Crude Oil Price Volatility Research Articles

Volatility Asymmetry and Spillover Effects in Crude Oil Futures Market: Evidence from China

In order to study the international influence and function of domestic crude oil futures prices in depth, based on the daily data of 2018-2022, this paper uses Granger causality test, cointegration test, ECM model and several forms of GARCH model to make empirical research on the price correlation, volatility, asymmetry and risk spillover effects of domestic crude oil, Brent crude oil, WTI crude oil and Oman crude oil futures in the three major foreign crude oil markets. The study found that there is a Granger causal relationship and a cointegration relationship between domestic and foreign crude oil futures prices. There are interactions in the crude oil futures markets at home and abroad, and there are different fluctuation patterns in the short-term fluctuation process. There are asymmetries, leverage effects and spillover effects in the volatility of the four crude oil futures markets at home and abroad with different intensities. The bearish news of the three foreign markets is greater than the impact of the bullish news than that of the domestic one. There are risk spillover effects and volatility effects between domestic and the three foreign crude oil futures markets, and the domestic spillover effect on foreign crude oil is greater than the spillover effect on domestic crude oil from abroad. The conclusion is that the international influence of domestic crude oil futures prices is stronger than that of Omani crude oil, and weaker than Brent and WTI crude oil. The leverage effect of the three foreign markets is greater than that of China, and the leverage effect of the Oman crude oil futures market is the strongest. The asymmetry and spillover effect of fluctuations in the domestic and foreign crude oil futures markets lead to the risk and speculation of the three foreign markets being greater than that of the domestic one, and the speculative arbitrage through the domestic and foreign crude oil markets may cause huge market risks, and it is suggested that the internationalization of the domestic futures market needs more systems and mechanisms to support the construction.

Macroeconomic effects of crude oil shocks: Evidence from South Asian countries.

This research tends to convey the relationship between crude oil price volatility and key macroeconomics indicators, i.e., gross domestic product (GDP), inflation rate (IR), interest rate, and exchange rate. The study collected the time-series data (2000–2020) from the South Asian countries (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka), and employed macroeconomic policy modeling tools (impulse response function and the prediction error variance decomposition technique) in the vector autorepression (VAR) setup. The outcome of the impulse response function explained considerable variance among macroeconomic indicators in response to crude oil price shocks. The macroeconomic indicators are extremely susceptible to minor fluctuations in oil prices causing a significant impact on the region's socioeconomic situation. The result of variance decomposition indicates that each country in the region reacts differently to crude oil price volatility which reflects their macroeconomics fundamentals, independent policy, sector structure, and country differences. The findings support change in public policies in a way to reduce their dependency on oil energy and encourage them toward renewal and green energy sources for better environmental results and sustainable development.

Revisiting natural resources volatility via TGARCH and EGARCH

The natural resources commodity price volatility is one of the leading discussion issues, particularly in global issues. For instance, the global financial crisis and the Covid-19 pandemic have created uncertainties in global economic and financial markets. During the said events, the global economy disrupts the demand and supply chain of raw materials, natural resources, and energy across the globe, which is among the leading issues of price volatility in natural resources. Therefore, it is important to evaluate the volatility of natural resources, especially during these two periods. The current study, unlike previous studies, evaluates the volatility in crude oil prices while using the extended period from 2007 to 11-01 to 2020-11-16 from a global perspective. The empirical analyses are examined using exponential generalized autoregressive conditional heteroscedasticity and the threshold generalized autoregressive conditional heteroscedasticity methods. The estimated results of both approaches indicate the existence of volatility in natural resource commodity prices. Yet, the global financial crisis and Covid-19 pandemic periods exhibit higher volatilities than the rest of the overall period. Comparatively, natural resources commodity price volatility in the Covid-19 pandemic surpasses the volatility than that of the global financial crisis. This study provides relevant policy implications regarding global volatility management based on the empirical results. The study further provides guidelines for future research on the same area and limitations indorded in this study.

Natural resources commodity prices volatility: Evidence from COVID-19 for the US economy

Volatility in natural resources commodity prices is prevailing for a longer period. However, after the Covid-19 pandemic, most economical and financial markets were affected, and the natural resources commodity market was greatly affected. Besides, the pandemic's closing or postponement of the industrial sector further fuels uncertainty in the natural resources commodity market. Therefore, it is important to analyze whether volatility exists in natural resources commodity prices during the pandemic period? In this regard, the current study investigated price volatility in copper and crude oil prices during the Covid-19 while using the most recent and extended dataset from January 02, 2019, to October 08, 2021, in the case of the US. For empirical examination, this study used the breakpoint unit root test, which illustrates that the leveled data is non-stationary while the first differenced data is stationary, where both leveled and first differenced data demonstrate the breakpoints in time series. This study uses the threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to analyze natural resource commodity price volatility. The empirical findings uncover that both variables are volatile during the Covid-19 pandemic. However, the negative shock of the Covid-19 pandemic makes the crude oil price more volatile relative to copper prices. Besides, the previous variances are also positively associated with the current variances of both variables. The findings also reveal that copper could be a safe haven for reducing volatility in natural resources. Based on the empirical findings, this study suggests ensuring macroeconomic stability and reconsidering the traditional price-fixing or price ceiling policies to tackle volatility in the Covid-19 pandemic.

A CEEMD-ARIMA-SVM model with structural breaks to forecast the crude oil prices linked with extreme events.

This paper develops an integrated framework to forecast the volatility of crude oil prices by considering the impacts of extreme events (structural breaks). The impacts of extreme events are vital to improving prediction accuracy. Aiming to demonstrate the crude oil price fluctuation and the impacts of external events, this paper employs the complementary ensemble empirical mode decomposition (CEEMD). It decomposes the crude oil price into some constituents at various frequencies to extract a market fluctuation, a shock from extreme events and a long-term trend. The shock from extreme events is found to be the most crucial element in deciding the crude oil prices. Then we combine the iterative cumulative sum of squares (ICSS) test with the Chow test to get the structural breaks and analyze the extreme event impacts. Finally, this paper combines the structural breaks, the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) to make a forecast of the crude oil prices. The empirical process proves that the CEEMD-ARIMA-SVM model with structural breaks performs the best when compared with the other ARIMA-type models and SVM-type models. The framework offers an insightful view to help decision-makers and can be used in many areas.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00500-022-07276-5.

Time-frequency analysis between Bloomberg Commodity Index (BCOM) and WTI crude oil prices

The price movement of commodities in general and crude oil, in particular, are critical for both commodity-consuming and producing countries. The prime objective of this study is to examine the characteristic behavior of commodity futures and commodity index and the dynamic relationships between commodity index and commodity futures. The study first investigates the evolution of volatility of Bloomberg commodity index (BCOM) and WTI crude oil (CRUDE) prices at different time scales using Wavelet Power Spectrum Analysis. Second, the correlation and causality between BCOM and CRUDE are investigated using Wavelet Coherency and Phase Difference methodology. The average level volatility of BCOM and CRUDE is different at different time scales. Wavelet Coherence shows that they are correlated in medium to long term periods and not in the short term. Further, during the period of changed correlation structure over the study period, the causality structure between BCOM and CRUDE is also changed at different time scales. Therefore, policy measures to control prices should be different in the short term than in the medium to long term when both the prices are not correlated.

The power play of natural gas and crude oil in the move towards the financialization of the energy market

The financialization of the energy market has capacitated energy commodities to affect economic activities unfathomably. Despite the availability of rich literature investigating crude oil and developing its strong ties with economic and financial stability and climate change, little attention has been paid to natural gas (NG or LNG) and its potential to affect economies. Natural gas is not merely an alternative fossil fuel and a substitute for crude oil but it also presents huge advantages in terms of cost and low carbon emission. This study contributes to the literature and provides meaningful evidence on how changes in natural gas futures prices affect the equity returns in G-5 economies using the data covering almost two decades. Our results present a few exciting insights. First, we find that oil and gas prices are weakly correlated and do not reflect the dependence of gas on oil as reported in previous studies. Second, we find that although gas price changes negatively affect equity market returns in the short run, it subsides substantially in the long run. Holistically, our findings identify the hedging potential that natural gas provides to cope with the volatility in crude oil prices. We also assert that substituting oil with natural gas may improve the sustainability and stability of financial markets in G-5 economies.

Dynamic Shocks of Crude Oil Price and Exchange Rate on Food Prices in Emerging Countries of Southeast Asia: A Panel Vector Autoregression Model

This paper empirically analyzes food price responses to shocks from crude oil price and exchange rate volatility in five emerging Southeast Asian nations between 2000 and 2020 using impulse response functions and variance decomposition analysis of the dynamic panel Vector Autoregression approach. Based on the findings of the impulse response functions analysis, food prices respond positively to both oil price and exchange rate shocks. Meanwhile, the results of variance decomposition analysis show that food prices account for a significant portion of volatility in its own shock. The contribution of oil price shock to food price volatility is found to be greater than the contribution of exchange rate shock. Hence, the study recommends that policymakers in these nations should be vigilant about the impacts of oil price and exchange rate shocks on food prices since these factors may undermine price stability and exacerbate food security.

Oil Price Uncertainty and Industrial Output in Nigeria

Concerned by the dismal performance of Nigeria’s industrial sector as evidenced by the sector’s inauspicious and uneven contributions to the country’s aggregate output and total government earnings, this study has been embarked upon to examine the impact of crude oil price volatility on industrial output in Nigeria. The Ordinary Least Square (OLS) technique was employed in estimating the time series data used in the study which spans from 1981–2019. The Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model was estimated to capture crude oil price fluctuations and government oil revenue fluctuations by obtaining the conditional variances from the estimated results. The study found that crude oil price fluctuation has negative impact on industrial output, while an increase in government oil revenue fluctuation results in a significant decrease in industrial output. It was also found that an increase in unearned government oil revenue leads to an insignificant decrease in industrial output, while government recurrent expenditure exerts positive but insignificant impact on industrial output. Domestic investment was found to have a positive and significant impact on industrial output while population growth has a positive and significant impact on industrial output. The study concludes that it is government’s financial imprudence that allows oil price volatility to be hazardous to Nigeria’s industrial output. It recommends more effective export diversification policies and a pro-development government financial structure that takes erratic oil revenue and inimical effects of high unproductive expenditures into account.

To jump or not to jump: momentum of jumps in crude oil price volatility prediction

A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price volatility. To address this issue, we find a phenomenon, “momentum of jumps” (MoJ), that the predictive ability of the jump component is persistent when forecasting the oil futures market volatility. Specifically, we propose a strategy that allows the predictive model to switch between a benchmark model without jumps and an alternative model with a jump component according to their recent past forecasting performance. The volatility data are based on the intraday prices of West Texas Intermediate. Our results indicate that this simple strategy significantly outperforms the individual models and a series of competing strategies such as forecast combinations and shrinkage methods. A mean–variance investor who targets a constant Sharpe ratio can realize the highest economic gains using the MoJ-based volatility forecasts. Our findings survive a wide variety of robustness tests, including different jump measures, alternative volatility measures, various financial markets, and extensive model specifications.

Performance of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Modeling Volatility of Brent Crude Oil Price

Fluctuations in the price of crude oil determines the economic state of many nations especially the oil producing ones, it becomes important to examine these fluctuations, hence the GARCH models were applied to model the volatility in the price of brent crude oil to determine the best model suitable for predicting future volatility. Descriptive statistics of the stock price and its returns were obtained, some inferential tests were employed to examine the stationarity and the goodness of fit of the daily price and the return under different distributions – the Gaussian, the Student t and the Generalized Error Distributions. Results showed that the price of crude oil dropped between 2014 and 2016 and drastically dropped in 2020. Among the competing models, Exponential GARCH(1,1) with Student t distribution was the best model with the least values of AIC, SBIC and HQIC respectively. The result showed negative significant value in the coefficient of its asymmetric parameter, suggesting that bad news or vital event such as COVID19 has larger effect on the volatility in the price of the Brent crude oil. This study therefore recommended the use of Exponential GARCH model in modelling or predicting volatility in the stock price of crude oils.

MEASURING THE IMPACT OF COVID-19 PANDEMIC ON THE OIL PRICES VOLATILITY BY GARCH MODELING

The aim of this work is the study of crude oil price volatility, particularly during the Covid-19 pandemic, which changed the world scene and inflicted serious crises not only in the global health system, but also in the international financial markets and economy. This economic situation has many policy makers, financiers and portfolio managers worried about avoiding the risk of potential damage. Hedging strategies are based on the correct estimation of price volatility. For this aim, we use the $GARCH$ model to measure the impact of volatility and shocks. More precisely, the model used for predicting volatility associated with the price variable is the $GARCH(1,2)$ model, in through this analysis. Although the $EGARCH$ and $TGARCH$ models are better for their asymmetry property of volatility, but the $GARCH(1,2)$ model was adopted because it presents lower values of the forecasting criteria compared to the two other models. The forecast is made for the last thr\'{e}e months of 2021. The result concludes that the predicted values and the current values are very close. The oil price series that will be examined here is $WTI$ (West Texas Intermediate).

COVID-19 Pandemic and Volatility Persistence of the Nigerian Crude Oil Price

Impacts of COVID-19 pandemic on the global economy cannot be overemphasized, especially with Nigeria, which largely depends on crude oil as a major source of her revenue. Thus, investigating COVID-19’s impacts on the volatility persistence of Nigerian crude oil price forms the nucleus of this study. Our modelling framework was based on GARCH, EGARCH and GJR-GARCH with two asymmetric innovation distributions Daily price data on the Nigerian crude oil sales (in dollars per barrel), ranging from 4th Jan., 2010 to 27th May, 2021, were obtained from the Central Bank of Nigeria (CBN). To capture the impact of the pandemic, the data were divided into two periods, before Covid-19 was proscribed as a pandemic by World Health Organisation (01/04/2010 to 10/03/2020) and during COVID-19 pandemic (11/03/2020 to 27/05/2021). Result shows that the leverage effect were positive and significant in both periods which indicates that positive shocks increases volatility more than negative news of the same sign. Also, EGARCH-SSTD and GJR-GARCH (1,1)-SSTD were the best fitted models for before and during pandemic respectively. Result shows that volatility persistence was higher during COVID-19 period (1.012639) than before the COVID-19 pandemic (0.988749) .There was also an increase and over persistence in the volatility of Nigerian crude oil price during COVID-19 than before COVID-19 period.

One-Step Biodiesel Production from Waste Cooking Oil Using CaO Promoted Activated Carbon Catalyst from Prunus persica Seeds

In recent years, the scope for replacing fossil fuels has been appealing to the world, owing to limited conventional fuels, crude oil price volatility, and greenhouse gas emission concerns. In this regard, this article demonstrates the preparation of a novel solid base catalyst for the transesterification of waste cooking oil. A calcium-loaded activated carbon catalyst was prepared through pyrolysis of peach shell followed by chemical activation with KOH and then calcium loading through the wet impregnation method. The prepared catalyst showed the best performance with 20% calcium loading and 650 °C of calcination temperature. The catalyst’s physicochemical, structural, and textural properties were examined using XRD, FTIR, SEM, EDX, and BET analysis. The catalyst showed a maximum yield of 96% at optimized conditions, i.e., 65 °C temperature, oil to methanol ratio 1:8, 5 wt% catalyst concentration, and a 160 min reaction time. Additionally, it illustrated high recyclability up to 10 cycles with negligible leaching of Ca+2 ions. The high activity of the catalyst was due to the presence of calcium ions on the activated carbon support. Physio–chemical properties and GC-MS analysis of prepared biodiesel determined that all attributes were within the biodiesel standard tolerances set by ASTM D6751 and EN 14214. Therefore, all the innovations mentioned above concluded that catalyst generated from peach shell biochar is a promising candidate for biodiesel production, ultimately resulting in solid and liquid waste management.

Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe

Crude oil price volatility impacts the global economy in general, as well as the economies of Europe and the United States in particular; it is supremely difficult to describe its tendency precisely, hence it leads to a forecasting methodology. This study aims to use the autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) approaches to cope with this problem in the United States and Europe. The data was gathered from the U.S. Energy Information Administration and federal research economic data (FRED) from January 2017 to September 2021. Simultaneously, values from January 2017 to March 2021, with 51 observations accounting for 90% of the total samples, were employed for the training phase, and the rest were used for the testing phase. The forecast result also indicated that the root mean square error (RMSE) and mean absolute percentage error (MAPE) values, applied by ARIMA models in Europe and the United States, have higher accurate indicators than SARIMA models. As a result, the ARIMA model achieved the best accuracy in both Europe and the USA, with MAPEEurope−ARIMA = 0.05, and MAPEUSA−ARIMA=0.05. Based on these accuracy parameters, the forecasting models appear incredibly reliable; similarly, the study results might assist governing bodies in making significant decisions, thereby accelerating socio-economic development in the world’s two largest economies.

Oil price volatility predictability based on global economic conditions

This study aims to examine the forecasting ability of five global economic activity proxies for WTI crude oil price volatility and construct a new index to improve the accuracy of WTI crude oil price volatility forecasts. We focus on the Global Economic Conditions Index (GECON) derived from 16 indicators related to real economic activity and adopt the autoregressive (AR) framework, along with three common indexes constructed by three dimensionality reduction approaches (scaled principal component analysis (sPCA), principal component analysis (PCA) and partial least squares (PLS)). The out-of-sample results show that the model incorporating the Global Economic Conditions Index (AR-GECON) has the strongest predictive power among the five global economic proxy models. More importantly, our newly constructed PLS model outperforms all the other forecasting models, including AR-GECON.

Integrating the Role of Green Fiscal Policies With Energy Prices Volatility and Energy Efficiency: Presenting a COVID-19 Perspective

Energy and other related sectors are changing in China. This study attempted to estimate the energy product price volatility with energy efficiency during COVID-19 with the role of green fiscal policies. For this, we applied unit-root tests, ADCC-GARCH, and CO-GARCH techniques to infer the study findings. The results showed that energy price volatility was significantly connected until 2018. More so, the green fiscal policies were significantly connected between energy product price volatility and energy efficiency during COVID-19 (2019–2020). From energy products, the crude oil price volatility was significant at 16.4%, heating oil volatility was significant at 18.2%, natural oil price volatility was 9.7%, gasoline price volatility was 28.7%, and diesel price volatility was 34.1% significant with energy efficiency, due to the intervening role of green fiscal policies. The findings of this study are robust in comparison to previous studies. Multiple stakeholders can take guidelines from the findings of the recent study. As per our best understanding and knowledge, if suggested recommendations are implemented effectively, these results will help to enhance energy efficiency through green fiscal policies in the post-COVID period.

Identifying the Determinants of Crude Oil Market Volatility by the Multivariate GARCH-MIDAS Model

Many macro-level variables have been used in forecasting crude oil price volatility. This article aims to identify which variables have the greatest impact and give more accurate predictions. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model. By combining the log-likelihood function with adaptive lasso penalty, three most informative determinants have been identified, namely, macroeconomic uncertainty, financial uncertainty and default yield spread. Out-of-sample results show that using these three variables significantly improves prediction accuracy compared to baseline models. However, the variables widely studied by other scholars, such as the supply and demand of crude oil, industrial production index, etc., were not selected, indicating that the impact of these variables may be overestimated. When studying crude oil price volatility, macroeconomic and financial market uncertainties can be used as effective predictors for investors and market analysts. Crude oil market participants should focus on macroeconomic and financial market uncertainties to make risk management more efficient.

Whether to abandon or continue the petroleum product price regulation in China？

Petroleum product price regulations are usually adopted to alleviate the shocks of crude oil price volatility on macroeconomy by adjusting the petroleum product price. However, the regulation can contribute to social welfare by alleviating the price shock on the one hand, but it can reduce welfare due to its price distortions on the other hand. Thus, this study assessed the net social welfare effect using a Regime-Switching Dynamic Stochastic General Equilibrium Model, so as to inform policy-makers whether to abandon or continue the regulation. The crude oil price volatility were analysed using data from June 2000 to December 2019, and impulse responses analyses were conducted to investigate the effects of the regulation on different economic indicators. Finally, the net social welfare were assessed using a social welfare loss function. It is found that the regulation is effective in alleviating the price shocks to the macroeconomy, and consequently reducing social welfare loss in a short run. Thus, while the reform of China's petroleum product pricing mechanism should be market-oriented, the regulation is economically justified for short-term implementation to deal with crude oil price volatility. The study provides policy makers with important information on the reform of petroleum product price regulation.

How macro-variables drive crude oil volatility? Perspective from the STL-based iterated combination method

Based on the GARCH-MIDAS framework, this article mainly explores whether the Seasonal and Trend decomposition using Loess (STL decomposition) and the iterated combination approach can improve the prediction accuracy of crude oil price volatility by using macro variables. We introduce five macro variables, that is, consumer price index, interest rate, producer price index, industrial production index, and unemployment rate, into the long-term component in the GARCH-MIDAS model. Though the model including raw macro variables performs better than the model containing each STL-based single subsequence, we find that the iterated combination forecasts using the forecasts obtained by each subsequence are superior to the corresponding standard ones. Besides, when mean-variance investors perform asset allocation, they can obtain more certainty-equivalent-returns by applying our new iterated combination approach. Finally, various robustness checks can verify the above-mentioned findings. In short, this article may provide a new perspective for economic empirical applications and theoretical research.