Crude Oil Price Volatility Research Articles

Analyzing the association between crude oil price volatility and economic growth in OECD economies

Analyzing the association between crude oil price volatility and economic growth in OECD economies

The Impact of Crude Oil Price Fluctuation on Revenue Generation in the Oil Dependent Economy: Nigeria

This study explores the dynamic relationship between crude oil price volatility and revenue generation in Nigeria over a 41-year period from 1981 to 2021. It encompasses an analysis of key variables, including total revenue (REV), oil price (OPV), oil revenue (ORV), non-oil revenue (NRV), and exchange rate (EXCHR). The study employs the Auto Regressive Distributed Lags (ARDL) model to examine the long-term and short-term impacts of oil price volatility on revenue generation. This study reveals that crude oil price volatility (OPV) failed to exert strong impact on total revenue (REV) in Nigeria in the distance period. Oil revenue (ORV) exhibits a strong and positive influence on total revenue, highlighting its pivotal role in revenue generation. Non-oil revenue (NRV) also significantly contributes to total revenue, emphasizing the importance of diversifying revenue sources. Exchange rate (EXCHR) fluctuations do not significantly predict changes in total revenue. Based on the findings, policy recommendations include diversifying revenue sources, enhancing non-oil revenue collection, effective oil revenue management, promoting economic diversification, strengthening tax infrastructure, and adopting prudent budgeting practices.

Spillover dynamics among commodities along the Chinese oil industrial chain: From the perspective of multidimensional networks

This paper investigates the dynamic spillover effects of major commodity prices within the Chinese oil industrial chain. By integrating the Granger causality test with the BEKK-GARCH model, a multidimensional spillover network is constructed to comprehensively analyze the price spillover relationships between commodities from 2013 to 2019. The study finds that crude oil price volatility is the primary driver of spillovers to other commodities within the industrial chain, with midstream commodities such as ethylene and benzene playing a critical intermediary role in the spillover process. Additionally, the spillover between commodities is generally bidirectional, with non-closed chain spillover patterns being more common. Based on these findings, the paper offers policy recommendations to mitigate the risks of price fluctuations, promoting the stable development of the oil market and the sustained growth of the national economy.

Intelligent crude oil price probability forecasting: Deep learning models and industry applications

The crude oil price has been subject to periodic fluctuations because of seasonal changes in industrial demand and supply, weather, natural disasters and global political unrest. An accurate forecast of crude oil prices is of utmost importance for decision makers and industry players in the energy sector. Despite this, the volatility of crude oil prices contributes to the uncertainty of the energy industry, which was particularly challenging following the recent global spread of the COVID-19 epidemic and the Russia–Ukraine conflict. This paper proposes a hybrid deep learning (DL) modelling framework to deal with the volatility of crude oil prices, applying ensemble empirical mode decomposition (EEMD), convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) integrated with quantile regression (QR); named EEMD-CNN-BiLSTM-QR. Two real-world datasets on crude oil prices from the West Texas Intermediate and Brent Crude Oil markets were employed to validate the EEMD-CNN-BiLSTM-QR hybrid modelling framework. Given that the probability density forecast can capture uncertainty, an in-depth analysis was carried out and prediction accuracy calculated. The findings of this study demonstrate that the proposed EEMD-CNN-BiLSTM-QR DL modelling framework, which uses the probability density forecast method, is superior to other tested models in terms of its ability to forecast crude oil prices. The novelty of this study stems mainly from its use of QR, which allows for the description of the conditional distribution of predicted variables and the extraction of more uncertain information for probability density forecasts.

Diversifying crude oil price risk with crude oil volatility index: The role of volatility-of-volatility

To understand the diversification benefit of crude oil volatility, we examine the return-volatility relation in the crude oil market, given the interaction of the volatility (VOL) and the volatility-of-volatility (VOV). We develop a novel empirical model of the crude oil price and crude oil volatility index (OVX) returns incorporating both time-varying and state-dependent variances and correlations, thus allowing us to identify distinct market regimes of VOL and VOV. We find that the behavior of the return-volatility relation is contingent on the prevailing VOV regimes. Specifically, in a low (high) VOV regime, the relation becomes less (more) negative as VOL increases. These empirical results therefore imply that the diversification benefit of crude oil volatility is far from uniform across the different market states. Finally, using our proposed empirical model, we demonstrate the economic significance of recognizing both the time-varying and state-dependent variances/correlations in portfolio risk forecasting and construction.

A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention

The volatility of crude oil price can significantly impact the stability of the crude oil market and even the global economy. Effectively predicting global crude oil price and volatility provides a scientific basis for decision-making for market regulators and investors worldwide, promoting the sound development of the global economy. In this study, we integrate signal processing with deep learning methods to present an optimal forecasting strategy for global crude oil price and volatility. We select the daily price of the WTI crude oil market from April 4, 1983, to December 12, 2023, for calculating volatility. Subsequently, employing extreme-point symmetric empirical mode decomposition (ESMD), K-means clustering, and fast independent component analysis method, we decompose and reconstruct the forecasting data, obtaining independent components with non-Gaussian characteristics. These components serve as inputs to estimate the accuracy of various models, including BiLSTM, Attention, LSTM, SVR, RF, and their combinations, in predicting crude oil price and volatility from both a point prediction and interval prediction perspective. Empirical results demonstrate that data decomposition, reconstruction, and the BiLSTM-Attention model outperform other models in predicting crude oil price and volatility.

Modelling and forecasting crude oil price volatility with climate policy uncertainty

The impact of climate risk on economic and financial fields has attracted considerable interest from academics and practitioners. Recent literature documents that climate risk can affect oil volatility through the dual channel of economic activities and financial markets. Inspired by this, this paper uses climate policy uncertainty (CPU) to examine the predictability of crude oil price volatility. The empirical results indicate that changes in the CPU, rather than the original CPU, can forecast oil volatility. The revealed volatility predictability is both in-sample and out-of-sample. Asset allocation exercise shows that this finding is also economically significant. Furthermore, the predictive ability is mainly concentrated during economic expansions and the CPU contains climate risk that is not available for other predictors. Finally, CPU change indicators can explain the changes in crude oil consumption, which is the economic source of their predictive power.

Crude oil price volatility and environmental performance

We study the relationship between crude oil price volatility and corporate environmental performance. Using an extensive dataset from 32 countries consisting of 18,464 firm-year observations, we provide strong evidence that oil price volatility significantly increases firms' environmental performance. Our main inference is robust when using alternative measures of oil price volatility and environmental performance, alternative econometric specifications and samples, and several approaches to control for endogeneity. In a set of additional analyses, we first conduct a difference-in-differences analysis that exploits the Arab Spring as an exogenous oil price volatility increase and document a stronger relationship between oil price volatility and environmental performance in the aftermath of the Arab Spring. We second identify (i) capital expenditures and (ii) alternative energy importation as two mechanisms through which oil price volatility influences environmental performance. We finally show that national culture plays a significant role in moderating the relationship between oil price volatility and environmental performance. Taken together, our empirical findings highlight the role of economic uncertainty in affecting firms’ environmental performance and provide significant contributions to management and policymakers.

Crude oil volatility forecasting: Insights from a novel time-varying parameter GARCH-MIDAS model

Stationary GARCH-MIDAS models encounter challenges in effectively capturing the dynamic impact of realized volatility on crude oil price volatility. This study introduces a novel time-varying parameter GARCH-MIDAS (TVP-GARCH-MIDAS) model to address these challenges and intricately capture the evolving dynamics between variables. The empirical results underscore the superior precision of the TVP-GARCH-MIDAS model in reflecting the influence of realized volatility on crude oil price volatility over time. In comparison to the stationary GARCH-MIDAS and MS-GARCH-MIDAS models, the proposed model exhibits outstanding out-of-sample forecasting performance and has excellent economic significance. This study provides valuable insights for investors and policy-makers, supporting better risk management and more effective investment strategy formulation.

Crude oil price volatility and its impact on the development of marginal fields: a case study from the Niger Delta Basin, Nigeria

Crude oil price volatility and its impact on the development of marginal fields: a case study from the Niger Delta Basin, Nigeria

Oil Volatility Uncertainty: Impact on Fundamental Macroeconomics and the Stock Index

This study utilized both single-regime GARCH and double-regime GARCH models to investigate oil price volatility, Spanish macroeconomic factors, and stock prices during major crises such as geopolitical conflicts, the global financial crisis (GFC), and COVID-19, covering the period from Q2-1995 to Q4-2023. Additionally, the impact of crude oil price volatility on these factors was examined. The empirical results confirmed the presence of the leverage effect and identified multiple volatility switches associated with remarkable events like the GFC, the European debt crisis, the COVID-19 pandemic, and the Russian war. ARDL model analysis revealed a statistically significant positive relationship between oil prices and both unemployment and inflation rates in the long term, while other factors showed a negative correlation.

Does crude oil price volatility affect risk-taking capability in business group firms: evidence from India?

PurposeThe study tests risk-taking and risk-aversion capabilities while distinguishing between business group firms and stand-alone firms and considering oil price volatility. Second, this attempt to study the linkage between risk-taking during market down movements and when the firms have established themselves as product market leaders. Third, this study analyses the “sentiment” state, where it explores the reaction of corporations when the market is in the negative direction, and lastly, it explores the linkage between product market competition and risk-aversion.Design/methodology/approachThis study uses financial information for 1,273 non-financial companies and other required data from various sources. The study employs panel data and utilizes different empirical methodologies, including the generalized method of moments (GMM) estimator, to test the stated hypotheses.FindingsWe find that the business group firms have more risk-taking proficiencies compared with the stand-alone firms. Moreover, this study discovers that the corporates avoid taking risks when the market is not performing well. Also, when the market is down and crude prices are high, the management expects high earnings in the future, willingly takes risks and shows that product market leaders do not follow the risk-aversion strategy.Practical implicationsThe empirical results indicate that oil price movement can restrict management’s behaviour when choosing a risky investment project. Management should develop a robust policy that follows the group of firms. In the policy, the management should describe the level of risk that may be taken by the firm and implement it when required.Originality/valueSince we do not find any studies in this context, then there is a major and essential gap in the literature that this study should fill.

Measuring the Effect of Regime Change on Petroleum Price in Nigeria Using Moving Index

The ongoing volatility of crude oil prices on the international market has harmed every sector of the Nigerian economy. Every Nigerian government regime experiences fluctuations in the price of petroleum. Thus, this research studied the effect of change in government regime on change in petroleum prices using a moving index with a constant and moving base year. Data on government regime and prices of petroleum were collected (1960 to 2021) from the Office of the Secretary to the Government of the Federation, Central Bank of Nigeria (CBN) Statistical Bulletin, and National Bureau of Statistics (NBS), spanning 62 years, and the changes in these prices over all the regime were observed via the time plot. The results of the analysis showed that regime change in Nigeria has significantly impacted the price of petroleum. The trend of change in petroleum prices using 1960 as a constant base year showed that regime change has a significant effect on change in petroleum price, while the moving index with varying base years showed no significant effect on the change in the petroleum price. Therefore, it can be concluded that variations in the price of petroleum in Nigeria are caused by both changes in time and regime. The estimated trend of change in the prices of petroleum with the period under study showed an upward trend. The movement showed that the price of petroleum is not likely to reduce shortly but rather will increase if nothing is done to stabilize it.

Oil price volatility and changes in corporate debt: An empirical study in the Indian landscape

Changes in crude oil prices affect corporate debt position. This study investigates the association between changes in debt and crude oil price volatility in nonfinancial, BG, and SA firms in India. We use financial variables, crude oil prices, and macroeconomics data from 2004 to 2020 for 16,452 firm-years and this study employs unbalanced panel data and utilizes different empirical methodologies, including the generalised method of moments (GMM) estimator, to test the stated hypotheses. The results reveal that the debt position and the crude oil price volatility significantly have different effects on business group (BG) and stand-alone (SA) firms. Crude oil price volatility is positively related to changes in the debt position. The effects of the financial variables also significantly differ for the BG and SA firms. Finally, firms change their debt position in the positive or negative direction based on the fluctuations in crude oil prices. Policy implications have also been provided.

Interval prediction of crude oil spot price volatility: An improved hybrid model integrating decomposition strategy, IESN and ARIMA

Crude oil price volatility prediction are important for energy policymaking and investment risk avoidance, and have attracted a great deal of global attention. In recent years, although some studies have proposed methods for prediction crude oil price volatility, most of them are point prediction, which are difficult to provide richer reference information for managers. Therefore, an improved hybrid interval prediction model is proposed. The hybrid model combines data feature extraction and quantification techniques, fuzzy information granulation(FIG), improved echo state network (IESN), and autoregressive integrated moving average model (ARIMA). The hybrid model is able to accurately portray the different features present in the volatility of crude oil spot price. The features are quantified in order to merge similar feature components and improve the prediction effect. On this basis, IESN, which possesses strong nonlinear learning ability, is used to predict high-frequency sequences, and ARIMA, which has strong linear carving ability, is used to predict low-frequency sequences and residual terms. By “divide and conquer”, excellent prediction intervals are obtained. West Texas intermediate (WTI) crude oil spot and Brent crude oil spot are selected for analysis, and consider the influence of futures on interval construction. The effect of volatility intervals is validated from the pricing benchmark and time scale dimensions, respectively. The experimental results show that the proposed model has optimal performance in all scenarios. Take the performance of WTI on the daily scale as an example, its PICWC value is 0.8205. Compared with other benchmark models, it improves by 137.4504, 3.1118, 1.0506, and 0.4875, respectively. The proposed model provides a reliable new idea for crude oil spot volatility interval prediction.

Does crude oil price volatility respond asymmetrically to financial shocks?

Recent literature has explored the effect of monetary and financial shocks, along with oil supply and global demand shocks, in determining oil price volatility. This paper elucidates whether the change in oil price volatility in response to financial shocks differs over quantiles over the period 1993–2021. First, we disintegrate the structural innovations to oil price volatility, originating from crude oil production, global demand, oil demand, oil inventories, interest rates, and financial market volatility, using the SVAR approach during pre-crisis and post-crisis phases. Furthermore, using quantile autoregressive distributed lag models (QARDL) and nonlinear autoregressive distributed lag (NARDL) models, we analyze the plausible asymmetric effects of these shocks. The findings indicate the significance of supply-side and global demand shocks on oil price volatility in the pre-crisis periods, followed by the increase in the explanatory power of financial shocks on oil price volatility after the crisis. It also suggests that oil price volatility responds asymmetrically to oil supply, inventories, and financial shocks.

Crude oil price volatility—An unintended consequence of carbon pricing: Evidence from transfer entropy and wavelet-partial wavelet coherence analyses

The urgent need to achieve the COP-27 targets has become evident due to the more frequent and severe climate-induced disasters and their socioeconomic consequences. The adverse effects of climate change highlight the need for an immediate shift away from fossil fuels without compromising economic development. Internalization of the negative externality in market transactions through the imposition of carbon pricing is widely touted as the most economically efficient means of solving this problem. This study, however, argues that crude oil price volatility could be an unintended consequence of carbon pricing. To this end, information flows between the European Union Emissions Trading Scheme and crude oil price volatility are examined through transfer entropy and wavelet-partial wavelet coherence analyses. Daily data from January 1, 2014 to July 1, 2023 are analyzed. The transfer entropy results show that information on carbon pricing reduces uncertainty about crude oil price volatility and vice versa, indicating that carbon pricing would be quite informative in building models to predict crude oil price volatility. The wavelet-partial coherence analyses reveal that the surge in carbon prices experienced in the late 2010s induced crude oil volatility, whereas the crude oil price volatility triggered by the COVID-19 pandemic forced carbon prices down. This study therefore identifies carbon price movements as a legitimate fear for policymakers, as it is a new source of volatility in conventional energy markets. Caution should thus be the watchword regarding optimal carbon pricing. Aiming to rapidly attain the full optimal carbon price is not recommended. Rapid changes in carbon prices will have strong redistributive implications across economies.

A novel prediction model to evaluate the dynamic interrelationship between gold and crude oil

AbstractGlobal events, such as the pandemic and European conflicts, have caused significant inflation and high volatility in gold and crude oil prices. This has garnered substantial international attention while banks, governments, and businesses are devoted to developing strategies to hedge against the potential impacts of economic uncertainties. Focused on addressing economic uncertainties, this study delves into the crucial role of the relationship between gold and crude oil in shaping global financial and economic dynamics. The primary objective of this study is to conduct a comprehensive analysis and construct a rolling SARIMAX model for predicting the rolling 12-months correlation of the Gold–WTI and Gold–Brent relationship. Monthly data on Gold spot prices, WTI futures, and Brent futures is collected from May 1983 to December 2022. Three sophisticated data analysis techniques, the Rolling Correlation method, the SARIMAX model, and the Rolling Model are integrated to develop the Rolling SARIMAX module. R-square values of this newly developed model achieved 89.8% and 88.4% for predicting the rolling correlation for Gold Spot Price-WTI Futures and Gold Spot Price-Brent Futures, respectively, while the mean absolute percentage error was 10.33% and 10.84%, respectively. The higher accuracy in correlation prediction between gold and crude oil prices can present critical insights for risk management, economic planning, strategic investment, economic cycles, and global economic outlook. This newly developed prediction model adeptly handles both linear and non-linear relationships while adapting to external variables in dynamic market scenarios. Its judicious balance between complexity and practicality positions it as a sophisticated analytical tool with real-world applicability, setting a new benchmark in financial market analysis.

Dynamic Multiscale Relationships Between COVID-19 Sentiment and Extreme Crude Oil Returns: Evidence from Wavelet Coherence Analysis

ABSTRACT This study investigates the dynamic, multi-scale relationship between sentiment related to the COVID-19 pandemic and extreme returns in crude oil. The recently developed COVID-19 indices are employed to gauge pandemic sentiment. Utilizing daily data spanning from January 2020 to December 2021, Granger’s linear and nonlinear causality tests reveal that indices nonlinearly influence extreme fluctuations in West Texas Intermediate and Brent crude oil prices. Interestingly, a reciprocal causation is also identified: extreme crude oil returns significantly affect the indices. Furthermore, the wavelet transform coherence analysis sheds light on the indices’ ability to predict extreme crude oil price volatility across specific time-frequency domains, displaying diverse distributions and lead-lag patterns among the sub-indices. Our study underscores the efficacy of indices in anticipating extreme fluctuations in crude oil values during the COVID-19 pandemic, carrying important implications for investors, scholars, and policymakers.

Unveiling the enigma: Exploring how uncertain crude oil prices shape investment expenditure and efficiency in Chinese enterprises

As a crucial energy resource, crude oil plays a vital role in propelling global economic progress. China's energy consumption is extensive due to its position as the second-largest global economy and leading importer of crude oil. A stable crude oil supply can support China's steady economic growth. The China Crude Oil Futures (INE) launch in 2018 has enriched the WTI and Brent crude oil market systems, further consolidating the critical position of crude oil in China's economic and financial development. Therefore, this paper first examines the impact of crude oil price uncertainty on the investment activities of Chinese micro-enterprises by selecting three crude oil futures: WTI, Brent, and INE. Based on the quarterly (half-year) report of Shanghai and Shenzhen A-share listed enterprises from 2008 to 2023, this study examines the effects of uncertainty in crude oil prices on investment spending and efficiency inside enterprises and the underlying mechanisms that drive these effects. This study provides evidence that the fluctuation in crude oil prices has a detrimental impact on investment expenditure, while concurrently improving the investment efficiency of enterprises. The mechanism test also demonstrates that the volatility of crude oil prices has the potential to diminish investment expenditure and enhance the investment efficiency of enterprises. On the macro level, it mainly depends on reducing the macroeconomic boom index, enterprise boom index, and entrepreneur confidence index. At the micro level, it relies on increasing enterprise cash flow, lowering enterprise investment expectations and consumer product demand. Further heterogeneous analysis indicates that during significant events such as the global COVID-19 pandemic and the Russia-Ukraine conflict, there is a substantial increase in crude oil price uncertainty, leading to reduced enterprise investment expenditure and improved enterprise investment efficiency. Furthermore, the volatility associated with the increase in oil prices has a more pronounced effect on investment outlays and operational effectiveness. Moreover, the volatility of crude oil prices significantly influences the investment expenditure and investment efficiency of enterprises in the secondary industry and non-state-owned sectors. This empirical study examined the causal relationship between crude oil price, investment spending, and investment efficiency. The results of this investigation enhance the current scholarly knowledge on the relationship between crude oil prices and investment, so contributing to the advancement and refinement of this particular field of inquiry. This paper makes a valuable exploration and attempt to explain the mechanism of the impact of crude oil price on the investment activities of micro-enterprises.