Crude Oil fell to 67.26 USD/Bbl on August 1, 2025, down 2.89% from the previous day. Over the past month, Crude Oil's price has fallen 0.28%, and is down 8.51% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on August of 2025.

On July 28, 2025, the Brent crude oil price stood at 69.68 U.S. dollars per barrel, compared to 66.71 U.S. dollars for WTI oil and 70.98 U.S. dollars for the OPEC basket. Brent and OPEC prices rose slightly that week, while WTI prices fell.Europe's Brent crude oil, the U.S. WTI crude oil, and OPEC's basket are three of the most important benchmarks used by traders as reference for oil and gasoline prices. Lowest ever oil prices during coronavirus pandemic In 2020, the coronavirus pandemic resulted in crude oil prices hitting a major slump as oil demand drastically declined following lockdowns and travel restrictions. Initial outlooks and uncertainty surrounding the course of the pandemic brought about a disagreement between two of the largest oil producers, Russia and Saudi Arabia, in early March. Bilateral talks between global oil producers ended in agreement on April 13th, with promises to cut petroleum output and hopes rising that these might help stabilize the oil price in the coming weeks. However, with storage facilities and oil tankers quickly filling up, fears grew over where to store excess oil, leading to benchmark prices seeing record negative prices between April 20 and April 22, 2020. How crude oil prices are determined As with most commodities, crude oil prices are impacted by supply and demand, as well as inventories and market sentiment. However, as oil is most often traded in future contracts (where a contract is agreed upon while product delivery will follow in the next two to three months), market speculation is one of the principal determinants for oil prices. Traders make conclusions on how production output and consumer demand will likely develop over the coming months, leaving room for uncertainty. Spot prices differ from futures in so far as they reflect the current market price of a commodity.

Brent fell to 69.48 USD/Bbl on August 1, 2025, down 3.10% from the previous day. Over the past month, Brent's price has risen 0.54%, but it is still 9.54% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Brent crude oil - values, historical data, forecasts and news - updated on August of 2025.

Annual futures contract three price for Cushing Oklahoma crude oil stood at 67.3 U.S. dollars per barrel in 2021, an increase compared to the previous years. During the period in consideration, figures peaked at over 100 U.S. dollars per barrel in 2008.

Crude oil futures live price refers to the current market value of futures contracts for crude oil. Traders, investors, and market participants use this price as a benchmark for global oil prices and to make informed decisions. Learn more about how to access and interpret the live price of crude oil futures.

The WTI Crude Oil Futures Price Chart provides a visual representation of the historical prices of West Texas Intermediate (WTI) crude oil futures. Traders, investors, and analysts can use this chart to track price movements, study historical price patterns, and compare current prices with historical data. Additionally, the chart may include features such as trading volume and open interest to provide further insights into the market. However, it is important to consider other factors such as geopolitical e

WTI Crude Oil Futures data, recent 43 years (traceable to Mar 30,1983), the unit is USD/bbl, latest value is 67.62, updated at Jul 17,2025

Despite their widespread use as predictors of the spot price of oil, oil futures prices tend to be less accurate in the mean-squared prediction error sense than no-change forecasts. This result is driven by the variability of the futures price about the spot price, as captured by the oil futures spread. This variability can be explained by the marginal convenience yield of oil inventories. Using a two-country, multi-period general equilibrium model of the spot and futures markets for crude oil we show that increased uncertainty about future oil supply shortfalls under plausible assumptions causes the spread to decline. Increased uncertainty also causes precautionary demand for oil to increase, resulting in an immediate increase in the real spot price. Thus the negative of the oil futures spread may be viewed as an indicator of fluctuations in the price of crude oil driven by precautionary demand. An empirical analysis of this indicator provides evidence of how shifts in the uncertainty about future oil supply shortfalls affect the real spot price of crude oil.

Replication data for peer-reviewed article published in Journal of Applied Econometrics. Paper published online February 24, 2010.

The Brent Crude Oil Futures Price is integral to the global oil market as it serves as a benchmark for pricing of oil around the world. This futures contract represents the price of oil for delivery in the future, specifically the Brent blend of crude oil which is extracted from the North Sea. As a highly regarded crude oil benchmark, it plays a significant role in the functioning of the oil industry.

The dataset includes monthly WTI crude oil spot and futures prices with the shortest maturity contracts (one-month, two-month, and three-month futures contracts), the US Ending Stocks of Crude Oil and Petroleum Products in thousands of barrels. All the datasets were sourced from US EIA, except for the three-month US treasury bill dataset sourced from the Federal Reserve Economic Data of St. Louis Federal Bank.

Oil shocks exert influence on macroeconomic activity through various channels, many of which imply a symmetric effect. However, the effect can also be asymmetric. In particular, sharp oil price changes "either increases or decreases" may reduce aggregate output temporarily because they delay business investment by raising uncertainty or induce costly sectoral resource reallocation. Consistent with these asymmetric-effect hypotheses, the authors find that a volatility measure constructed using daily crude oil futures prices has a negative and significant effect on future gross domestic product (GDP) growth over the period 1984-2004. Moreover, the effect becomes more significant after oil price changes are also included in the regression to control for the symmetric effect. The evidence here provides economic rationales for Hamilton's (2003) nonlinear oil shock measure: It captures overall effects, both symmetric and asymmetric, of oil price shocks on output.

Brent Crude Oil Futures data, recent 38 years (traceable to Jun 24,1988), the unit is USD/bbl, latest value is 68.39, updated at Jul 25,2025

Brent Crude Oil Price: EA: Forward: 1 Month data was reported at 72.079 EUR/Barrel in Mar 2025. This records an increase from the previous number of 69.734 EUR/Barrel for Dec 2024. Brent Crude Oil Price: EA: Forward: 1 Month data is updated quarterly, averaging 34.532 EUR/Barrel from Sep 1985 (Median) to Mar 2025, with 159 observations. The data reached an all-time high of 103.376 EUR/Barrel in Jun 2022 and a record low of 9.999 EUR/Barrel in Dec 1998. Brent Crude Oil Price: EA: Forward: 1 Month data remains active status in CEIC and is reported by European Central Bank. The data is categorized under Global Database’s European Union – Table EU.P005: European Central Bank: Crude Oil Price. [COVID-19-IMPACT]

Heating Oil fell to 2.29 USD/Gal on August 1, 2025, down 4.50% from the previous day. Over the past month, Heating Oil's price has fallen 4.95%, and is down 1.81% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Heating oil - values, historical data, forecasts and news - updated on August of 2025.

In addition to their theoretical analysis of the joint determination of oil futures prices and oil spot prices, Alquist and Kilian (Journal of Applied Econometrics, 2010, 25(4), 539-573) compare the out-of-sample accuracy of the random walk forecast with that of forecasts based on oil futures prices and other predictors. The results of my replication exercise are very similar to the original forecast accuracy results, but the relative accuracy of the random walk forecast and the futures-based forecast changes when the sample is extended to August 2016, consistent with the results of several other recent studies by Kilian and co-authors.

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.

Murban Crude Oil Futures data, recent 5 years (traceable to Mar 29,2021), the unit is USD/bbl, latest value is 70.77, updated at Jul 17,2025

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Crude Oil Futures: A Hold for the Next 3 Months

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Crude Oil Futures: Experts Predict Volatility for S&P GSCI Crude Oil Index.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Crude Oil Futures Show Bullish Outlook for S&P GSCI Crude Oil Index.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data