Graph and download economic data for CBOE Crude Oil ETF Volatility Index (OVXCLS) from 2007-05-10 to 2025-06-23 about ETF, VIX, volatility, crude, oil, stock market, and USA.

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.

As of April 2025, the average annual price of Brent crude oil stood at 73.89 U.S. dollars per barrel. This is some seven 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 a 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.

The average price of Indian basket crude oil was estimated to reach 82.58 U.S. dollars per barrel in the financial year 2024. While Indian basket crude oil prices have fluctuated during the reported period, this figure significantly decreased from the previous year’s average of 93.15 U.S. dollars. The average price of crude oil went up marginally around the financial year 2012, touching almost 112 U.S. dollars per barrel. Recent trends in the Indian oil industry The last several years have seen a slight but steady increase in Indian crude oil refinery capacity. However, the annual domestic crude oil production volume has consistently decreased. Not surprisingly, the volume of crude oil imports had recently been on the rise for several years. The future of the Indian energy sector As the third-largest primary energy consumer globally, India relies on various sources to meet its energy demands. At the same time, a significant increase in primary energy consumption across various sources is projected for the coming decades, with renewables playing a vital role in the Indian energy transition.

Discover how OPEC+ output hikes and tariff policies contribute to oil price volatility, with insights from the EIA.

The East Africa Oil and Gas Upstream Market is valued at USD 35 Million in 2023 and is projected to reach USD 50 Million by 2028, exhibiting a CAGR of 6.00%. This growth is attributed to the increasing demand for energy, government initiatives, technological advancements, and the presence of significant oil and gas reserves in the region. The upstream oil and gas market in East Africa has, in recent years exploded into tremendous growth and transformation based on abundant hydrocarbon reserves found in Kenya, Uganda, and Tanzania. Hydrocarbons have transformed international oil companies' interest in regionally untapped potential where discovery has been made. Promising oil fields were discovered in the East African Rift System, particularly in Lake Albert in Uganda. Tanzania equally has promising prospects for natural gas, especially offshore. On the other hand, the region suffers from such issues as infrastructural deficits, legal and regulatory hurdles, and a high need for investment in developing extraction and transportation capacities. Political factors are also crucial because stability and structure have different degrees of impact on a decision for investment. The governments in East Africa realize nowadays the critical importance of developing local content policies and creating conducive regulatory environments that would both attract foreign investment and benefit the locals through extracting resources. Regional cooperation is also needed to overcome some of the infrastructural hurdles, especially in terms of pipeline development. These will be critical for the transportation of crude oil and gas to markets. As energy requirements rise globally, East Africa's upstream oil and gas market offers a huge opportunity to grow if challenges are well managed and strategic partnerships fostered. Recent developments include: In January 2022, Mozambique witnessed the commissioning of its first offshore project. It is a USD 2.5-billion floating Coral South facility above the 450 billion cubic meters (Bcm) of resources in the Coral field in Area 4 of the Rovuma Basin plant. It has the capacity to liquefy 3.4 million ton of natural gas per year from subsea gas-producing wells., In June 2022, Equinor and Shell signed a framework deal with Tanzania to develop the planned USD 30 billion LNG export project in Lindi.. Key drivers for this market are: 4., Abundant Oil and Gas Reserves4.; Favorable Investment in Upstream Sector. Potential restraints include: 4., Volatility of Crude Oil Prices. Notable trends are: Onshore Sector to Dominate the Market.

The maximum crude oil price refers to the highest recorded price at which crude oil has been traded or sold. This article explores the factors that influence crude oil prices, including supply and demand dynamics, geopolitical tensions, macroeconomic conditions, and market speculation. It also highlights historical price volatility, the impact of emerging economies, geopolitical tensions, and market speculation on oil prices. The article discusses recent fluctuations in oil prices due to the COVID-19 pandem

Even though the effect of oil price shocks on macroeconomics has been extensively investigated, the literature on how efficiency in household energy use affect crude oil price volatility is yet explored. This study unveils whether household energy efficiency lower crude oil price volatility asymmetrically in the United States using the historical and forecast dataset that spans from 1970:Q1-2040:Q1. Applying the multivariate case of Quantile-on-Quantile Regression, the empirical results show that household energy efficiency dampens crude oil price volatility with a stronger connection in quantiles before the median quantiles of crude oil price volatility. However, the effect of household energy efficiency decreases with an increase across quantiles of the crude oil price volatility. The results further show that energy-related CO2 emissions and retail electricity price intensify crude oil price volatility with varying effects across quantiles. These findings are similar to the sensitivity analysis and robustness checks. Overall, the policy implication of our findings is that government and policymakers need to demonstrate unequivocal commitments to improving not only energy-efficient practices at household level but also to mitigate energy-related environmental disasters.

The annual price of West Texas Intermediate (WTI) crude oil is expected to reach an average of 61.81 U.S. dollars per barrel in 2025, according to a May 2025 forecast. This would be a decrease of roughly 15 U.S. dollar compared to the previous year. In the first months weeks of 2025, weekly crude oil prices largely stayed below 70 U.S. dollars per barrel amid trade tariffs and expected economic downturn. What are benchmark crudes? WTI is often used as a price reference point called a benchmark (or ”marker”) crude. This category includes Brent crude from the North Sea, Dubai Crude, as well as blends in the OPEC reference basket. WTI, Brent, and the OPEC basket have tended to trade closely, but since 2011, Brent has been selling at a higher annual spot price than WTI, largely due to increased oil production in the United States. What causes price volatility? Oil prices are historically volatile. While mostly shaped by demand and supply like all consumer goods, they may also be affected by production limits, a change in U.S. dollar value, and to an extent by market speculation. In 2022, the annual average price for WTI was close to the peak of nearly 100 U.S. dollars recorded in 2008. In the latter year, multiple factors, such as strikes in Nigeria, an oil sale stop in Venezuela, and the continuous increase in oil demand from China were partly responsible for the price surge. Higher oil prices allowed the pursuit of extraction methods previously deemed too expensive and risky, such as shale gas and tight oil production in the U.S. The widespread practice of fracturing source rocks for oil and gas extraction led to the oil glut in 2016 and made the U.S. the largest oil producer in the world.

Even though the effect of oil price shocks on macroeconomics has been extensively investigated, the literature on how efficiency in household energy use affect crude oil price volatility is yet explored. This study unveils whether household energy efficiency lower crude oil price volatility asymmetrically in the United States using the historical and forecast dataset that spans from 1970:Q1-2040:Q1. Applying the multivariate case of Quantile-on-Quantile Regression, the empirical results show that household energy efficiency dampens crude oil price volatility with a stronger connection in quantiles before the median quantiles of crude oil price volatility. However, the effect of household energy efficiency decreases with an increase across quantiles of the crude oil price volatility. The results further show that energy-related CO2 emissions and retail electricity price intensify crude oil price volatility with varying effects across quantiles. These findings are similar to the sensitivity analysis and robustness checks. Overall, the policy implication of our findings is that government and policymakers need to demonstrate unequivocal commitments to improving not only energy-efficient practices at household level but also to mitigate energy-related environmental disasters.

Learn about the factors that influence crude oil prices and how they have fluctuated over the years, from record highs in 2008 to the significant decline caused by the COVID-19 pandemic in 2020. Discover the key drivers of price volatility and the future outlook for crude oil prices.

Information on price volatility and forecast uncertainty for crude oil and natural gas as well as an analysis of 7 key factors that may influence oil prices, physical market factors and factors related to trading and financial markets.

There has been a systematic increase in the volatility of the real price of crude oil since 1986, followed by a decline in the volatility of oil production since the early 1990s. We explore reasons for this evolution. We show that a likely explanation of this empirical fact is that both the short-run price elasticities of oil demand and of oil supply have declined considerably since the second half of the 1980s. This implies that small disturbances on either side of the oil market can generate large price responses without large quantity movements, which helps explain the latest run-up and subsequent collapse in the price of oil. Our analysis suggests that the variability of oil demand and supply shocks actually has decreased in the more recent past, preventing even larger oil price fluctuations than observed in the data.

Understanding the historical trends of crude oil prices and the factors driving price volatility can provide valuable insights for analyzing the energy market and predicting future movements. This article examines the past five decades of crude oil price fluctuations, from significant events like the 1970s oil embargo to the impact of shale oil production and geopolitical developments in recent years.

Crude oil prices by year from 1970 to 2021, including significant events and average prices, along with the factors that affect price volatility.

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.

We analyze the role of oil price volatility in reducing U.S. macroeconomic instability. Using a Markov Switching Rational Expectation New-Keynesian model we revisit the timing of the Great Moderation and the sources of changes in the volatility of macroeconomic variables. We find that smaller or fewer oil price shocks did not play a major role in explaining the Great Moderation. Instead oil price shocks are recurrent sources of economic fluctuations. The most important factor reducing overall variability is a decline in the volatility of structural macroeconomic shocks. A change to a more responsive (hawkish) monetary policy regime also played a role.

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."

The paper explores the role of speculation and economy fundamentals in the oil market using a two-component GARCH-MIDAS model. Particularly, the authors highlight the different role played by changing oil shocks on short-term and long-term components in terms of oil market volatility. The results show that the global demand shock is the only one factor found to be positive and significantly increasing long- or short-term oil volatility in the full sample. This is consistent with a classic host advocating that global demand dominates the oil market. However, impacts of other oil shocks are significantly weakened and even reversed since the year of 2004. In particular, the speculative demand shock plays a role in stabilizing long-term oil volatility during the post-2004 period. The results also suggest the existence of asymmetric impacts on the short-term oil volatility, particularly for shocks from oil supply, oil specific and oil speculative demand.

The aim of this paper is to analyse the implications of the theory of irreversible investment under uncertainty for investment in oil fields on the United Kingdom Continental Shelf (UKCS). We consider the problem of an operator who owns a licence to develop and extract oil from a field of known capacity. An intertemporal optimization model in discrete time is developed to derive decision rules for the timing of the irreversible development investment and for the optimal rate of extraction. Model simulation is then used to describe the properties of the numerical solutions. The predictions of the theory on the determinants of the irreversible investment decision are then examined using statistical duration analysis. Data on the length of the time period between discovery and development are available for individual fields on the UKCS. We measure the duration of the irreversible investment gestation lag for each field and test the model by assessing the significance of the theoretical variables in explaining the significance of such a lag. Both our theoretical model and our empirical results suggest the importance of a nonlinear interaction of the level of oil prices and the volatility of oil prices in determining the development lag. The simulation of our theoretical model shows a nonlinear impact of oil price volatility on the trigger level of oil prices. Our empirical results suggest that the effect of price volatility is a function of the expected price level, with increased price volatility having a positive impact on the duration of investment appraisal when expected prices are low and a negative impact when they are high.