Brent fell to 63.05 USD/Bbl on December 2, 2025, down 0.19% from the previous day. Over the past month, Brent's price has fallen 2.84%, and is down 14.36% compared to the same time last year, 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 December of 2025.

The Brent Crude Oil stock market symbol is BZ and it is a major benchmark for global oil prices. Learn more about its significance and how it is traded in financial markets.

The Brent Stock Market allows investors to trade Brent crude oil futures contracts on various global stock exchanges. This article explores the factors influencing the market, the instruments available for trading, and the opportunities and risks it presents for traders.

The file "fuels.txt" includes daily data for Brent futures (BrentF) and spot (BrentS) prices obtained from nasdaq.com database and three NASDAQ indices: 1) NASDAQ OMX Bio/Clean Fuels Index (GRNBIO). Source: {https://indexes.nasdaqomx.com/Index/Overview/GRNBIO} 2) NASDAQ OMX Fuel Cell Index (GRNFUEL). Source:{https://indexes.nasdaqomx.com/Index/Overview/GRNFUEL} 3) NASDAQ OMX Transportation Index (GRNTRN). Source: {https://indexes.nasdaqomx.com/Index/Overview/GRNTRN} The file "fundamentals.txt" includes monthly data for the following variables: 1) WIP: world industrial production index collected from:{https://sites.google.com/site/cjsbaumeister/datasets?authuser=0} 2) COMM: real commodity price factor - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 3) GECON: global economic condition indicator (standardised) - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 4) S.SH: oil supply shock - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 5) OCDSH: oil consumption demand - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 6) OIDSH: oil inventory demand- obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 7) EASH: oil demand shocks driven by global economic activity - obtained from {https://sites.google.com/site/cjsbaumeister/datasets?authuser=0}; 8) GEPU: global economic policy uncertainty index - , a normalised index of the volume of news articles discussing economic policy uncertainty; due to the nonstationarity of the data, obtained from: {https://www.policyuncertainty.com/global_monthly.html} 9) EXPT: Brent spot prices expectations formulated by the U.S. Energy Information Association; 10) SPX - end-of-month data of S&P500 11) SPECUL1: Net position of Money Managers (long-short) for Brent contract - based on the ICE Futures Europe Commitments of Traders Reports ({www.ice.com/marketdata/reports/122}); 12) SPECUL2: Speculation measure analogous to Working's (1960) index, which measures the speculative activity of non-commercial traders in the crude oil market.

Crude Oil fell to 59.17 USD/Bbl on December 2, 2025, down 0.25% from the previous day. Over the past month, Crude Oil's price has fallen 3.08%, and is down 15.40% 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 December of 2025.

Graph and download economic data for CBOE Crude Oil ETF Volatility Index (OVXCLS) from 2007-05-10 to 2025-12-01 about ETF, VIX, volatility, crude, stock market, oil, and USA.

This data is made up of daily stock prices and commodities' futures of a range of variables including NASDAQ clean focused price index, ARCA technology price index, Brent oil futures, Henry hub natural gas futures, Newcastle coal futures, carbon emission futures and green information technology stock price. The dataset supports empirical analysis which examines the volatility of clean energy stock returns (CERs) given the aggregate influence of energy security elements (ESEs) internal to CERs and the individuals influences of a range of exogenous variables including oil futures, natural gas futures, coal futures, carbon emission futures and green information technology stock price.

Maximum unit root module of each VAR models (GARCH volatilities).

This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

The price of a barrel of oil is a key indicator in the global financial markets and is closely watched by investors, traders, and analysts. It serves as a benchmark for the cost of oil and directly impacts various industries and economies worldwide. This article explores the factors affecting the price of oil, including supply and demand dynamics, geopolitical events, economic indicators, and market sentiment. It also discusses the main oil price benchmarks, such as West Texas Intermediate (WTI), Brent Crud

The p values of PP test of WTI, Brent and 11 Chinese sector volatilities estimated from GARCH models.

The p values of PP test of all new variable series.

Cumulative response of Chinese sector indexes volatilities to three different kinds of oil market uncertainty.

This study examines Kuwait’s stock market sectors’ response to the 2020 dual shock of the COVID-19 pandemic and the oil price war between Russia and Saudi Arabia. Traditional cointegration and causality models support the analysis alongside the frequency domain to assess short-run dynamics. The research sample integrates daily data from December 31, 2015, to February 23, 2022. The core research findings indicate that the consumer services, industrials, and basic materials sectors were the most affected by fluctuations in oil prices, highlighting the vulnerability of Kuwait’s oil-dependent economy. Furthermore, the results from the econometric modelling reveal a significant long-run relationship with the West Texas Intermediate index (WTI) and short-run dynamics with the Brent, OPEC and Dubai oil benchmarks. At the global level, monitoring the impact of shocks in commodity-driven economies can help in designing policies that minimise the effects of systemic risks, affecting energy supply chains and inflation stability. This research advances the field by providing a multi-methodological analysis integrating traditional and efficient econometric models to assess sectoral sensitivities in the context of Kuwait, which is an understudied oil-exporting economy. The research findings offer valuable insights for investors and policy-makers in managing risks associated with oil price fluctuations during times of enhanced uncertainty.