Oil Prices Research Articles

Effects of oil shocks on markets in G7 countries

This paper aims to assess the effects of oil price shocks on the stock markets of the G7 countries. We develop an oil sentiment indicator that measures the volatility of oil prices. We analyze oil shocks on the stock market of the G7 countries, using a structural VAR and Local Projection approach. Our results suggest that oil shocks explain 8% of the US stock market, 10% in Germany, and 7% in the UK. These results point out the possibility of using this sentiment variable for forecasting the stock market’s volatility.

Risk premium, price of risk and expected volatility in the oil market: Evidence from survey data

This paper contributes to the literature on crude oil risk premiums by providing ex-ante measures of these premiums using survey oil price expectations over an extended period. These ex-ante premiums are uncorrelated with ex-post premiums commonly used in existing studies, whereas they are more relevant as they directly influence investors' decision-making. Utilizing a portfolio choice model, we explain the ex-ante premium as the product of the price of risk and the expected variance, both varying over time and across horizons. We estimate this relationship using a multivariate state-space framework. From our estimated risk prices we find, on average, that investors exhibit risk-seeking behaviour in the short term and risk aversion in the long term. It follows that the term structure of oil risk premiums are prominently upward-sloping. Additionally, consistent with the prospect theory, investors are found to be predominantly risk averse in a context of expected gains and risk-seeking in a context of expected losses. Finally, the dynamics of risk prices are shown to be driven by identifiable economic, financial, and oil market-related factors.

Are All Shocks Alike? Evidence From the Effects of Oil Shocks on Military Expenditure in Nigeria

This study investigated the asymmetric effects of oil shocks–oil price, oil demand and oil supply shocks–on military expenditure in Nigeria. Our study confirmed the presence of short-run and long-run asymmetries in all the three models used. Generally, positive oil price and demand shocks positively influence military expenditure and vice versa for negative shocks. However, a positive oil supply shock is detrimental to military expenditure.

The Model of Stock Pricing Banking Sector on the Indonesia Stock Exchange during the Government of Susilo Bambang Yudhoyono and Joko Widodo

Differences in macroeconomic conditions between the SBY and JKW government periods can significantly impact stock prices within the banking sector, especially when the economy experiences fluctuations, leading to increased stock price volatility. This research aims to assess the influence of various macroeconomic factors, including the Global index, macroeconomics, world oil prices, China index, Arab index, competitive resources, and inflation, on banking sector returns from October 2004 to October 2014. The analysis utilizes the arbitrage pricing theory, difference test, and mean absolute deviation. The study divides the analysis into high-return and low-return groups for each government period. Findings indicate that macroeconomic factors exert a simultaneous influence on return groups during both SBY and Jokowi's administrations. However, for JKW high returns, these factors do not significantly impact them. Specifically, the Global Index, China Index, Arab Index, Resource Competitiveness, and Inflation show a positive correlation with both high and low returns during SBY term. Conversely, during JKW tenure, only the Global Index and China Index exhibit positive effects on high and low returns. The difference test reveals disparities in the Arab index for high and low returns of SBY, while only the Global index displays differences for JKW. The variations in high returns between SBY and Jokowi stem from discrepancies in the Global and Arab indices, while no distinctions exist in low returns between the two administrations. Additionally, the mean absolute return values for high returns during JKW era demonstrate the smallest accuracy, indicating potential limitations in the predictive power of the model during this period.

Analisis Pengaruh Harga Emas dan Minyak Mentah Periode Terhadap LQ45 Menggunakan Model ARDL

This research aims to analyze the influence of gold and crude oil prices on LQ45 using the ARDL approach. In the research carried out, researchers used the variables Gold Price and Oil Price. The data used is secondary data sourced from the websites www.yahoo.co.id and investing.com. Using purposive sampling technique and the research sample obtained was 240 time series data. The results of this research show that gold prices have a negative effect and crude oil prices have a positive effect in the long term on LQ45. The results of this research can be a reference material for investors in developing investment strategies, especially during a crisis.

The Macroeconomic Impact of Crude Oil Price Fluctuations in Nigeria

The main objective of the study was to determine the way fluctuations in the price of crude oil affected macroeconomic variables in Nigeria from 2000 Q1 to 2022 Q4. The data was analysed in the paper using Structural Vector Autoregressive (SVAR) models. According to the findings, Nigeria's current economic growth has been significantly impacted by lag GDP values. This indicates that economic growth in Nigeria was significantly impacted by one-period lag GDP by 3,791.7 units in the short-run and 7,430.4 units in the long-run. Also in the short-run, the current broad money supply was significantly impacted by both the one-period lag general price level (0.445 units) and the one-quarter lag broad money supply (757.7 units). According to the crude oil price fluctuation model, current crude oil price variations in Nigeria are significantly influenced by one-period lags in general price level (-64.9 units), crude oil price fluctuation (1,114.6 units), and fiscal budget deficits (0.65 units). All of the one-period lagged coefficients of macroeconomic variables, including real GDP, broad money supply, interest rates, general price level, inflation rates, and fiscal budget deficits, had a significant impact on current economic growth and broad money supply, respectively, as demonstrated by the long-run economic growth and broad money supply models. The real GDP's impulse response to fluctuations in crude oil prices fell during the first period, peaked during the second, and then increased to show a positive trend during the third. This study came to the conclusion that shocks or fluctuations in the price of crude oil are the main cause of macroeconomic policy instability in Nigeria. The study recommended that the government should control foreign exchange rate regimes, pursue expansionary monetary policy, general price level control and stabilisation measures.

Receiver or transmitter? Unlocking the role of green technology innovation in sustainable development, energy, and carbon markets

In the context of sustainable development, the energy crisis and the volatile carbon trading markets have made the integration of green technology increasingly essential. This study explores the spillover effects between green technology innovation (GTI), oil price, clean energy (CE), carbon emissions trading prices (CETP), and sustainable development (SD) using quantile connectedness. The static analysis reveals that the connectedness relationship in extreme quantiles is remarkably more related than the middle quantile. The dynamic results indicate that major events significantly strengthen the overall risk correlation, but spillover effects are asymmetric under different quantiles. Further analysis under extreme market conditions demonstrates that SD and CE have produced a continuous and steady risk spillover to GTI, while CETP increased the risk transmission to GTI following the COVID-19 pandemic. This research underscores the significance of coordinating the development of GTI, energy, carbon markets, and SD by investigating risk contagion mechanisms in various market scenarios. In terms of policy implications, the findings indicate that stable energy system and reasonable carbon prices can stimulate the advancement of GTI.

On the design of price caps as sanctions

A ceiling has been imposed on the price at which Russian producers can sell oil. The price cap is intended to reduce Russian government tax revenue without increasing the world price of oil excessively. We show that such price caps can have counterintuitive effects. A price cap can induce sanctioned producers to increase their output, thereby increasing their revenue. This increased output can also reduce the world price of the homogeneous product supplied by sanctioned and non-sanctioned producers. The welfare-maximizing price cap, which is often well below the unrestricted world price, can increase welfare substantially.

Forecasting of Monthly Crude Oil Prices in Kenya Using Comparative Time Series Models

Crude oil is one of the most vital products that ever existed and variation in prices affects all sectors of the economy and variation in its prices is very crucial. Therefore, without an accurate and appropriate predictive model for crude oil prices, it has proven difficult to predict future oil prices. Therefore, appropriate modeling is crucial for the oil companies to adjust strategies used in the production and supply as well as structural optimization. This study sought to fit several time series forecasting models namely; ARIMA, Naïve, Seasonal Naïve, Time Series Linear Model (TSLM), and Exponential Smoothing (ETS) to model and forecast crude oil prices. The study used Kenya’s monthly crude oil prices from Jan 2003 to Dec 2023, giving a sample of 252 observations. The selection of the best model was based on the minimization of the information criteria, where ARIMA, ETS, and Seasonal Naïve attained an AIC of 1294.193, 1846.780, and 1403.821, respectively. Similarly, the models attained the BIC of 1304.991, 1863.071, and 1450.489, respectively. Since the Naïve and TSLM could not provide the AIC and BIC, model selection was entirely based on the forecast accuracy measures. From the results, the Naive model reported the lowest RMSE (19.6329), indicating that it has the smallest average squared error, with the lowest MAE (15.5212), suggesting it has the smallest average absolute error. Besides, the Naive model reported an ME (1.9819) which is relatively low but not the lowest. The automatic ETS model has a slightly lower ME (3.478211). The naive model reported lower MPE and MAPE values (-3.9525 and 26.8022, respectively) compared to most models, indicating less percentage error. Similarly, a lower MASE and RMSSE were reported by the naïve model, (0.7343624) and RMSSE (0.7228792), respectively, indicating that the model performs well relative to forecasting crude oil prices in Kenya. The naïve model demonstrated a higher consistency and reliability in forecasting crude oil prices in Kenya.

Enhancing Multi-step Brent Oil Price Forecasting with Ensemble Multi-scenario Bi-GRU Networks

Accurate crude oil price forecasting is crucial for economic stability, investment planning, and strategic decision-making across various industries. Despite numerous research efforts in applying deep learning to time-series forecasting, achieving high accuracy in multi-step predictions for volatile time-series like crude oil prices remains a significant challenge. Moreover, most existing approaches primarily focus on one-step forecasting, and the performance often varies depending on the dataset and specific case study. This paper introduces ensemble-based deep-learning models to capture Brent oil price volatility and enhance the multi-step price prediction. Our methodology employs a two-pronged approach. First, we present an empirical comparison of deep-learning models and architectures, including RNNs, CNNs, and transformers, for forecasting Brent oil prices. We also examine the impact of various external factors on forecasting accuracy. Then, we introduce a novel approach that employs ensemble GRU-based models to enhance prediction accuracy across multiple forecasting scenarios. Extensive experiments were conducted using a dataset of historical Brent prices encompassing the COVID-19 pandemic, which significantly impacted energy markets. The results demonstrate that the proposed model outperforms benchmark and established models, achieving a 9.3% reduction in MSE compared to the closest benchmark model for a 3-day forecasting horizon.

COMPARATIVE ANALYSIS OF ECONOMIC DEVELOPMENT RUSSIA IN THE LONG TERM

The objective of this study is to analyze the relationship between the economic weight of Russia and its large neighboring countries (Japan, Germany, Turkey, and Poland) until the end of the 21st century. The primary research method is a systematic comparative analysis of key aspects influencing economic development. The analytical model is based on the dominant influence of human capital on economic dynamics. Among Russia's large neighboring countries, during the period from 2005 to 2023, Japan exhibited the slowest GDP growth by PPP with an exponential trend (3.0 times slower), followed by Poland (1.64 times slower) and Germany (1.4 times slower). Turkey's GDP growth by PPP was 27% higher than that of Russia, which outpaced the global average growth rate by approximately 16%. Russia's unexpected move from 6th to 4th place in the world by GDP in PPP terms is a result of the economy's heavy dependence on oil prices and a series of oil price crises in 2008, 2014, and 2020, which obscured the real growth of the Russian economy. It has been shown that periods of convergence in GDP by PPP between Russia and large neighboring countries (mainly Germany) have repeatedly coincided with military conflicts. These include the War of 1812, World Wars I and II, the Russia-Georgia conflict (2008), and others. In terms of population size, which is the most important factor in GDP growth, Russia has slightly outpaced Japan over the last 35 years (by 16.5% in 2023). Going forward, Japan's population will decline rapidly compared to Russia. The fastest-growing neighboring country, Turkey, will reach the closest point of convergence with Russia in population size by 2060, after which the gap will begin to widen. In terms of the proportion of the population with tertiary education (DT), Russia (57%) is ahead of the neighboring countries under consideration, but Russia's DT growth over the last 20 years has been only 3%, which is a significant problem.

Lesson from Crude Oil Treasure Failure in 2020

After the 2020 pandemic, the Chicago Mercantile Exchange (CME) changed its trading rules, allowing the negative oil price mechanism in the futures market. That same month, Bank of China's (BOC) "Crude Oil Bao" investment product brought huge losses to investors, exposing the product's risk management flaws. The purpose of this report is to conduct a comprehensive study of the factors that led to the losses suffered by Chinese investors in international crude oil product trading. It delves into the operational dynamics of these financial industry institutions, as well as the perspectives of Chinese banks and retail investors affected by the incident. Finally, recommendations are made to strengthen risk management measures in response to events such as the Crude Oil Treasure, with the aim of helping commercial banks to improve their product design methodologies. The article provides valuable insights into understanding financial product failures and offers guidance on mitigating risk.

A novel link prediction model for interval-valued crude oil prices based on complex network and multi-source information

Accurate crude oil price forecasting is crucial for maintaining energy economy stability and enhancing investment and operational decision-making. Nowadays, the link prediction model for crude oil prices based on a complex network model has become an emerging and promising approach. However, existing link prediction methods are unable to extract complex information about interval-valued crude oil prices, and fail to consider the impact of multi-source information. Therefore, this paper proposes the link prediction model for interval-valued crude oil prices using complex network and multi-source information, which converts crude oil price interval series into complex networks. Through link prediction, it takes into account both the impact of past interval-crude oil prices on future interval time series and the interference of external real-time information on interval-valued prices. First, news headlines and geopolitical risk indices related to crude oil prices are captured, and search index data and news sentiment scores associated with interval data are analyzed. Subsequently, the interval sequence data and related influencing factors are decomposed and reconstructed respectively. The reconstructed interval center and radius sequence are then converted into a directed network, and node similarity in the network is calculated to generate similar nodes of the interval subsequence as feature values. Finally, multiple machine learning models are employed to acquire the optimal weighted combination prediction with interval prediction values. According to the experimental data, this approach outperforms other benchmark methods in terms of prediction accuracy.

Interplay between oil prices, country risks, and stock returns in the context of global conflict: A PVAR approach

Employing the panel vector autoregressive (PVAR) modeling, we analyze the interplay among oil prices, country risks, and stock returns across twenty-nine economies from February 2005 to August 2020. We find that, in the short run, rising oil prices temporarily boost stock prices by reducing country risk. However, over longer horizons, reductions in country risk are linked to lower stock returns. Moreover, in the interaction between stock and oil markets we identify the heterogeneity of three forms of country risk: economic, financial, and political, particularly when comparing developed and developing economies. Findings, offering new insights into the linkages between oil and stock markets, especially in the context of increased global conflict context, are of much value for investment strategies and policy formulations aimed at mitigating risk.

A Time Trend and Persistence Analysis of Sunflower Oil and Olive Oil Prices in the Context of the Russia-Ukraine War

The imbalances between sunflower oil production (Ukraine) and olive oil production (Europe) due to the substitution effects caused by the Russian invasion of Ukraine on 24 February 2022 affected the prices. Given that Ukraine is the largest producer of global sunflower oil and Europe is the largest territory that produces olive oil, this scientific article tries to analyze the global prices of both vegetable oils and understand how the war between Russia and Ukraine has affected them. Advanced statistical and econometric methods to carry out this analysis have been used. It is found that the prices of both variables separately have similar behavior and that the shock caused by the war will be transitory, with the original price trend recovering in the long term using fractional integration methods. In a multivariate analysis, using a causality test in the frequency domain we observe that both variables are related to each other, and the effects of war will have an impact in the long term, with olive oil being the cause. A negative relationship between both variables measured with a wavelet analysis is also observed. Furthermore, if this trend continues, the price of olive oil would prevail over the price of sunflower in the war between Russia and Ukraine. Finally, a 12-month prediction is presented using artificial neural networks, where the price of olive oil will be high for at least 11 more months. The price of sunflower oil is predicted to last for only 5 more months.

How does oil policy uncertainty influence resource rents? New empirical evidence from Organization of the Petroleum Exporting Countries

AbstractThe uncertainty surrounding oil‐related policies has raised concerns about its influence on revenues derived from resource extraction activities. In this view, the current study aims to investigate the nuanced relationship between oil policy uncertainty (OPU) and resource rents, focusing on oil rents (ORTs), natural gas rents (NRTs), and total resource rents (TRT). Analyzing data spanning from 1985 to 2019 across Organization of the Petroleum Exporting Countries, various econometric models including DOLS, FMOLS, and autoregressive distributed lag are employed to assess the impact of OPU on resource rents. The empirical findings reveal a significant negative effect of heightened OPU levels on resource rents, indicating a reduction in ORT, NRT, and TRT. This negative impact underscores the deterrence of long‐term investments in oil exploration and production due to regulatory unpredictability, leading to decreased revenues from oil extraction activities. Additionally, increased OPU contributes to heightened volatility in oil prices, disrupting the stability of resource rents. Furthermore, variables such as FDI inflow, inflation rate, and banking sector development exhibit positive relationships with resource rents, emphasizing their role in bolstering revenues derived from natural resources. The study's implications highlight the necessity for policymakers to address and mitigate OPU to foster stability and sustainable revenues within resource‐driven economies. This study contributes to the existing literature by offering empirical insights into the adverse impact of OPU on resource rents.

Co-movement of Oil and Stock Markets During COVID-19: Evidence from the Gulf Corporation Council

This study aims to document the impact of oil price variations on generating stock returns in Gulf Cooperation Council (GCC) markets during the COVID-19 era. It documents the pandemic era results from January 2020 to October 2022 by employing cointegration, Granger causality, and time-varying coefficient-vector autoregression techniques on daily data. The findings suggest two-way causality between all stock indices and the oil market. Time-dependent relationships were observed during the review period. The findings (based on regression and variance decomposition) indicate that although the reliance of the GCC stock markets was not on oil alone, oil had a significant impact during the study period. It is recommended that investors not consider diversifying portfolios in GCC stocks and oil markets to optimize benefits. The findings are expected to enhance the understanding of academics, market players, regulators, and investors regarding relationships among GCC stocks and oil markets. This study contributes to the literature by documenting the impact of the oil market on stocks during an abnormal period of the COVID-19 pandemic, considering time-varying parameters in a net oil exporting region.

Economic activities, dry bulk freight, and economic policy uncertainties as drivers of oil prices: A tail-behaviour time-varying causality perspective

This paper examines the dynamic impact of economic activity, Baltic dry bulk and economic policy uncertainty on oil prices. Drawing on demand-side economic theory, we focus on how economic activity and economic policy uncertainty drive oil prices and subsequently contribute to the formation of market equilibrium. In this context, we first introduce a unidirectional quantile and time-varying Granger-causality mechanism for the drivers of oil prices. Our novel rolling window quantile Granger causality approach reveals that economic activity drives crude oil price fluctuations and highlights their important role in market imbalances. In particular, economic activity and the Baltic exchange rate index emerge as essential drivers of oil prices, confirming our hypothesis of a demand-side effect. Moreover, our robustness wavelet quantile correlation (WQC) analyses reveal that economic activity promotes oil price fluctuations along both long-term equilibrium trends and short-term dynamics. In contrast, economic policy uncertainty has a dampening effect on oil prices. More broadly, economic activity is positively correlated with the Baltic exchange dry index, highlighting its broader impact on global trade indicators. Moreover, we use a bivariate Quantile on Quantile regression method to identify the equilibrium searches of different thresholds and estimate the robustness of the validity of the WQC findings. Analyzing the quantile and frequency connectedness spillovers, we observe that shocks from economic activity are particularly pronounced in the tail quantiles, reinforcing the demand-side narrative. Overall, the increased risk in extreme tails underscores the increased interaction between economic activity, oil prices and economic policy uncertainties. Our findings have important implications for policymakers and highlight the need for adaptive strategies to manage potential oil price shocks. Thus, by adopting a demand-side perspective, policymakers can better anticipate and respond to oil price fluctuations, promoting more resilient economic policies.

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

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

RV-FELM: Futures commodity price forecasting based on RIME-VMD algorithm coupled with FA-ELM

Commodity futures are an important hedging tool in material trade, and by accurately predicting prices, countries and firms are able to make informed production and consumption decisions. This paper introduces a novel machine learning ensemble method that combines decomposition algorithms and physical optimization algorithms to predict commodity futures prices. First, the VMD(Variational mode decomposition) is optimized by the RIME algorithm (Rime optimization algorithm) to obtain the optimal modal decomposition results, and the trend and seasonal terms are predicted using the ELM (Extreme Learning Machines) and FA (Fourier Attention) models, respectively, and the results are finally synthesized. The results show that the MAPE(mean absolute percentage error) of one-step, three-step, and six-step methods for predicting crude oil prices are 0.48%, 0.66%, and 0.75%, respectively, and the MAPE of soybean prediction results are 0.22%, 0.27%, and 0.37%, respectively. The empirical results and ablation experiments show that it outperforms other benchmark models in terms of both horizontal and directional accuracy. Notably, it outperforms in predicting soybean futures prices, which demonstrates the ability of our model to better capture the characteristics of both the time and frequency domains of the series, to take sufficient consideration of the series characteristics, and to ensure robustness.