Oil Price Volatility Research Articles

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.

Economic policy uncertainty, oil price volatility and stock market returns: Evidence from a nonlinear model

This paper investigates the nonlinear relationship between economic policy uncertainty, oil price volatility and stock market returns for 25 countries by applying the panel smooth transition regression model. We find that oil price volatility has a negative effect on stock returns, and this effect increases with economic policy uncertainty. Furthermore, there is pronounced heterogeneity in responses. First, oil-exporting countries whose economies depend more on oil prices respond more strongly to oil price volatility than oil-importing countries. Second, stock returns of developing countries are more susceptible to oil price volatility than that of developed countries. Third, crisis plays a crucial role in the relation between oil price volatility and stock returns.

Volatility spillover from the global oil price to ASEAN stock markets: A cross-quantilogram analysis

This paper investigates the link between the volatility of global oil prices and ASEAN stock market indices using the cross-quantilogram approach developed by Han et al. (2016). We find that a large and medium change in the global oil prices could result in persistent and robust volatility in the stock index of almost ASEAN markets. Moreover, Vietnam is a unique stock market sensitive to the slight change in global oil price, although it is not an instant response. This study offers strong implications for investors in optimising their portfolios. Besides, understanding the risk spillover from the global oil market to the stock market helps policymakers enact more appropriate policies to reduce equity volatility.

Robust Exploration and Production Sharing Agreements Using the Taguchi Method

The short- and long-term volatility of oil and gas prices has a wide-ranging impact on both parties of petroleum contractual agreements, thus affecting the profitability of the project at any stage. Therefore, the government (first party) and the international oil company (second party) set the parameters of their contracts in a way that reduces the uncertainty. The effect of price fluctuations on economic indicators is investigated in this paper. The Taguchi method is used for the first time to find the best-agreement parameters, which are the “A” and “B” factors, in the standard Libyan agreement. There are four “A” components from “A1” to “A4”, and four “B” components from “B1” to “B4”. The purpose is to reduce the variability in the response variables, which are the company take (the percent of net cash flow for the international company) and average value of the second-party percent share of production (ASPS). The noise factors considered in this paper are oil, liquefied hydrocarbon byproduct (LHP), and gas prices. The method was applied to a case study of oil field development in Libya. The results showed that “A3” and “A4” were the most important control factors that affect the ASPS, while “B2” and “B3” are the most important factors affecting the company take. To obtain robust results, the most important factors to reduce variability were also determined. The effect of control parameters on the average NPV may be worth more than USD 22 MM in the 1-billion-barrel oilfield case study. The results showed that, for a given combination of “A” and “B” factors with a certain company take, the mean absolute deviation (MAD) of the NPV of the second party was reduced by 18% if the optimal combinations of the levels were used.

The impact of Islamic events on herding behaviour in Saudi Arabian equities market

AbstractUsing data from Saudi Arabia, a strictly religious society, we examine how Islamic events (i.e., Ramadan, Eid‐ul‐Fitr, Eid‐ul‐Adha and Ashoura) moderate the impact of social mood (positive and negative) on herding in the stock market. We use the cross‐sectional absolute deviation (CSAD) of returns and find that investors' mood during Islamic events of Eid‐ul‐Fitr, Eid‐ul‐Adha and Ashoura significantly affects herding behaviour in the market. Our results, however, contrast with existing evidence of herding in the month of Ramadan. Overall, results are robust after controlling for market conditions (i.e., domestic and US market returns, liquidity, sentiments, and oil price volatility) and crisis events (i.e., global financial crisis and Arab Spring). Though most prior research investigates the impact of individual Islamic events on stocks as seasonal anomalies, our study contributes by jointly exploring how four key Islamic events, associated with contrasting moods, induce diverse herding patterns in the Saudi stock market.

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.

The nexus of carbon emissions, oil price volatility, and human capital efficiency

The corporate sector's active engagement is vital for achieving a net-zero future. It entails a sizeable investment in human capital to foster more conscious efforts to limit carbon emissions. Therefore it is critical to evaluate the nexus between carbon emissions and human capital efficiency. This paper analyzes the link between human capital efficiency and carbon emissions using a comprehensive sample of 5740 firms across eight countries spanning over ten years. Our findings show a negative relationship between investment in human capital and carbon emissions. We emphasize that optimal human capital efficiency can help limit emissions by fostering the transitions to renewable energy sources and the development of cognitive appreciation. The results remained robust for periods marked by booming and receding oil prices. These findings have important implications for optimizing firm performance while preserving sustainable development goals.

Testing oil price volatility during Covid-19: Global economic impact

This paper is occasioned by the current events in the crude oil markets throughout the Covid pandemic time. The study analyzes the evolving nature of crude oil cost unpredictability caused by the variations that influence the crude sector throughout the current contagion. Every day's dataset is within the first month of 2020 and December 30, 2021 were measured by applying VAR and GARCH models. The results corroborate that the current contagion has adverse effects on the crude sector, primarily in two ways. It resulted in the headwinds for demand and cut international demand for crude oil, increasing uncertainty for major advanced and developing nations. Next, it resulted in output headwinds as the pandemic caused hydrocarbons conflicts among the leading crude supplying countries. The two headwinds seem to have caused the more than necessary crude unpredictability. Moreover, it was found that the United States output, total requirements, and crude-leaning demand shocks adversely affect the supply unpredictability of the United States and the extractive sectors. The findings depict that crude price instability responded significantly to the contagion caused by crude headwinds. Specifically, the study recorded the effect of uncertainty because of these headwinds beyond financiers' concerns about crude price instability. This study indicates that spillovers do not have meaningful forecast data, igniting critical debates concerning the relevance of the spillover indicator for predicting at minimal sampling occurrence.

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.

Central African Economic and Monetary Community

CEMAC ended 2021 in a fragile external position, with gross reserves at only 2.7 months of prospective imports and net foreign assets (NFA) at their lowest level in decades, despite the availability of Fund financing, the SDR allocation, and monetary policy tightening. The terms of trade shock this year is expected to be broadly positive for CEMAC. This more favorable outlook is, however, subject to heightened external uncertainties associated with the fallout from the war in Ukraine (notably global inflation pressure, global growth uncertainties, and high oil price volatility), faster-than-anticipated global financial tightening, possible emergence of new COVID strains and risks from cryptoassets. Current high oil prices, if sustained, will help rebuild fiscal and external buffers, provided fiscal policies remain prudent. Shielding vulnerable populations from soaring energy and food prices adds to the complexity of navigating this uncertain environment, given CEMAC’s already limited policy options.

Oil price volatility and industrial productivity: a comparative analysis of Nigeria and Egypt

This study examines the relationship between oil price volatility and industrial productivity in Nigeria and Egypt from 1980 - 2020. The study employs cointegration analysis and the Error Correction model to analyze data. The result shows that oil price volatility hinders industrial productivity in both Countries. But the magnitude of the effect was more in Nigeria than in Egypt. It can be traced to the fact that Nigeria is an importing Country while Egypt is a net exporting country. Also, Nigeria has neglected important sectors like the Industrial sector at the advent of oil, which made Nigeria a mono-product country for decades. Based on these findings, the study recommended diversification of the export revenue base for Nigeria to minimize reliance on oil. Also, alternative energy sources such as biofuel and solar power plants should be developed for the two countries to reduce dependency on oil consumption.

Examining the spillover effects of volatile oil prices on Iran’s stock market using wavelet-based multivariate GARCH model

Fluctuations in the oil market can significantly influence various sectors of the economy, such as the stock markets of countries that rely heavily on oil revenues. Oil prices are one of the key influential external factors affecting the stock exchange index of oil-dependent Iran. This paper investigates the spillover effects of oil prices on Iran’s stock exchange index weekly from March 2009 to March 2020. Using a time-series wavelet decomposition approach, a series of OPEC oil prices and Iran’s total stock market index were decomposed into various time scales (4 levels) to analyze oil market spillover into the stock market using the multivariate GARCH TBEKK model. The results confirmed that volatility spillover from the oil to the stock market occurred in all the time scales (short, medium, and long term). However, the spillover in the long term is more pronounced than over the short, demonstrating that stock market volatility is strongly influenced by long-term exogenous oil price fluctuations. Hence, oil market shocks are one of the influential factors affecting stock market turbulence in Iran.

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.

Will Oil Price Volatility Cause Market Panic?

It is generally known that violent oil price volatility will cause market panic; however, the extent to which is worthy of empirical test. Firstly, this paper employs the TVP-VAR model to analyze the time-varying impacts of oil price volatility on the panic index using monthly data from January 1990 to November 2021. Then, after using the SVAR model to decompose the oil price volatility, this paper uses the PDL model to analyze the heterogeneous impacts of oil price volatility from different sources. Finally, based on the results of oil decomposition, this paper uses the TARCH model to analyze the asymmetric impacts of oil price volatility in different directions. The results show that: (1) oil price volatility can indeed cause market panic, and these impacts exhibit time-varying characteristics; (2) oil price volatility from different sources has different impacts on the panic index, and the order from high to low is oil-specific demand shocks, supply shocks, and aggregate demand shocks; and (3) oil price volatility has asymmetric impacts on the panic index, and positive shocks have greater impacts than negative.

Does the volatility spillover effect matter in oil price volatility predictability? Evidence from high-frequency data

This paper examines whether volatility spillovers between oil and equity markets can improve the predictive power of oil's realized volatility. This paper uses intraday data for the WTI futures market and the U.S. and Chinese equity markets from January 4, 2006, to August 1, 2018, and the heterogeneous autoregressive (HAR) model as the benchmark. Our results reveal that the spillover effect has significant predictability and can indeed be applied to the prediction field. First, the results of in-sample estimation show that spillovers transmitted by equity markets have significant predictive information on the oil market. Second, the results of the out-sample provide evidence that net pairwise spillovers transmitted by the Chinese equity market provide significant forecasting gains and those transmitted by the U.S. equity market provide significant forecasting gains only during the precrisis period. Finally, we perform robustness tests, and the results are robust.

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.

How does oil price volatility affect unemployment rates? A dynamic stochastic general equilibrium model

Although empirical evidence reveals a strong correlation between oil price volatility and unemployment, the transmission mechanism of these two variables is unclear. We theoretically examine the effects of oil price uncertainty shocks on the unemployment rate using a dynamic stochastic general equilibrium model incorporating a search-and-match component. We find that an unexpected increase in oil price volatility leads to a persistent increase in the unemployment rate. Comparing the oil price uncertainty shock with supply and demand uncertainty shocks in the final goods market, we observe that the oil price uncertainty shock has effects analogous to those of the supply uncertainty shock. In addition, we demonstrate that oil as a production input mitigates the adverse effects of uncertainty shocks to supply and demand sides of final goods market.

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.

Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?

Bitcoin has grown in popularity and has now attracted the attention of individual and institutional investors. Accurate Bitcoin price direction forecasts are important for determining the trend in Bitcoin prices and asset allocation. This paper addresses several unanswered questions. How important are business cycle variables like interest rates, inflation, and market volatility for forecasting Bitcoin prices? Does the importance of these variables change across time? Are the most important macroeconomic variables for forecasting Bitcoin prices the same as those for gold prices? To answer these questions, we utilize tree-based machine learning classifiers, along with traditional logit econometric models. The analysis reveals several important findings. First, random forests predict Bitcoin and gold price directions with a higher degree of accuracy than logit models. Prediction accuracy for bagging and random forests is between 75% and 80% for a five-day prediction. For 10-day to 20-day forecasts bagging and random forests record accuracies greater than 85%. Second, technical indicators are the most important features for predicting Bitcoin and gold price direction, suggesting some degree of market inefficiency. Third, oil price volatility is important for predicting Bitcoin and gold prices indicating that Bitcoin is a substitute for gold in diversifying this type of volatility. By comparison, gold prices are more influenced by inflation than Bitcoin prices, indicating that gold can be used as a hedge or diversification asset against inflation.