplies such as sand and water, demand of which has also grown—along with their costs—to support the continued production growth. Many operators in the basin struggled to generate positive cash flow even before the bottlenecks became an issue. Now, the price of the crude they’re producing is trading at a big discount vs. spot prices in Houston, near the Gulf Coast refining and export center; and Cushing, Oklahoma, the crude trading hub from which the West Texas Intermediate (WTI) price is derived. The spread between Midland crude and WTI grew to double digits in May, spiking from roughly $1 a couple months earlier. If WTI prices were closer to where they were a year ago, the impact of the spread immediately “would have been significant for almost every operator,” noted R.T. Dukes, Wood Mackenzie US Lower 48 upstream research director. Fortunately for Permian operators, WTI prices have been much higher than the range of prices for which they budgeted coming into the year, minimizing the impact of the spread thus far. But WTI prices aren’t guaranteed to remain that high, and differentials could grow—just look at the Western Canada Select heavy crude price, which has traded at up to a $30/bbl discount to WTI this year due in large part to transportation constraints. The spread forced operators such as Cenovus, Husky Energy, and Canadian Natural Resources to slow their oil sands production during the first quarter. Permian operators are similarly being forced to examine whether continued growth over the next year is sustainable or even worthwhile. Their exposure to Midland spot prices varies, ranging anywhere from less than 5% to 50–60% of Permian production, said John Coleman, Wood Mackenzie senior analyst, North American crude oil markets. Most operators are in the 20–30% range, meaning there already has been a modest hit to cash flow, he said. Consultancy Rystad Energy believes the Midland vs. Cushing spread will “stay very much depressed” into mid-2019 and “won’t be surprised” if it moves into the low $20s/bbl, said Artem Abramov, Rystad vice president, shale analysis. “Everything points to the situation getting worse and worse going forward.”

The global crude oil market, known for its pronounced volatility and nonlinear dynamics, plays a pivotal role in shaping economic stability and informing investment strategies. Contrary to traditional research focused on price forecasting, this study emphasizes the more investor-centric task of predicting returns for West Texas Intermediate (WTI) crude oil. By spotlighting returns, it directly addresses critical investor concerns such as asset allocation and risk management. This study applies advanced machine learning models, including XGBoost, random forest, and neural networks to predict crude oil return, and for the first time, incorporates sustainability and external risk variables, which are shown to enhance predictive performance in capturing the non-stationarity and complexity of financial time-series data. To enhance predictive accuracy, we integrate 55 variables across five dimensions: macroeconomic indicators, financial and futures markets, energy markets, momentum factors, and sustainability and external risk. Among these, the rate of change stands out as the most influential predictor. Notably, XGBoost demonstrates a superior performance, surpassing competing models with an impressive 76% accuracy in direction forecasting. The analysis highlights how the significance of various predictors shifted during the COVID-19 pandemic. This underscores the dynamic and adaptive character of crude oil markets under substantial external disruptions. In addition, by incorporating sustainability factors, the study provides deeper insights into the drivers of market behavior, supporting more informed portfolio adjustments, risk management strategies, and policy development aimed at fostering resilience and advancing sustainable energy transitions.

This study examines the dynamic interplay between international crude oil prices, import dependency, and refinery input in shaping Indonesia’s national fuel oil consumption. Amid growing concerns over energy security, the research investigates how fluctuations in global oil markets and domestic refining capacity impact the country’s ability to meet energy demand sustainably. Drawing upon secondary data from authoritative sources, a multiple linear regression model was employed to quantify the relationship among the key variables: crude oil price (X₁), crude oil imports (X₂), and refinery input (X₃), with fuel consumption (Y) as the dependent variable. The findings indicate that both crude oil price and import volume positively and significantly affect fuel consumption, while refinery input shows a negative but statistically insignificant effect. The model explains approximately 65.3% of the variation in fuel consumption (R² = 0.653), suggesting a moderately strong explanatory power. Classical assumption tests revealed that the residuals are normally distributed (Shapiro-Wilk p = 0.417), although multicollinearity (VIF X₁, X₂ > 5) and autocorrelation (Durbin-Watson = 1.023) were detected, alongside heteroscedasticity in residual patterns. These insights underline the importance of managing oil import policies and refining strategies to stabilize domestic fuel supply. Moreover, the study supports the broader transition toward sustainable energy, especially through city gas development, as aligned with national and institutional research roadmaps. The study offers valuable implications for policymakers in designing resilient and adaptive energy strategies in the face of global market volatility.

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.

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.

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

THE POTENTIALThe United States imports more oil and oil products from Canada than from any other single source. It imports nearly two million barrels of oil per day, three quarters of which is crude oil, placing Canada marginally behind Saudi Arabia as a US crude supplier. Canada is the leading exporter to the US of both refined oil products and natural gas (Canada is the world's second largest exporter of natural gas, after Russia). All of Canada's natural gas exports go to the US, accounting for 87 percent of total US natural gas imports.1As oil and gas production fall in the lower 48 states, a decline matched across the border in the western Canadian sedimentary basin (WCSB), the source of most current Canadian oil and natural gas production, both countries have turned their attention to the vast potential of the Albertan tar sands. These tar sands, covering an area equivalent to both Ireland and Scotland, are located predominantly in the Cold Lake, Peace River, and Athabasca regions of Alberta. While proven conventional oil resources in Canada are estimated at a paltry 4.5 billion barrels, proven tar sands reserves are estimated at some 174 to 178 billion barrels. This places Canada second only to Saudi Arabia in terms of potential crude oil reserves. Ultimate reserves may be as high as 315 billion barrels.The Canadian-US market is unique in its integration, particularly for natural gas markets. Hydrocarbons from both countries pass freely over the Canadian-US frontier in a seamless web of pipelines, by vehicle and tanker transport. New England's natural gas demand is critical to offshore Nova Scotia. Futures contracts for gas sold at Henry Hub in Louisiana affect the overall North American price structure, playing a role similar to that for NYMEX futures contracts for West Texas Intermediate (WTI) crude sold at Cushing, Oklahoma. Thus a Wyoming refiner can purchase Saskatchewan Lloydminster grade crude at a discount from the prices quoted for WTI at Cushing, or it can purchase Mexican Mayan or Alaskan North Slope crude, both of which are priced with reference to WTI quotes. The Sable Island Consortium sells Atlantic Canada natural gas at spot prices in the northeast corridor, prices which in turn reflect the going prices at Henry Hub.Pipelines are being redirected to take account of freer North American trade. For example, the flow in the Sarnia-Montreal pipeline, designed to carry oil from the WCSB to Montreal, has been reversed from west-to-east to east-to-west, reflecting market fundamentals. In May 2004, the main federal Canadian regulatory authority, the National Energy Board (NEB), and its US counterpart, the Federal Energy Regulatory Commission (FERC) signed a memorandum of understanding committing themselves to further harmonization of their regulatory approaches to cross-border projects.After many years of teething problems, there is now progress in developing the Albertan tar sands. As can be seen from table i, current production of the various grades of tar sands-based bitumen (and synthetic crude) is one million barrels per day, and is predicted to increase to two millions per day by 2015. There are over 40 tar sands projects, projected with a cumulative investment value of Cdn. $60 billions. Virtually every major oil company or large independent from the US, Canada, or Europe is involved in some fashion with the tar sands industry. Two million barrels is roughly equivalent to 60 percent of total current Canadian production level, and 133 percent of the level of current crude exports to the United States.The integration of Canadian-US continental hydrocarbons trade would appear to be a success. How did it get that way? What are its prospects for the future?MARKET INTEGRATION: TWO VIEWSEconomists look at market integration in terms of an efficient allocation of resources and a consequent increase of the absolute gains made from trade. When these gains are viewed politically, however, there is a tension between the absolute gains from trade, and how these gains are distributed among various parties, i. …

In order to fulfill a need to measure water in crude oils containing materials that interfere with the measurement of water by the Karl Fischer method, by reacting with iodine or iodide, a coulometric method has been developed and validated using 0.1 mol L(-1) Sodium thiosulfate as a calibrant. These interfering substances were measured in water-mass-equivalents, which were expressed as the mass of water that reacts with an equal mass of iodine in the Karl Fischer method. The SO(2)-free reagent that has been modified reacts quantitatively with sodium thiosulfate, cysteine and ascorbic acid but does not react with vinyl acetate. The level of interfering substances was measured in five transformer oils (including Reference Materials RM 8506 and RM 8507), a high and a low sulfur crude oil (Standard Reference Materials SRM 2721 and SRM 2722 respectively), a white oil, a high-vacuum oil and a high-viscosity base-stock oil. One oil contained less than 10 mg kg(-1) (water-mass-equivalents of interfering substances in oil) and two oils (RM 8507 and Drakeol 35) contained no measurable amount of interfering material (<0.2 mg kg(-1)). SRM 2271, a sour crude oil contained 834 mg kg(-1) (standard deviation (SD)=25 mg kg(-1)) (water-mass-equivalents of interfering substances in oil). Approximately 20% of this material was volatile and an additional 20% appeared to undergo some degradation (possibly oxidation) once the oil was exposed to air. These results indicate that this is a general method for measuring substances in oils that react with iodine and that it is capable of measuring in a variety of oils, using commercial instrumentation, interfering substances that inflate water measurements.