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.

View market daily updates and historical trends for Oil VIX. from United States. Source: Chicago Board Options Exchange. Track economic data with YCharts …

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.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-12-01 about VIX, volatility, stock market, and USA.

‡ and † indicate the rejection of the null hypothesis at the 1% and 5% significance levels, respectively.

As of August 2025, the average annual price of Brent crude oil stood at 71.3 U.S. dollars per barrel. This is over nine 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.

BEKK model parameter estimates for the volatility indices by sub-period.

Data from EIA, St. Louis Federal Reserve, LSEG Refinitiv Workspace and Yahoo finance from January 2000 to October 2025 on meaningful fundamental datapoints involved in US motor gasoline and crude oil pricing.

Data is included in three forms, weekly, monthly, and monthly composite. Several variables from the weekly dataset are not present in the monthly dataset, and vice versa. The monthly composite dataset includes all variables across both weekly and monthly data. The data is merged into a monthly form by taking the closest weekly point before the first of each month and using it as the monthly figure for that variable.

Several variables such as US GDP, US CPI, and oil volatility index (OVX) prices are either reported quarterly or were not tracked until the late 2000s, and thus have large chunks of Nan values.

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 standard errors of the estimated parameters are displayed in parentheses.

The 2025 annual OPEC basket price stood at ***** U.S. dollars per barrel as of August. This would be lower than the 2024 average, which amounted to ***** U.S. dollars. The abbreviation OPEC stands for Organization of the Petroleum Exporting Countries and includes Algeria, Angola, Congo, Equatorial Guinea, Gabon, Iraq, Iran, Kuwait, Libya, Nigeria, Saudi Arabia, Venezuela, and the United Arab Emirates. The aim of the OPEC is to coordinate the oil policies of its member states. It was founded in 1960 in Baghdad, Iraq. The OPEC Reference Basket The OPEC crude oil price is defined by the price of the so-called OPEC (Reference) basket. This basket is an average of prices of the various petroleum blends that are produced by the OPEC members. Some of these oil blends are, for example: Saharan Blend from Algeria, Basra Light from Iraq, Arab Light from Saudi Arabia, BCF 17 from Venezuela, et cetera. By increasing and decreasing its oil production, OPEC tries to keep the price between a given maxima and minima. Benchmark crude oil The OPEC basket is one of the most important benchmarks for crude oil prices worldwide. Other significant benchmarks are UK Brent, West Texas Intermediate (WTI), and Dubai Crude (Fateh). Because there are many types and grades of oil, such benchmarks are indispensable for referencing them on the global oil market. The 2025 fall in prices was the result of weakened demand outlooks exacerbated by extensive U.S. trade tariffs.

Learn about the factors that influence the price of crude oil, including supply and demand dynamics, geopolitical events, economic indicators, and weather patterns. Discover how benchmarks and indexes are used to track crude oil prices and how volatility and price fluctuations can impact various sectors of the economy.

The balanced annual panel data for 32 sub-Saharan countries from 2000 to 2020 was used for this study. The countries and period of study was informed by availability of data of interest. Specifically, 11 agricultural commodity dependent countries, 7 energy commodity dependent countries and 14 mineral and metal ore dependent countries were selected (Appendix 1). The annual data comprised of agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices, export value of the dependent commodity, total export value of the country, real GDP (RGDP) and terms of trade (TOT). The data for export value of the dependent commodity, total export value of the country, real GDP and terms of trade was sourced from world bank database (World Development Indicators). Data for agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices are obtained from World Bank commodity price data portal. This study used data from global commodity prices from the World Bank's commodity price data site since the error term (endogenous) is connected with each country's commodity export price index. The pricing information covered agricultural products, world oil, minerals, and metal ores. One benefit of adopting international commodity prices, according to Deaton and Miller (1995), is that they are frequently unaffected by national activities. The utilization of studies on global commodity prices is an example (Tahar et al., 2021). The commodity dependency index of country i at time i was computed as the as the ratio of export value of the dependent commodity to the total export value of the country. The commodity price volatility is estimated using standard deviation from monthly commodity price index to incorporate monthly price variation (Aghion et al., 2009). This approach addresses challenges of within the year volatility inherent in the annual data. In footstep of Arezki et al. (2014) and Mondal & Khanam (2018), standard deviation is used in this study as a proxy of commodity price volatility. The standard deviation is used because of its simplicity and it is not conditioned on the unit of measurement.

A crude oil price chart for the past 10 days demonstrates the fluctuating nature of crude oil prices and highlights the various factors that influence its volatility, including global demand and supply, geopolitical events, and economic indicators. This article provides a detailed analysis of the price movements over the 10-day period, showcasing how tensions in the Middle East, growing demand, concerns over economic growth, unexpected inventory increases, and the impact of the COVID-19 pandemic have all co

NASDAQ Stock Data with Economic Indicators

Overview

This dataset comprises historical stock price data for NASDAQ-listed companies, combined with a selection of key economic indicators. It is designed to provide a comprehensive view of market behavior, facilitating financial analysis and predictive modeling. Users can explore relationships between stock performance and various economic factors.

Features

The dataset includes the following features:

• Date: The date of the recorded stock prices (formatted as YYYY-MM-DD).

• Open: The price at which the stock opened for trading on a given day.

• High: The highest price reached by the stock during the trading day.

• Low: The lowest price recorded during the trading day.

• Close: The price at which the stock closed at the end of the trading day.

• Volume: The total number of shares traded during the day.

• Interest Rate: The prevailing interest rate, which influences economic activity and stock performance.

• Exchange Rate: The exchange rate for the USD against other currencies, reflecting international market influences.

• VIX: The Volatility Index, a measure of market risk and investor sentiment, often referred to as the "fear index."

• Gold: The price of gold per ounce, which serves as a traditional safe-haven asset and is often inversely correlated with stock prices.

• Oil: The price of crude oil, an essential commodity that influences various sectors, especially transportation and manufacturing.

• TED Spread: The difference between the interest rates on interbank loans and short-term U.S. government debt, which indicates credit risk in the banking system.

• EFFR (Effective Federal Funds Rate): The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, influencing overall economic activity.

Use Cases

This dataset is suitable for a variety of applications, including: - Financial Analysis: Evaluate historical trends in stock prices relative to economic indicators. - Predictive Modeling: Develop machine learning models to forecast stock price movements based on historical data and economic variables. - Time Series Analysis: Conduct analyses over different time frames (daily, weekly, monthly, yearly) to identify patterns and anomalies.

Data Source

The data is sourced from reputable financial APIs and databases: - Yahoo Finance: Historical stock prices. - Federal Reserve Economic Data (FRED): Economic indicators such as interest rates and VIX. - Alpha Vantage / Quandl: Commodity prices for gold and oil.

Conclusion

This dataset provides a rich foundation for analysts, researchers, and data scientists interested in the intersection of stock market performance and macroeconomic conditions. Its structured features and comprehensive nature make it a valuable resource for both academic and practical financial inquiries.

The price of crude oil per barrel is influenced by factors such as supply and demand, geopolitical events, and economic indicators. This article explores the fluctuations in crude oil prices, the impact on the global economy, and the role of benchmarks like Brent crude and WTI crude. It also discusses the historical volatility of oil prices, the influence of geopolitical tensions, economic indicators, market speculation, and the shale oil revolution. Understanding crude oil prices is crucial as they have si

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.

The Brent Crude Oil Price is the international benchmark price for oil and a leading indicator of global oil prices. This article discusses the factors influencing its price, including supply and demand dynamics, geopolitical events, and economic conditions. It also explores the significant volatility experienced in recent years and the impact of the COVID-19 pandemic on oil prices. With constant fluctuations, the price of Brent crude remains a closely watched indicator of the global oil market.

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.

The RTSI index is expected to experience volatility in the coming months, driven by geopolitical uncertainty, global economic concerns, and fluctuations in oil prices. While the index may see short-term rallies, a sustained upward trend remains uncertain. The potential for further economic sanctions on Russia, coupled with rising inflation and interest rates, could weigh on market sentiment and lead to downward pressure. However, potential for growth in the Russian economy, particularly in energy and commodity sectors, could support the index. Investors should exercise caution and closely monitor market developments to assess the risks and opportunities associated with the RTSI.