As of June 2025, the average annual price of Brent crude oil stood at 71.91 U.S. dollars per barrel. This is over eight U.S. dollars lower than the 2024 average. Brent is the world's leading price benchmark for Atlantic basin crude oils. Crude oil is one of the most closely observed commodity prices as it influences costs across all stages of the production process and consequently alters the price of consumer goods as well. What determines crude oil benchmarks? In the past decade, crude oil prices have been especially volatile. Their inherent inelasticity regarding short-term changes in demand and supply means that oil prices are erratic by nature. However, since the 2009 financial crisis, many commercial developments have greatly contributed to price volatility, such as economic growth by BRIC countries like China and India, and the advent of hydraulic fracturing and horizontal drilling in the U.S. The outbreak of the coronavirus pandemic and the Russia-Ukraine war are examples of geopolitical events dictating prices. Light crude oils - Brent and WTI Brent Crude is considered a classification of sweet light crude oil and acts as a benchmark price for oil around the world. It is considered a sweet light crude oil due to its low sulfur content and low density and may be easily refined into gasoline. This oil originates in the North Sea and comprises several different oil blends, including Brent Blend and Ekofisk crude. Often, this crude oil is refined in Northwest Europe. Another sweet light oil often referenced alongside UK Brent is West Texas Intermediate (WTI). WTI oil prices amounted to 76.55 U.S. dollars per barrel in 2024.

We find that oil supply shocks decrease average real wages, particularly skilled wages, and increase wage dispersion across regions, particularly unskilled wage dispersion. In a model with spatial energy intensity differences and nontradables, labor demand shifts, while explaining the response of average wages to oil supply shocks, have counterfactual implications for the response of wage dispersion. Only an additional response in labor supply can explain this latter fact, highlighting the importance of general equilibrium effects in a spatial context. We provide additional empirical evidence of regionally directed worker reallocation and housing prices consistent with our spatial model. Finally, we show that a calibrated version of our model can quantitatively match the estimated effects of oil supply shocks.

This data set is used in the Master's thesis: "A Comparison of Price Fluctuations Between Brent Crude Oil and Retail Fuel Prices in Stavanger - An Algorithmic Model for Refueling" by Ola Nes (2021) The data set contains the fuel prices collected (Excel and CSV files), and the Python code which contains all functions used in the thesis. Abstract for thesis: "This thesis investigates and compares the volatility in the retail fuel market in Stavanger and Brent crude oil. Gasoline and diesel prices have been collected from gas stations in Stavanger in 2020 and 2021, and are used for the thesis’ main goal of developing an algorithmic mathematical model for refueling vehicles at optimal times for consumers that could be used in practice. The collected data suggests that there is higher volatility in the retail fuel market in Stavanger compared to the Brent crude oil market. Gas stations follow a characteristic Edgeworth cycle pattern that have price spikes occur when restarting their price cycles. These occur for the most part at the same time across all gas stations monitored in Stavanger. This pattern can be difficult for consumers to predict. Therefore, a practical refueling algorithm could be useful. There are many factors that go in to such a model to make it efficient such as price spike analysis from the Edgeworth cycle pattern found in retail fuel markets and estimating volatility using GARCH(1,1) method."

This study investigates the impact of oil price uncertainty (OPU) on corporate profitability in China, the world’s largest crude oil consumer. Most importantly, we examine how the Chinese government’s oil price reform affects this relationship. Using the yearly data of Chinese-listed companies, we find that the uncertainty of oil prices negatively affects corporate profitability but positively impacts operating expenses from 2007 to 2020. This finding holds after robust tests, including alternative profitability metrics and endogeneity model. Most interestingly, implementing the 2013 market-oriented oil pricing reform amplifies the adverse impact of OPU on corporate profitability owing to increased operating costs in the post-2013 period. Moreover, the detrimental effect of uncertain oil prices on corporate profitability is less prominent for large-capitalized companies. This research adds to the body of knowledge on the factors affecting corporate profitability by highlighting the volatility effect of oil prices and government pricing mechanisms. The results offer grounds for legislators and corporate managers to consider how to control the uncertainty surrounding oil price matters to ensure stable corporate profitability.

Deterministic and stochastic are two methods for modeling of crude oil and bottled water market. Forecasting the price of the market directly affected energy producer and water user.There are two software, Tableau and Python, which are utilized to model and visualize both markets for the aim of estimating possible price in the future.The role of those software is to provide an optimal alternative with different methods (deterministic versus stochastic). The base of predicted price in Tableau is deterministic—global optimization and time series. In contrast, Monte Carlo simulation as a stochastic method is modeled by Python software. The purpose of the project is, first, to predict the price of crude oil and bottled water with stochastic (Monte Carlo simulation) and deterministic (Tableau software),second, to compare the prices in a case study of Crude Oil Prices: West Texas Intermediate (WTI) and the U.S. bottled water. 1. Introduction Predicting stock and stock price index is challenging due to uncertainties involved. We can analyze with a different aspect; the investors perform before investing in a stock or the evaluation of stocks by means of studying statistics generated by market activity such as past prices and volumes. The data analysis attempt to identify stock patterns and trends that may predict the estimation price in the future. Initially, the classical regression (deterministic) methods were used to predict stock trends; furthermore, the uncertainty (stochastic) methods were used to forecast as same as deterministic. According to Deterministic versus stochastic volatility: implications for option pricing models (1997), Paul Brockman & Mustafa Chowdhury researched that the stock return volatility is deterministic or stochastic. They reported that “Results reported herein add support to the growing literature on preference-based stochastic volatility models and generally reject the notion of deterministic volatility” (Pag.499). For this argument, we need to research for modeling forecasting historical data with two software (Tableau and Python). In order to forecast analyze Tableau feature, the software automatically chooses the best of up to eight models which generates the highest quality forecast. According to the manual of Tableau , Tableau assesses forecast quality optimize the smoothing of each model. The optimization model is global. The main part of the model is a taxonomy of exponential smoothing that analyzes the best eight models with enough data. The real- world data generating process is a part of the forecast feature and to support deterministic method. Therefore, Tableau forecast feature is illustrated the best possible price in the future by deterministic (time – series and prices). Monte Carlo simulation (MCs) is modeled by Python, which is predicted the floating stock market index . Forecasting the stock market by Monte Carlo demonstrates in mathematics to solve various problems by generating suitable random numbers and observing that fraction of the numbers that obeys some property or properties. The method utilizes to obtain numerical solutions to problems too complicated to solve analytically. It randomly generates thousands of series representing potential outcomes for possible returns. Therefore, the variable price is the base of a random number between possible spot price between 2002-2016 that present a stochastic method.

About the ProjectWe developed the KAPSARC Energy Model for Saudi Arabia (KEM-SA) to understand the dynamics of the country’s energy system. It is a partial equilibrium model formulated as a mixed complementarity problem to capture the administered prices that permeate the local economy. KEM-SA has been previously used to study the impacts of various industrial fuel pricing policies and improved residential efficiency on the energy economy. The passenger transportation model presented in this paper helps understand more of the end-use energy demand.Key PointsIn 2016, policymakers in Saudi Arabia increased domestic transportation fuel prices, which are expected to approach market levels in the near future. Current low crude oil prices offer an excellent opportunity for policymakers to deregulate the passenger transportation sector without a significant change in local fuel prices. We developed a bottom-up transportation sub-model and integrated it with the KAPSARC Energy Model (KEM) to assess whether consumers could afford such reforms; and the resulting travel mode choices, energy consumption levels and revenue. We do not consider price-induced efficiency improvements; hence, the results would represent an upper bound for the shift to public modes.Despite a deregulation of the passenger transportation sector, Saudi households would continue to allocate one of the lowest transport budgets (as a percentage of income) in Gulf Cooperation Council (GCC) countries and also stay within Saudi Arabian historical boundaries.Deregulating fuel prices would encourage consumers to travel by more efficient public transport modes, as they become available in the near future, leading to significant energy savings and CO2 emissions reductions of between 4 million to 26 million metric tons (mt) per year.The Kingdom would receive an annual average $8.2 billion as additional revenue from domestic sales and exports in the varying crude price scenario and $5 billion in the fixed $60/bbl scenario.Despite the increase in transport fuel price, the net gain for Saudi Arabia in the varying crude oil price scenario remains positive as a result of substantial increase in revenue and the introduction of more convenient public travel modes.Our findings show that analyzing energy policies using empirical estimates are generally valid even for large variations in price; however, if new transport modes and technologies are introduced in Saudi Arabia, consumer response may be slightly greater than that of empirical estimate, which did not account for such new modes.

Complete replication and data package. Abstract: This paper proposes methods to include information from the underlying nominal daily series in model-based forecasts of average real series. We apply these methods to forecasts of the real price of crude oil. Models utilizing information from daily prices yield large forecast improvements and, in some cases, almost halve the forecast error compared to current specifications. We demonstrate for the first time that model-based forecasts of the real price of crude oil can outperform the traditional random walk forecast, that is the end-of-month no-change forecast, at short forecast horizons.

The primary data used in the paper are the published weekly price of Ron97 (CompareHero.my, 2020; Malaysia, 2021; The Ministry of Domestic Trade and Consumer Affairs, 2020) , daily crude oil price in barrels (WTI, BRENT and OPEC) (EIA, 2020; OPEC, 2020) and daily foreign exchange rate (Selling rate) of the Ringgit per US dollars (Bank Negara, 2020). The data is pre-processed to clean the data and standardize the data for the modelling process. The crude oil price and foreign exchange rates are converted to weekly averages. Daily missing data are replaced with weekly averages. The foreign exchange rates, (G) in Ringgits per US dollar(RM/USD) (Bank Negara, 2020), is multiplied with the international crude oil price (X) to convert ringgits per barrel( RM/bbl), (I), using Equation(2). The crude oil price are converted to ringgit per litre using the Barrel to Litre Metric Conversion (M)(Ltd, 2020), as shown in Equation(3).

Comparative quadratic regression analysis (control variables included in the model).

Context

Abstract of associated article: This paper contributes to the debate on the role of oil prices in predicting stock returns. The novelty of the paper is that it considers monthly time-series historical data that span over 150years (1859:10–2013:12) and applies a predictive regression model that accommodates three salient features of the data, namely, a persistent and endogenous oil price, and model heteroscedasticity. Three key findings are unraveled: first, oil price predicts US stock returns. Second, in-sample evidence is corroborated by out-sample evidence of predictability. Third, both positive and negative oil price changes are important predictors of US stock returns, with negative changes relatively more important. Our results are robust to the use of different estimators and choice of in-sample periods.

Acknowledgements

Narayan, Paresh K. (2016), “Data for: Has oil price predicted stock returns for over a century? ”, Mendeley Data, V1, doi: 10.17632/7s446mxhyv.1

License - CC by NC 3.0

Abstract (en): Some analysts and economists recently warned that the United States economy faces a much higher risk of recession should the price of oil rise to $100 per barrel or more. In February 2008, spot crude oil prices closed above $100 per barrel for the first time ever, and since then they have climbed even higher. Meanwhile, according to some surveys of economists, it is highly probable that a recession began in the United States in late 2007 or early 2008. Although the findings in this paper are consistent with the view that the United States economy has become much less sensitive to large changes in oil prices, a simple forecasting exercise using Hamilton's model augmented with the first principal component of 85 macroeconomic variables reveals that a permanent increase in the price of crude oil to $150 per barrel by the end of 2008 could have a significant negative effect on the growth rate of real gross domestic product in the short run. Moreover, the model also predicts that such an increase in oil prices would produce much higher overall and core inflation rates in 2009 than most policymakers expect. A zipped package contains a programming syntax file (text format) and a Microsoft Excel file, which contains the data, tables, and corresponding figures used in the article.These data are part of ICPSR's Publication-Related Archive and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigators if further information is desired.

This dataset contains both time series describing the global crude oil market (prices, production, inventories, global demand) as well as Matlab/R scripts that allows to replicate the results described in the article "Modelling oil consumption in Baumeister and Hamilton’s (2019) model of the global oil market" published in Economics Letters. The detailed instruction on how to use the attached codes is presented in the readme.txt file.

This study investigates the impact of oil price uncertainty (OPU) on corporate profitability in China, the world’s largest crude oil consumer. Most importantly, we examine how the Chinese government’s oil price reform affects this relationship. Using the yearly data of Chinese-listed companies, we find that the uncertainty of oil prices negatively affects corporate profitability but positively impacts operating expenses from 2007 to 2020. This finding holds after robust tests, including alternative profitability metrics and endogeneity model. Most interestingly, implementing the 2013 market-oriented oil pricing reform amplifies the adverse impact of OPU on corporate profitability owing to increased operating costs in the post-2013 period. Moreover, the detrimental effect of uncertain oil prices on corporate profitability is less prominent for large-capitalized companies. This research adds to the body of knowledge on the factors affecting corporate profitability by highlighting the volatility effect of oil prices and government pricing mechanisms. The results offer grounds for legislators and corporate managers to consider how to control the uncertainty surrounding oil price matters to ensure stable corporate profitability.

This study investigates the impact of oil price uncertainty (OPU) on corporate profitability in China, the world’s largest crude oil consumer. Most importantly, we examine how the Chinese government’s oil price reform affects this relationship. Using the yearly data of Chinese-listed companies, we find that the uncertainty of oil prices negatively affects corporate profitability but positively impacts operating expenses from 2007 to 2020. This finding holds after robust tests, including alternative profitability metrics and endogeneity model. Most interestingly, implementing the 2013 market-oriented oil pricing reform amplifies the adverse impact of OPU on corporate profitability owing to increased operating costs in the post-2013 period. Moreover, the detrimental effect of uncertain oil prices on corporate profitability is less prominent for large-capitalized companies. This research adds to the body of knowledge on the factors affecting corporate profitability by highlighting the volatility effect of oil prices and government pricing mechanisms. The results offer grounds for legislators and corporate managers to consider how to control the uncertainty surrounding oil price matters to ensure stable corporate profitability.

Five frequently-used models were chosen and evaluated to calculate the viscosity of the mixed oil. Totally twenty mixed oil samples were prepared with different ratios of light to crude oil from different oil wells but the same oil field. The viscosities of the mixtures under the same shear rates of 10 s**-1 were measured using a rotation viscometer at the temperatures ranging from 30°C to 120°C. After comparing all of the experimental data with the corresponding model values, the best one of the five models for this oil field was determined. Using the experimental data, one model with a better accuracy than the existing models was developed to calculate the viscosity of mixed oils. Another model was derived to predict the viscosity of mixed oils at different temperatures and different values of mixing ratio of light to heavy oil.

Vietnam IPI: YoY: GSO Calculation: MQ: Crude Oil data was reported at -15.790 % in Oct 2018. This records a decrease from the previous number of -14.150 % for Sep 2018. Vietnam IPI: YoY: GSO Calculation: MQ: Crude Oil data is updated monthly, averaging -12.685 % from Jan 2018 (Median) to Oct 2018, with 10 observations. The data reached an all-time high of -7.520 % in Feb 2018 and a record low of -15.790 % in Oct 2018. Vietnam IPI: YoY: GSO Calculation: MQ: Crude Oil data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.B009: Industrial Production Index: VSIC 2007: 2015=100: YoY Growth: GSO Calculation.

Infant Formula Oil Market Outlook

The global infant formula oil market size is projected to reach USD 2.5 billion by 2032, up from USD 1.2 billion in 2023, growing at a CAGR of 8.1% over the forecast period. The market is witnessing significant growth driven by increasing awareness about infant nutrition and rising disposable incomes among middle-income groups. The demand for diversified and highly nutritious infant formula oil products is on the rise, further fueling market expansion.

One of the primary growth factors for the infant formula oil market is the increasing awareness among parents regarding the nutritional needs of infants. There is a growing understanding of the importance of essential fatty acids and other nutritional components in supporting infant growth and development. This trend has led parents to seek out high-quality infant formula oils that can provide the necessary nutrients. Additionally, the rise in dual-income households has resulted in higher disposable incomes, enabling parents to invest more in premium infant nutrition products.

Another significant growth factor is the advancement in food technology and biotechnology. Innovations in these fields have led to the development of new and improved infant formula oil products that are closer to breast milk in terms of nutritional content. These advancements have also made it possible to produce formula oils that cater to specific dietary needs and allergies, thereby broadening the market appeal. Furthermore, collaborations between biotechnology firms and infant formula manufacturers are likely to spur further innovations, driving market growth.

The increasing rate of urbanization and the growth of the organized retail sector are also contributing to the market's expansion. Urbanization often leads to lifestyle changes, including a greater reliance on formula feeding due to the convenience it offers. The growth of supermarkets, hypermarkets, and online retail platforms has made infant formula oils more accessible to a broader consumer base. Enhanced distribution networks and the availability of a wide range of products have made it easier for parents to find and purchase the right infant formula oil for their needs.

Regionally, the Asia Pacific is expected to lead the market, driven by high birth rates and increasing disposable incomes in countries like China and India. North America and Europe are also significant markets due to high awareness levels and the availability of advanced products. Latin America and the Middle East & Africa are emerging markets with considerable growth potential, driven by improving economic conditions and increasing awareness about infant nutrition.

Product Type Analysis

The product type segment in the infant formula oil market includes palm oil, soy oil, coconut oil, sunflower oil, and others. Each type of oil offers unique nutritional benefits and has varying levels of popularity and acceptance among consumers. Palm oil, for instance, is widely used due to its balanced fatty acid profile and cost-effectiveness. However, issues related to sustainability and health concerns have somewhat limited its growth.

Soy oil is another significant segment, known for its high protein content and essential fatty acids. It is particularly favored in the Americas and Europe due to its nutritional benefits and the well-established soy industry in these regions. Soy oil is also often used in specialized infant formula products designed for infants with specific dietary needs or allergies, making it a versatile choice for manufacturers.

Coconut oil has gained popularity in recent years due to its unique composition of medium-chain triglycerides (MCTs), which are easily digestible and provide quick energy. This has made coconut oil a preferred choice for premium and specialized infant formula products. The increasing consumer preference for natural and organic ingredients has also contributed to the growth of this segment.

Sunflower oil, rich in essential fatty acids and vitamins, is another important product type. Its neutral flavor and high nutritional value make it a popular choice among parents and manufacturers alike. The oil's versatility allows it to be used in various types of infant formula products, from standard formulas to those designed for toddlers and special dietary needs.

Other oils, including olive oil, fish oil, and flaxseed oil, are also used in the infant formula market, albeit to a lesser extent. These oils are typically

Comparative linear regression analysis (control variables included in the model).

In 2007, Irene Henriques and Perry Sadorsky wrote "Oil prices and the stock prices of alternative energy companies," a paper examining the relative importance of oil prices and technology stock performance when determining the performance of alternative energy companies. Using a vector autoregression model, they showed that oil prices were not all-powerful when determining alternative energy performance, and indeed, technology appeared to be considerably more important. We have extended the Henriques/Sadorsky model to include a breakdown of various types of alternative energy companies to show that while certain types of alternative energy companies do indeed follow this model of more compelling reactions to technology stock performance than oil price, certain types of alternative energy companies defy the aggregate pattern that Henriques and Sadorsky discovered.

Vietnam IPI: YoY: YTD: GSO Calculation: MQ: Crude Oil data was reported at -11.330 % in Jul 2018. This records a decrease from the previous number of -10.910 % for Jun 2018. Vietnam IPI: YoY: YTD: GSO Calculation: MQ: Crude Oil data is updated monthly, averaging -9.460 % from Jan 2018 (Median) to Jul 2018, with 7 observations. The data reached an all-time high of -7.570 % in Feb 2018 and a record low of -11.330 % in Jul 2018. Vietnam IPI: YoY: YTD: GSO Calculation: MQ: Crude Oil data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.B013: Industrial Production Index: VSIC 2007: 2015=100: YoY Growth: YTD: GSO Calculation.