Oil Price Volatility Research Articles

The connectedness between uncertainty and exchange rates of oil import countries: new evidence from time and frequency perspective

In this study, we have empirically investigated the dynamic spillovers in the connectedness of global economic policy uncertainty and oil market volatility concerning exchange rates in crude oil-importing countries. Our findings reveal that global economic policy uncertainty consistently acts as a net recipient of spillovers, both in the short and long runs. Contrastingly, in the short term, oil market volatility emerges as a significant net transmitter of risk spillovers, with oil price fluctuations exerting a predominant influence on exchange rate volatility in nations reliant on crude oil imports. Remarkably, this relationship undergoes a notable reversal in the long run. In the long run, geopolitical uncertainty takes precedence, establishing itself as the primary net spillover factor, superseding the influence previously exerted by oil price volatility. These empirical insights offer valuable guidance for policymakers in effectively managing external shocks in the context of global economic uncertainties and oil market dynamics.

Volatility forecasting of crude oil futures based on Bi-LSTM-Attention model: The dynamic role of the COVID-19 pandemic and the Russian-Ukrainian conflict

The COVID-19 epidemic and the Russian-Ukrainian conflict have created significant uncertainty in the crude oil market, greatly increasing the difficulty of crude oil futures price volatility forecasting. The objective of this study is to investigate the time-varying nature of the impact of major events on the crude oil market and to examine how to accurately predict crude oil futures price volatility during major shocks. To this end, we propose a novel crude oil futures price volatility forecasting framework based on the Bidirectional Long and Short-Term Memory Neural Network-Attention Mechanism Model (Bi-LSTM-Attention), which contains period division, variable screening, model prediction, and assessment indicators, to further analyze in-depth the impacts of COVID-19 and the Russia-Ukraine conflict on the volatility of crude oil futures price. In addition, we also investigate the impact of external factors on the crude oil market, including economic status, geopolitical events, and the COVID-19 pandemic. Empirical evidence demonstrates that the impact of major events on crude oil futures price volatility is significantly time-varying and differentiated. Particularly during high shock periods, the Fake News Index plays an extremely influential role in driving crude oil futures price volatility. Moreover, our proposed model reliably captures the trend of crude oil futures price volatility during these violent shocks. The contributions of this study are to provide valuable insights into the impact of significant events on crude oil price volatility and contribute to a deeper understanding of crude oil market dynamics.

Oil Price Volatility And Macroeconomics, Does It Affect The Performance Of Islamic Stocks In The Jakarta Islamic Index?

Oil Price Volatility And Macroeconomics, Does It Affect The Performance Of Islamic Stocks In The Jakarta Islamic Index?

Oil Shocks, Monetary Policy, and Stock Returns: A Case of Oil-based Economy

This paper investigates the dynamic effects of oil price shocks on the Saudi stock market. It differs from previous work by examining the role of monetary transmission of oil shocks to stock returns via interest rate and credit channels. A Structural Vector Autoregressive model (SVAR) is used to trace the impacts of the underlying shocks. The results provide robust evidence in favor of a positive relationship between oil price volatility and stock market returns, which is aligned with the prevailing view on the oil impact in net oil-exporting countries. Variance decomposition results indicate that oil shocks are the key source of fluctuations in stock markets as they explain more than 25% of stock return variations. On the other hand, a weak over-all effect of monetary policy is concluded by this study using the two monetary policy channels. Unlike interest rate, credit channel is found to be relatively effective in the monetary policy transmission to stock market. The response of stock returns showed a positive significant impact of credit shocks, but a negligible effect of interest rate shocks. The outcome of this paper is considered plausible in an oil-based economy with fixed-exchange rate regime.

Evaluation of Offshore Project Development under Two Fiscal Regime in Indonesia

The general characteristics of deep water offshore field development are the uncertainty of geological conditions, high technology requirements and development costs, and volatility of oil and gas prices. Despite these challenges, the development of a deep water offshore field remains a strategic project for sustainable economic growth. This study will evaluate the impact of current fiscal of the three offshore field development and estimates the limits of oil and gas price, reserves, and expenses. The intention is to obtain the optimal conditions to encourage the development of new offshore fields. The approached will used a discounted cash flow modelling, followed by evaluate the profitability risk using deterministic and stochastic sensitivity analysis. Monte Carlo simulation used to take into account the uncertainty of several variables, such as oil and gas prices, operating costs, development costs, and reserves. The result shows that investment decisions for the development of new and/or extensions fields in offshore areas remain quite challenging economically, considering to the high development and operating costs, as well as fluctuating of oil and gas prices. The opportunity to obtain a positive net present value on new deep water offshore development fields is less than 41%, thus indicates that further initiative of cost efficiency and fiscal incentive is required in preparing investment planning of new deep water offshore field to compensate the high development and operational cost.

Oil Volatility and Economic Growth: Evidences from Top Oil Trading Countries

The research attempts to delve further into the relationship of oil volatility and economic growth of top oil exporting and importing countries. Annual time series data on oil prices and economic growth (1987-2022) has been considered for top 5 exporting and importing countries. Basic statistical techniques and VAR regressions have been used to analyze data. The relationship between volatility and economic activity was found to be more significant for exporting countries rather than importing countries and a lag effect on 6 years is observed as optimal in this relationship. The global financial crisis was observed as an insignificant event on oil volatility (contrary to Ftiti et al.,2016). One of the significant finding of the study is that Japan’s economic growth is positively associated with the long term oil price volatility.

Assessing the Impact of Oil Price Volatility on Food Prices in Saudi Arabia: Insights From Nonlinear Autoregressive Distributed Lags (NARDL) Analysis

Abstract This research investigates the impact of oil price fluctuations on food prices in Saudi Arabia between 1979 and 2020 using Nonlinear Autoregressive Distributed Lags (NARDL) methodology. The study employs Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests to determine the integration order of the variables and Bounds testing to confirm the existence of long run cointegration relationships between each variable. The results indicate that oil shocks influence food prices through several mechanisms. Firstly, the cost of energy increases due to the rise in oil prices, affecting the agricultural commodity market, including farm equipment, food processing, packaging, and distribution costs. Secondly, the demand for agricultural commodities to produce biofuels affects food availability and thus food prices. Thirdly, as an oil-based economy, the oil price shock affects food prices through its impact on government spending, which affects aggregate demand and liquidity. Therefore, Saudi Arabia should adopt appropriate policies to mitigate the impact of oil price shocks on food prices, including investing in renewable energy sources, diversifying its economy, and improving food production and supply chain efficiency. Additionally, implementing appropriate fiscal policies to ensure sufficient budget allocation for food support programs is crucial. However, Investing in groundwater exploration can contribute significantly to the development of Saudi agriculture and to gradually achieving food self-sufficiency.

The impact of joint events on oil price volatility: Evidence from a dynamic graphical news analysis model

The analysis of event-oriented oil market risk deserves attention owing to the diversity, complexity and time variant of the oil-related events. From the aspect of oil price volatility, we examine the dynamic impact of joint events of different aspects on the oil market using graph analytical models and the proposed value-at-risk estimation approach. Based on the construction and evaluation of news indices, we confirm that events of six categories have strong short-term causality with oil price returns. Furthermore, we prove the capacity of event-oriented indices on oil market risk assessment tasks. Demand and energy-based events have shown their effect in improving oil market risk estimation, as proved by empirical analysis of assessing value-at-risk of Brent and WTI oil price. This paper confirms the impact of oil-related joint events on oil market risk and provides a new perspective of assessing latent oil market risks associated with exogenous events.

Green recovery determination: Interlinkage of international trade, crude oil price volatility, and economic performance

This research delves into the intricate web of relationships between green economic growth, global trade, crude oil price volatility, and economic output. We use a complex econometric framework, the Autoregressive Distributed Lag (ARDL) model, to examine the long-term equilibrium correlations between these variables. When subjected to the Johansen co-integration test, strong long-term ties are shown to be present. The Error Correction Model (ECM) extension of the ARDL model reveals short-term dynamics and corrects deviations from equilibrium values. According to the results of the research, there is a connection between Green economic growth and international trade and economic performance. Economic performance is further dampened by the uncertainty surrounding crude oil prices. Policymakers in today's increasingly globalizing economy would do well to take note of these findings, as they highlight the vital role the factors mentioned above play in creating the Green recovery. The research provides further evidence that it is critical to integrate environmental factors into economic and trade policy in order to promote sustainable development. Based on the study's shreds of evidence, multiple policy implications are recommended for the associated stakeholders.

Oil Price Volatility and Business Profitability: Evidence from The Local Market

This study seeks to investigate the impact of oil price volatility on business profitability in the Province of Guimaras. Through a mixed-methods approach, the study aims to answer important research questions such as the correlation between crude oil price changes and micro business profitability and investment, potential differences in the effect of crude oil prices during periods of high and low prices, trends in the profitability of micro businesses entrepreneurs, the role of the oil and gas upstream segment and its prospects for the future, and proposes an extension program toolkit to help micro business entrepreneurs mitigate the impact of oil price fluctuations on their businesses. The study will collect and analyze data from 200 micro business entrepreneurs operating in five municipalities within the Province of Guimaras, which represent approximately 90 percent of micro businesses in the area by population. By providing significant insights into the impact of oil price volatility on small businesses, the findings of this study can help inform policymakers and business owners to improve economic sustainability and competitiveness in the local market. The researchers recommend several strategies to help small businesses better manage the risks associated with oil price volatility. These include diversifying business operations and revenue streams, developing proactive risk management strategies, seeking financial support and education, and advocating for policies that promote energy efficiency or diversify energy sources. The researchers hope that these recommendations will help small businesses maintain profitability in the face of changing market conditions.

Oil Market Stabilization: The Performance of OPEC and Its Allies

We examine the influence of OPEC+ on the level and volatility of oil prices. By extending Pierru et al.’s (2018, 2020) modeling framework, we are able to distinguish OPEC’s particular role and impact from that of its Allies—those countries who joined with OPEC at the end of 2016 in the attempt to stabilize the market. In addition to corroborating earlier results regarding the impact of OPEC’s management of spare capacity prior to 2017, we now present an analysis of how the concerted actions by the larger community of OPEC+ members have affected prices, including during the tumultuous period in which the COVID-19 pandemic took hold. We find that OPEC+’s efforts to stabilize the market reduced price volatility by up to one half, both before and during the pandemic. We attribute most of that reduction to OPEC’s own actions whereas the impact of the Allies’ efforts was mostly to support the price level. In that vein, OPEC+’s management of spare capacity barely impacted the average price over the pre-pandemic period, but, by countering the price collapse caused by the pandemic demand shock, lifted the average price by $35.70 from May 2020 through August 2021.

The Dynamic Relation between the Oil Price Volatility, Stock Market, Exchange and Interest Rate in GCC Countries: Panel Vector Autoregressive (PVAR) Model

The Dynamic Relation between the Oil Price Volatility, Stock Market, Exchange and Interest Rate in GCC Countries: Panel Vector Autoregressive (PVAR) Model

Oil Price Volatility and Federal Government Recurrent Expenditure in Nigeria: Mediating Role of Exchange Rate

An empirical examination of the impact of oil price volatility on federal government recurrent expenditure in Nigeria and the mediating role of exchange rate from 1980 to 2022 was carried out in this study. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model was used in the data analysis. The findings reveal compelling evidence that oil price volatility as measured using the Brent oil volatility has a significant effect on recurrent federal government spending patterns. The analysis also shows that exchange rate variations serve as significant control variable that influence oil price fluctuations and federal government recurrent expenditure. Hence, the study recommends that federal government should create and properly administer a stabilization fund to save excess oil revenue during periods of boom to lessen the immediate effects of oil price volatility and the abrupt impact on budgetary allocations on recurrent expenditure. There is also the need to have a deep-cut on unproductive recurrent expenditures by federal government and its appointees. The study further recommends the adoption of a closely monitored exchange rate policy by government.

Do implied volatilities of stock and commodity markets affect conventional & shariah indices differently? An evidence by OVX, GVZ and VIX

The current study aims to investigate how index returns of conventional and shariah indices of the USA, Europe, and Asia are affected by changes in oil prices, gold prices, VIX, gold-VIX, and oil-VIX. In our investigation, we used the S&P 500, S&P Europe 350, S&P Pan Asia, and their relevant shariah counterparts for the USA, Europe, and Asia. To examine how the explanatory factors affect the overall distribution of the explained variables, we used OLS and quantile regression. For the time frame prior to Covid-19, we discover that all volatility indices—OVX, GVZ, and VIX—influence returns of all indices simultaneously, and that all variables—aside from the spot price of oil—have a greater impact during the bear phase according to QR findings. Further, Volatility indices have a greater impact on volatility of index returns during the Covid-19 period. This is largely because the Covid-19 outbreak had a rapid impact on economies all around the world, and the only thing that affected financial markets consistently was high volatility. This is further supported by the findings of BEKK, which demonstrate that volatility extends across all markets and originates from commodities like gold, oil, gold-VIX, and VIX. Evidence for this can be seen in the fact that during the COVID-19 period, stock prices reacted more favorably to oil price volatility than to oil spot prices, which even went negative on April 20, 2020. Because of this, market stability can be promoted by reducing volatility through the prompt dissemination of crucial information, even while governments have little direct control over the prices of significant commodities like gold and crude oil.

The moderating role of tax avoidance on CSR and stock price volatility for oil and gas firms

PurposeThis study examines the impact of CSR (corporate social responsibility) on stock price volatility of oil and gas firms and, then identifies the moderating role of tax avoidance.Design/methodology/approachTo achieve the study's purposes, 330 observations are extracted from 30 oil and gas firms for the period between 2010 and 2020, and the estimation method of the Generalized Least Squares (GLS) is used. Actually, the CSR is proxied using the ESG (environmental, social, and governance) score, and the stock price volatility is measured by the degree of stock price variations over 12 months, according to the last 52 week's price.FindingsThe main findings indicate that CSR negatively impacts the stock price volatility. Nonetheless, this negative relationship is moderated positively by tax avoidance. This result is robust to the variation in the measure of volatility, namely the systematic risk.Practical implicationsThis research is helpful for investors to manage their portfolio risk as this article highlights the importance of engaging in sustainable development to reduce financial risk. This study also helps regulators and policymakers, such as environmental agencies and tax authorities, to reassess their control with oil and gas firms and record them according to their CSR practices, because this article emphasizes that it is not fair to pay taxes and engage in CSR practices at the same time.Originality/valueThe impact of CSR on stock price volatility is widely treated for firms. Nevertheless, the mechanisms that may affect this relationship are still seldom discussed. This study attempts to examine the impact of tax avoidance on the CSR–stock price volatility relationship for the oil and gas industry.

A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, and statistical models

This study proposes a novel deep-learning convolution neural network (CNN) to forecast crude oil prices based on historical prices of five precious metals (Gold, Silver, Platinum, Palladium, and Rhodium) in context of green financing. The proposed deep learning CNN has three components: a convolution block called a group block, a novel convolutional neural network architecture called GroupNet, and a regression layer. The proposed model is tested against seven machine learning models and three traditional statistical models for predicting oil price volatility using the same independent variables (5 precious metals). A comparison of the deep learning model (our proposed model) with machine learning/deep learning models and statistical methods indicates that the proposed deep learning model has the highest prediction accuracy. A feature selection technique is also applied using the WEKA ML tool to improve the accuracy of the proposed model and existing machine learning and traditional statistical models. The findings indicate a non-linear correlation between oil price volatility and prices of precious metals. Moreover, statistical analysis indicates that deep learning can be used to predict oil price volatility with greater accuracy than machine learning and statistical methods while using precious metals as predictors. The results also indicate that machine learning models (Decision Tables and M5rules) can be used to predict oil price volatility with considerable accuracy. Moreover, the study proves that traditional statistical models can perform better than a few machine learning models (Lazy LWL and GPR).

Analysis of firm performance in presence of oil price shocks: Importance of skilled management

This paper adopts an innovative technique to separate the impacts of fuel shocks on supply, customer needs, and inventory needs which reveals that the competence of managers have a significant but fluctuating effect on business performance under such circumstances. Manufacturing firms can reap maximum benefits from employing high-ability managers during oil shocks. Managers with exceptional skills exhibit remarkable outcomes in the energy, agricultural, forestry, fishing, and service industries, especially when unfavorable economic consequences accompany oil supply shocks. On the other hand, capable managers fall short in the energy, mining, building, retail, and service sectors during fuel usage need shocks representative of financial expansion. The connection between significant outperformance and underperformance during fuel supply and need shocks is linked to energy companies that have high-ability managers. To protect against fuel value declines, these astute managers will likely employ more aggressive hedging plans to mitigate economic stress or limit profit volatility. Similarly, experienced managers in other sectors may also take measures against the volatility of oil prices by diversifying their supply or using derivatives. Notably, fuel consumption demand shocks are more expensive than supply shocks and vice versa.

A Reassessment of Oil Market Volatility and Stock Market Volatility: Evidence from Selected SAARC Countries

Volatility spillover informs whether the information in one market impacts the information in another. This paper examines whether oil market volatility spills over to the equity markets of selected SAARC countries. The study uses data from February 2013 to September 2019 to obtain updated evidence about the transmission of global oil price volatility to the equity markets of the SAARC member countries. The bivariate EGARCH model is used to test for volatility transmission from the oil market to the stock market. It is found that oil price shocks do not significantly impact equity market volatility, except in Bangladesh. Policymakers can use these findings when making policy decisions.

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

AbstractAs the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

Crude oil prices in times of crisis: The role of Covid-19 and historical events

Oil markets have faced several crises since 1986, with the most recent global economic downturn linked to the Covid-19 pandemic leading to significant demand and supply shocks in the critical oil market. Given that the impact of crises on oil prices has not been uniform we aim to accurately assess oil price volatility during crises by implementing an ARMA(r,s)-Spline-GJR(p,q) model that accounts for volatility persistence. Principal components analysis (PCA) is employed to study the correlation between various crises and their impact on oil prices, while because of the limited number of observations available, PLS regressions are used to study the Covid-19 Crisis. The findings reveal higher levels of spline conditional volatility during the Covid-19 Crisis, providing novel evidence of the correlation between this pandemic and other major crises. Through the NIPALS algorithm, we identify 13 primary components that show a correlation between historical crises and the Covid-19 Crisis. In contrast, PLS-2 regressions indicate a strong correlation between historical crises and the Covid-19 Crisis in only six principal components. These results can help investors, hedgers, speculators, and governments to estimate, predict and make informed decisions as soon as signs of a crisis appear.