Crude Oil Price Dynamics Research Articles

Energy price and returns: a threshold heteroscedasticity model approach

ABSTRACT The energy market is characterized by its intricate dynamics, influenced by various factors that result in erratic behavior, making it challenging to model energy price returns and volatility. In energy market, the changes in Brent and West Texas Intermediate (WTI) crude oil price yields and volatility are the focus of great interest and attention. Due to the influence of different periods and some economic factors, the fluctuation of crude oil price shows asymmetric characteristics. For exploring the asymmetric characteristics of energy market, we introduce the application of the threshold double autoregressive (TDAR) model to handle the complexities of energy price returns and volatility. The TDAR model is able to capture nonlinearities and regime shifts in the oil market, making it particularly suitable for understanding the inherent complexities in crude oil price movements. The empirical results implied that TDAR model demonstrates superior performance in capturing the intricate dynamics of crude oil prices compared to traditional asymmetric GARCH-type models, making it a valuable tool for market practitioners and policymakers. Our findings reveal the subtle movements in Brent and WTI prices and offer valuable insights for risk management and investment strategies, which provide an alternative for market practitioners and policymakers.

The tail risk of crude oil Price_Based on EPU and geopolitical risk perspective

This study investigates the conditional prediction of crude oil price growth rates as a function of geopolitical risk (GPR) and economic policy uncertainty (EPU), characterizing the extreme tail risk characteristics. Utilizing monthly data on crude oil futures price growth rates from 1997 Jan to 2022 Dec, we employ Quantile Regression and Growth at Risk models to capture the heterogeneous and asymmetric effects of GPR and EPU across different quantiles of the oil price distribution. The sample period covers major geopolitical events, economic recessions, and the emergence of new energy sources, providing a comprehensive view of the factors influencing crude oil market dynamics.The empirical findings reveal significant asymmetries in the forecast density function of crude oil price growth rates, with periodic right-skewed deviations during financial crises and epidemic situations, and prevalent left-skewed deviations during the remaining sample periods. The impact of EPU on crude oil prices is found to be more pronounced in the short to medium term, while the influence of GPR persists over a longer duration, exacerbating the upside risk of price growth. The study highlights the effectiveness of EPU and GPR in capturing the tail characteristics of crude oil price growth, offering valuable risk identification indicators for policymakers and market participants.Furthermore, the analysis uncovers the heterogeneous effects of EPU and GPR across different quantiles of the oil price distribution. The results indicate that the relationship between these uncertainty measures and crude oil prices varies depending on the market conditions and the specific quantile examined. This finding underscores the importance of considering the entire distribution of crude oil prices rather than focusing solely on the conditional mean.The study contributes to the existing literature by providing a comprehensive examination of the role of geopolitical risk and economic policy uncertainty in driving crude oil price dynamics, with a particular focus on tail risks. The findings have important implications for risk management strategies in the energy sector.By employing advanced econometric techniques and considering a wide range of geopolitical and economic factors, this study offers new insights into the complex and evolving nature of crude oil price dynamics. The results highlight the need for policymakers and market participants to consider the asymmetric and heterogeneous effects of uncertainty measures when assessing the risks and opportunities in the crude oil market.

Equilibrium pricing of European crude oil options with stochastic behaviour and jump risks

In this paper, we investigate the pricing of European crude oil options under nonlinear dynamics with stochastic behaviour and jump risks, incorporating the features of arising convenience yield of crude oil and potential extreme fluctuation in the dynamics of crude oil prices. We present a closed-form solution to European crude oil option prices under an incomplete market setting, after deriving the pricing kernel with the equilibrium method and determining the risk neutral dynamics of crude oil prices. We extend our model to a mean-reverting stochastic volatility case, which also admits an analytical formula for the equilibrium price of European crude-oil options. Finally, our model is shown to be consistent with a number of interesting facts documented in the recent literature.

Dependences and dynamic spillovers across the crude oil and stock markets throughout the COVID-19 pandemic and Russia-Ukraine conflict: Evidence from the ASEAN+6

This research studies WTI crude oil and stock price dynamics in Australia, China, Indonesia, India, Japan, Korea, Malaysia, New Zealand, the Philippines, Singapore, Thailand, and Vietnam. We investigate data from January 2, 2018, to June 13, 2023 using BEKK- and DCC-GARCH. Crude oil volatility affects ASEAN+6 stock markets. Crude oil volatility spillovers were not statistically significant during pre-COVID-19, COVID-19 outbreak, and Russia-Ukraine war periods. After the crisis, asymmetric volatility remained prominent. The findings suggest that investors diversifying their portfolios should consider dynamic volatility and crude oil-stock price correlations to maximize returns and decrease risk.

A weekly structural VAR model of the US crude oil market

We present a weekly structural Vector Autoregressive model of the US crude oil market. Exploiting weekly data we can explain short-run crude oil price dynamics, including variations related with the COVID-19 pandemic and with the Russia’s invasion of Ukraine. The model is set identified with a Bayesian approach that allows to impose restrictions directly on structural parameters of interest, such as supply and demand elasticizes. Our model incorporates both the futures-spot price spread to capture shocks to the real price of crude oil driven by changes in expectations and US inventories to describe price fluctuations due to unexpected variations of above-ground stocks. Including the futures-spot price spread is key for accounting for feedback effects from the financial to the physical market for crude oil and for identifying a new structural shock that we label expectational shock. This shock plays a crucial role when describing the series of events that have led to the spike in the price of crude oil recorded in the aftermath of Russia’s invasion of Ukraine.

Cascade networks model to predict the crude oil prices in Iraq

<span lang="EN-US">Oil prices are inherently volatile, and they used to suffer from many fluctuations and changes. Therefore, oil prices prediction is the subject of many studies in the field, some researchers concentrated on the key factors that could influence the prediction accuracy, while the others focused on designing models that forecast the prices with high accuracy. To help the institutions and companies to hedge against any sudden changes and develop right decisions that support the global economy, in this project the concept of cascade networks model to predict the crude oil prices has been adopted, that can be considered relatively as new initiative in the field. The model is used to predict the Iraqi oil prices since as its commonly known that the economy in Iraq is totally depend on oil. Therefore, it is vital to develop a better perception about the crude oil price dynamics because its volatility can cause a sudden economic crisis.</span>

Outlook of oil prices and volatility from 1970 to 2040 through global energy mix-security from production to reserves: A nonparametric causality-in-quantiles approach

In the midst of the global challenge of climate change induced by the consumption of fossil fuels, energy security remains a global concern. Thus, this study examines the role of energy security from the witty perspective of the production and reserves of the world’s leading energy mix (oil, natural gas, and coal) in the dynamics of global crude oil prices and volatility over the period of 1970–2040. Having discovered nonlinear dynamics in the relationship, we employ the nonparametric causality-in-quantiles test which differentiates causality-in-mean from the causality-in-variance. The results show that the causality-in-variance is exceedingly stronger than causality-in-mean for the predictability of oil price. The mean oil price is unpredictable by the production and reserves of the energy sources, except those of crude oil and natural gas at the lower quantiles. Moreover, the production and reserves of all the energy sources are strong predictors of the volatility of oil price for most of the quantiles. This is further confirmed by the causality-in-mean conducted for the actual oil price volatility series. In general, this study weighs in from the perspective of an effective policy instrument associated with the trilemma among crude oil price, energy security, and environmental sustainability.

Predicting the changes in the WTI crude oil price dynamics using machine learning models

This study aims to use a monthly dataset from 1991 to 2021 to predict West Texas Intermediate (WTI) oil price dynamics using U.S. macroeconomic and financial factors, as well as a global crisis and crashes. We used advanced machine learning models such as Logistic Regression, Decision Tree, Random Forest, AdaBoost, and XgBoost in this study. According to the results, the XgBoost and Random Forest models outperform traditional models. We also used DeLong statistical test procedures to accurately compare machine learning models' performance. In addition, the study used SHAP - SHapley Additive exPlanations values to support model evaluation and interpretability. This new outline highlights the critical features of the WTI crude oil price prediction and provides appropriate model explanations by utilizing the practical SHAP values. The empirical findings showed that machine learning models could successfully and accurately predict the trend of WTI crude oil price changes. Our findings are important for policymakers, companies, and investors, as well as long-term energy-based economic development.

Conditional Probability of Jumps in Oil Prices

The objective of this research is to model the behavior of oil returns. The volatility of oil returns is described through a TGARCH process. Conditional probability jumps are incorporated through uniform, double exponential and normal jump intensity distributions. We found that the volatility of oil returns follows the stylized facts of leptokurtosis, leverage effect and volatility clustering. The abnormal information that causes the jumps, can cause another type of unexpected changes in the following period and the intensity of the jumps has a negative effect on the probability of jumps in the next period. The dynamic model proposed can be extended to other markets and to multivariate time series modeling considering the dependence among the markets’ returns. The main contribution of this work is the estimation of the conditional probability of jumps depending on the previous behavior leading to a better description of the stochastic dynamics of crude oil prices. This will be useful for making better decisions regarding oil as an underlying asset in derivatives or in the formulation of better public policies.

Crude oil price determinants and multi-sectoral effects: A review

ABSTRACT In this article, we provide a structured review of crude oil price dynamics. Specifically, we summarize evidence on important factors determining oil prices, cover the impact of oil market shocks on the macro economy and the stock market, discuss how the financialization of crude oil markets affects oil market functionality and efficiency, and we then outline approaches for forecasting crude oil prices and volatility. By comparing the results of the most influential early contributions and recent studies, we can identify important developments and research gaps in each field. Thus, our review provides academics and practitioners newly engaging in crude oil research with an overview of what scientists know about crude oil dynamics and highlights which topics are particularly promising for future research.

An inquiry into the structure and dynamics of crude oil price using the fast iterative filtering algorithm

Abstract The identification of the temporal scales related to market activities is crucial for understanding the dynamics of international crude oil prices. Standard analysis techniques fail in producing consistently good results due to the non-linear behaviour of the oil market. In this paper we propose an innovative approach based on the concurring application of a new non-linear data analysis method, Fast Iterative Filtering (FIF), and a multi-scale statistical analysis (Standardized Mean Test). This approach proves to be able to separate automatically crude oil price data into three components: a long term trend, an intermediate or middle period behaviour, and a transitory or short-run behaviour. The economic meaning of each component is clearly identified as: high frequency variations, caused by normal supply-demand disequilibrium; medium term fluctuations, driven by geopolitical, financial, and technological shocks; a low frequency trend reflecting the global business cycle. All these results make the proposed approach a more performing tool for analysing oil price data structure and dynamics. Such a method, if coupled with different prediction techniques (e.g., ARIMA, ARCH, etc., or ANN, SVM, etc.), can potentially show higher performance than existing hybrid models in forecasting crude oil prices.

The dynamics of crude oil price and the real estate market in Saudi Arabia: A Markov‐switching approach

The volatility evidence of the International crude oil prices has overtime been linked to the dynamics and sector performances of states' economies. By employing the Markov switching regression model over daily time series data from October 3, 2005 to March 29, 2018, this study examines the response of the Saudi Arabia real estate market to the crude oil price dynamics. An empirical observation revealed that there is statistical evidence of significant and positive impact of crude oil price return on the real estate market in the regimes. Am empirical evidence shows that the impact is higher in Regime 2 even as the global factor (proxied by volatility index) is evidently significant. Although the regimes are persistent, the expected regime duration of the stable regime is of the higher quarter. This investigation is statistically significant and robust, especially when OPEC price of crude oil is used in lieu of the Europe Brent price. By a further estimation techniques, it affirms that the model exhibits nonlinearity rather than a linear relationship. The estimation result encourages more strict energy policies such as the implementetion of energy diversification policy among others, thus rendering a panacea to Saudi Arabia's energy and the real estate challenges.

Crude oil price dynamics with crash risk under fundamental shocks

Our paper presents a crude oil price model in which the price is confined in a wide moving band. A price crash occurs when the price breaches the lower boundary where a smooth-pasting condition is imposed. Using an asymmetric mean-reverting fundamental (supply/demand) shock, the solution derived from the oil price equation for the model shows the oil price follows a mean-reverting square-root process, which is quasi-bounded at the boundary. The oil price dynamics generates left-skewed price distributions consistent with empirical observations. A weakened mean-reverting force for the price increases the probability leakage for the price across the boundary and the risk of a price crash. The empirical results show the oil price dynamics can be calibrated according to the model, where the mean reversion of the price dynamics is positively co-integrated with the oil production reaction to negative demand shocks, and with the risk reversals of the commodity currencies, the Canadian dollar and the Australian dollar in currency option markets. The results are consistent with an increased price crash risk with negative demand shocks and negative risk reversals. The forecasting performance of the oil price model is better than the futures-spread models and random walk models during the crash periods. While the price of oil was above the lower boundary for most of the time, the conditions for breaching the boundary were met in 2008 and 2014 when the price fell sharply.

Crude Oil Price Dynamics with Crash Risk Under Fundamental Shocks

Our paper presents a crude oil price model in which the price is confined in a wide moving band. A price crash occurs when the price breaches the lower boundary where a smooth-pasting condition is imposed. Using an asymmetric mean-reverting fundamental (supply/demand) shock, the solution derived from the oil price equation for the model shows the oil price follows a mean-reverting square-root process, which is quasi-bounded at the boundary. The oil price dynamics generates left-skewed price distributions consistent with empirical observations. A weakened mean-reverting force for the price increases the probability leakage for the price across the boundary and the risk of a price crash. The empirical results show the oil price dynamics can be calibrated according to the model, where the mean reversion of the price dynamics is positively co-integrated with the oil production reaction to negative demand shocks, and with the risk reversals of the commodity currencies, the Canadian dollar and the Australian dollar in currency option markets. The results are consistent with an increased price crash risk with negative demand shocks and negative risk reversals. The forecasting performance of the oil price model is better than the futures-spread models and random walk models during the crash periods. While the price of oil was above the lower boundary for most of the time, the conditions for breaching the boundary were met in 2008 and 2014 when the price fell sharply.

DID THE US SHALE OIL REVOLUTION RUIN OIL INDUSTRY STOCK MARKET RETURNS?

The stock market performance of the US oil industry is evaluated against a combination of benchmarks before and after the US shale oil revolution, in order to ascertain whether the increase in US shale oil production had an adverse impact on oil industry stock market returns. In 2014, the dynamic of the global crude oil supply-demand situation was such that the oil price fell because of the increased US crude oil production. Saudi Arabia and the other major producers preferred to have low oil prices, at least temporarily, in order to penalize the US shale oil players. Any oil price increase since then is seen as leading to an increase in US crude oil supply which then further reduces oil prices. The oil industry outperformed the benchmarks prior to the ramp up of US crude oil production led by the shale oil revolution, but the industry underperformed the benchmarks after these production increases. Hence, the US shale oil revolution did ruin the oil industry stock market returns. Several topics for discussion are included: The US Shale Oil Revolution; World Crude Oil Markets; Crude Oil Price Dynamics in the US; and Crude Oil Price Impacts on Oil Companies. Keywords: Shale Oil Revolution, Crude Oil Price, Oil and Gas Industry, Stock Market Returns JEL Classifications: Q32, Q41, Q43 DOI: https://doi.org/10.32479/ijeep.9171

Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning

A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN-S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way of improving the BN-S model with the implementation of various machine learning algorithms is proposed. This refined BN-S model is more efficient and has fewer parameters than other models which are used in practice as improvements of the BN-S model. The procedure and the model show the application of data science for extracting a "deterministic component" out of processes that are usually considered to be completely stochastic. Empirical applications validate the efficacy of the proposed model for long range dependence.

Coronavirus and Oil Price Crash

Coronavirus (COVID-19) creates fear and uncertainty, hitting the global economy and amplifying the financial markets volatility. The oil price reaction to COVID-19 was gradually accommodated until March 09, 2020, when, 49 days after the release of the first coronavirus monitoring report by the World Health Organization (WHO), Saudi Arabia floods the market with oil. As a result, international prices drop with more than 20% in one single day. Against this background, the purpose of this paper is to investigate the impact of COVID-19 numbers on crude oil prices, while controlling for the impact of financial volatility and the United States (US) economic policy uncertainty. Our ARDL estimation shows that the COVID-19 daily reported cases of new infections have a marginal negative impact on the crude oil prices in the long run. Nevertheless, by amplifying the financial markets volatility, COVID-19 also has an indirect effect on the recent dynamics of crude oil prices.

On the pernicious effects of oil price uncertainty on US real economic activities

The last five decades have witnessed dramatic changes in crude oil price dynamics. We identify the influence of extreme oil shocks and changing oil price uncertainty dynamics associated with economic and political events. Neglecting these features of the data can lead to model misspecification that gives rise to: firstly, an explosive volatility process for oil price uncertainty, and secondly, erroneous output growth dynamic responses to oil shocks. Unlike past studies, our results show that the sharp increase in oil price uncertainty after mid-1985 has a pernicious effect on output growth. There is evidence that output growth responds symmetrically (asymmetrically) to positive and negative shocks in the period when oil price uncertainty is lower (higher) and more (less) persistent before (after) mid-1985. These results highlight the importance of accounting for outliers and volatility breaks in oil price and output growth and the need to better understand the response of economic activity to oil shocks in the presence of oil price uncertainty. Our results remain qualitatively unchanged with the use of real oil price.

Determinants of High Crude Oil Price: A Nonstationary Extreme Value Approach

There are a number of factors that influence crude oil prices. We use a nonstationary extreme value approach to model crude oil prices. The model includes world oil demand, supply, dollar index, oil consumption, geopolitical and economic events as exogenous factors influencing crude oil prices. We assess the change in crude oil price dynamics as the consequence of changes in covariates. The primary objectives of the study are as follows: firstly, to investigate the impact of some exogenous variables on high crude oil prices; secondly, to fit the best nonstationary generalized extreme value model; thirdly, to compute the return level of crude oil prices for certain values of exogenous variables; fourthly, to compute the probability of crude oil prices which are extreme in nature; and finally, to measure the relative risk of a high oil price due to different covariate values. The finding shows that it is reasonable to model extreme oil prices using the nonstationary extreme value approach as opposed to the stationary approach. Moreover, the study shows that some of the independent variables considered here have significant roles in high crude oil prices and their changes create high risk of occurring extreme crude oil prices.

The ethanol mandate and crude oil and biofuel agricultural commodity price dynamics

We investigate mean and volatility spillovers between the crude oil market and the main biofuel feedstock markets (corn, soybean, and sugar). In doing so, we estimate a four-variable vector error correction (VEC)–GARCH–in–Mean model with a BEKK representation for the variance equation, and also examine the possible effects of the ethanol mandate by including a dummy variable in both the conditional mean and variance equations. We find that the oil market and the biofuel feedstock markets are tightly interconnected and that the ethanol mandate has strengthened their linkages in terms of volatility spillovers.