Oil Price Movements Research Articles

Not all geopolitical shocks are alike: Identifying price dynamics in the crude oil market under tensions

Many studies have investigated the effects of geopolitical risks on oil price dynamics, but few distinguish their impacts by the event categories. In this paper, we employ the structural vector autoregression with time-varying parameters to identify oil price movements in the face of different types of geopolitical shocks. We not only observe differences between shocks from geopolitical threats and realities, but also find that various geopolitical tensions deliver disparate shocks to oil markets. Our results suggest that all kinds of geopolitical shocks, except terror threats, can drive oil prices, but the margins and significance vary over time. Oil supply responds negatively, though not significantly, to all geopolitical shocks, while aggregate demand has heterogeneous responses to war and terror shocks. In addition, war- and terror-related events create far more and longer-lasting uncertainty in oil prices than other geopolitical risks. We also observe heterogeneous impacts of different geopolitical risks on the real economy.

Time-varying causality between oil price and exchange rate in five ASEAN economies

The aim of this study is to investigate the effect of oil price changes on the exchange rates of the five ASEAN economies. In the study, a rolling and recursive evolving window algorithm is applied to detect changes in the link between oil price and exchange rate from January 1988 to June 2022 for five ASEAN countries. We extend the existing literature using the Time-varying Granger causality model, which captures sensitivities across various time horizons. The findings revealed heterogeneous effects of oil price on the exchange rate at different time horizons in terms of importance and magnitude over time. Our empirical results support the combined movements of oil prices and exchange rates against some important dates and events. The findings provide investors with insight into the utility of the shock transmission mechanism and how central banks design market intervention policies.

Dynamic Association among Economic Growth, Oil Prices, and Key Macroeconomic Factors: Evidence from the US and Korea

Purpose - This study aims to investigate how economic growth responds to oil price volatility and key macroeconomic factors, such as interest rate, inflation and unemployment rate in two big oil consumers and importers in the world, the US and Korea.
 Design/Methodology/Approach - This study used quarterly time-series data of each variable for the period 1990 to 2022, consisting of two sub-periods, to employ ARDL (autoregressive distributed lag) method and the Granger causality test in order to identify the role of the movement of oil prices and key macroeconomic factors in national economic growth in the US and Korea.
 Findings - The results show that economic growth is correlated with interest rate, CPI, and unemployment rate in the US and Korea, with no significant relationship between economic growth and crude oil price shocks for both the pre- and post-GFC periods in the two countries. For the long-run and short-run effects among variables based upon the ARDL model estimation, there exists a long-run impact of past CPI value on the Korea’s economic growth in the pre-GFC period and, in the post-GFC, various variables affect economic growth in the US and Korea. In the short run, there is no evidence of an oil price shock effect on national economic growth in the US and Korea.
 Research Implications - The findings confirmed that the impact of oil price shocks on the national economic performance has weakened. This study has significant implications for policymakers attempting to address the effects of oil price shocks in their economies. Policymakers need to focus on the relatively manageable economic variables for sustainable economic growth rather than oil price changes. It is also suggested that oil price as a production cost and policy-manageable macroeconomic variables that have been used as growth strategies have limitations in using them any longer because the expansion of potential growth through reform or innovation is believed to be the source of economic growth.

The influence of oil price uncertainty on stock liquidity

AbstractUsing high‐frequency intraday data, this study provides strong empirical evidence that elevated oil price uncertainty has a significant and negative influence on stock liquidity. More specifically, the results suggest that large oil‐related corporations are most affected, followed by small‐listed firms more generally. Further analysis reveals that liquidity providers widen the bid‐ask spreads to protect themselves during periods of high oil price uncertainty for large‐listed firms, particularly those in the oil industry. These findings are robust to various measures of oil price uncertainty, different market conditions, structural break analysis and show the influence of oil price movements extends to stock liquidity.

Dynamic relationship between equity, bond, commodity, forex and foreign institutional investments: Evidence from India

The interrelationship between equity, bond, commodity and forex movements can provide investors with abundant trading opportunities regardless of whether one market is trending upward or downward. Hence, to understand the interlinkage between markets, this study examines the long-run and causal linkage between forex, G-sec bonds, oil prices, gold rates, foreign institutional investment (FII) flows, and equity market and sectoral index returns. Daily time-series data from August 2012 to August 2021 were considered for empirical analysis. Johansen’s cointegration test revealed that foreign exchanges like USD, Euro, GBP and Yen, oil and gold rates, G-bond returns and FII flows were significantly cointegrated with the stock market and sectoral indices in the long run. Further, Granger causality found a uni-directional relationship between forex rates (i.e., USD, Euro, Yen) and the market, as well as sectoral indices, except Nifty 50 and Nifty IT indices. Oil price movements were found to effectively predict future price changes of Nifty consumer durables, auto, IT indices. Gold prices are useful to predict Nifty-Auto, Bank, Financial Services, Oil & Gas and PSU. The study also found a bi-directional relationship from FII inflows to the stock market and sectoral indices. The findings suggest that forex rates, oil prices and FII flows significantly affect India’s stock market and sectoral performance. The study contributes to the existing literature by comprehensively examining the interlinkage between commodities such as oil and gold, foreign exchanges like USD, Euro, GBP and Yen, G-bond, FII flows and the stock market, and fourteen sectoral indices in the Indian context.

Can cryptocurrencies hedge oil price fluctuations? A pandemic perspective

The article aims to verify whether cryptocurrencies can hedge extreme price movements in Brent crude oil. The COVID-19 pandemic revealed that oil prices are heavily influenced by economic uncertainty and the mobility factor. We analyse Brent crude oil prices from February 10, 2020, to February 10, 2022. We consider Bitcoin, BNB, Ether, Tether, and USD Coin, the top five cryptocurrencies by market capitalization, as possible hedges. We explore their potential to protect oil investments using two approaches. The first focuses on price movement, and the second one on minimizing portfolio volatility. We use three modelling techniques: asymmetric causality in prices, a threshold vector-autoregressive model for returns, and dynamic conditional correlation analysis. We show that while stablecoins provide the best protection against downward movements in oil prices, they do not reduce investment volatility.

A Mutual Information-Based Network Autoregressive Model for Crude Oil Price Forecasting Using Open-High-Low-Close Prices

The global financial markets are greatly affected by crude oil price movements, indicating the necessity of forecasting their fluctuation and volatility. Crude oil prices, however, are a complex and fundamental macroeconomic variable to estimate due to their nonlinearity, nonstationary, and volatility. The state-of-the-art research in this field demonstrates that conventional methods are incapable of addressing the nonlinear trend of price changes. Additionally, many parameters are involved in this problem, which adds to the complexity of such a prediction. To overcome these obstacles, a Mutual Information-Based Network Autoregressive (MINAR) model is developed to forecast the West Texas Intermediate (WTI) close crude oil price. To this end, open, high, low, and close (OHLC) prices of crude oil are collected from 1 January 2020 to 20 July 2022. Afterwards, the Mutual Information-based distance is utilized to establish the network of OHLC prices. The MINAR model provides a basis to consider the joint effects of the OHLC network interactions, the autoregressive impact, and the independent noise and establishes an intelligent tool to estimate the future fluctuations in a complex, multivariate, and noisy environment. To measure the accuracy and performance of the model, three validation measures, namely, RMSE, MAPE, and UMBRAE, are applied. The results demonstrate that the proposed MINAR model outperforms the benchmark ARIMA model.

Forecasting Price of the Crude oil using LSTM based on RNN

Abstract: Oil demand is inelastic; therefore the rise in price is good news for producers because they will see an increase in their revenue. Oil importers, however, will experience increased costs of purchasing oil. Because oil is the largest traded commodity, the effects are quite significant. A rising oil price can even shift economic/political power from oil importers to oil exporters. The crude oil price movements are subject to diverse influencing factors. Our work mainly focuses on applying Recurrent Neural Networks to predict the Crude Oil Price. This decision helps common people to buy crude oil at the proper time. Time series analysis is the best option for this kind of prediction because we are using the previous history of crude oil prices to predict future price of the crude oil. So we would be implementing RNN (Recurrent Neural Network) with LSTM (Long Short Term Memory) to achieve the task. We will be experimenting with different types of models with varying number of epochs, look backs and other tuning methods.

An Explainable Machine Learning Framework for Forecasting Crude Oil Price during the COVID-19 Pandemic

Financial institutions, investors, central banks and relevant corporations need an efficient and reliable forecasting approach for determining the future of crude oil price in an effort to reach optimal decisions under market volatility. This paper presents an innovative research framework for precisely predicting crude oil price movements and interpreting the predictions. First, it compares six advanced machine learning (ML) models, including two state-of-the-art methods: extreme gradient boosting (XGB) and the light gradient boosting machine (LGBM). Second, it selects novel data, including user search big data, digital currencies and data on the COVID-19 epidemic. The empirical results suggest that LGBM outperforms other alternative ML models. Finally, it proposes an interpretable framework for facilitating decision making to interpret the prediction results of complex ML models and for verifying the importance of various features affecting crude oil price. The results of this paper provide practical guidance for participants in the crude oil market.

The Impact of Crude Oil Prices on Macroeconomic Factors in Korea

Purpose - The purpose of this study is to examine how Korea’s macroeconomic factors, such as GDP, CPI, Export, Import, Unemployment rate and USD/KRW exchange rate, are affected by the oil price shocks. Design/methodology/approach - This study used monthly and quarterly time-series data of each variable for the period 1983 to 2022, consisting of two sub-periods, to employ Granger causality test and GARCH method in order to identify the role of the oil price movement in macroeconomic factors in Korea. Findings - Korea’s currency rate to the US dollar is negatively correlated with the price change of crude oil while the GDP change is positively correlated with the price change of crude oil with strong relationship between Export and Import in particular. The exchange rate and GDP growth are believed to be not correlated with the oil price change for the pre-GFC period. According to the Granger causality test, the price change in crude oil has a causal impact on CPI, Export and Import while other factors are relatively slightly affected. Transmission effect from the oil price to Export is found and there also exists volatility spillover from oil price to economic variables under examination. Comparing two sub-periods, CPI and Export volatility responds negatively to shocks in the oil price for the pre-GFC period while volatility of CPI and Unemployment reacts positively to the oil price shocks for the post-GFC period. Research implications or Originality - The findings of this study could be helpful for both domestic and international investors to build their portfolio for the risk management since rising WTI price can be interpreted as a result of global economic growth and ensuing increase in the worldwide demand of the crude oil. Consequently, the national output is expected to increase and the currency is also expected to be strong in the long run.

Exploring the dynamic nonlinear relationship between crude oil price and implied volatility indices: A new perspective from MMV-MFDFA

This paper investigates the cross-correlations between the crude oil market and three implied volatility indices for the crude oil market, the stock markets in emerging and developed economies. We employed a newly developed method—the multiscale multivariate multifractal detrended fluctuation analysis (MMV-MFDFA) within the framework of the fractal market hypothesis (FMH). The research target here is to detect the fractal autocorrelation characteristics for the time series covered in this study. The empirical results demonstrate that cross-correlated behaviors have multifractal features. The cross-correlated behaviors of smaller fluctuations are persistent while large fluctuations are anti-persistent between crude oil and OVX. The cross-correlated behaviors between crude oil and implied indices of the developed and emerging markets are anti-persistent both small and large fluctuations. Crude oil price fluctuations and the implied volatility indices change in a positive direction in the short term, but in the reverse direction in the long term. In addition, the multifractal strength between crude oil and implied volatility index for crude oil market is larger than that between crude oil and emerging and developed stock markets. Political, economic, and military shocks, with relatively large influence have a major impact on the internal volatility of the system. The findings of nonlinear and multifractal correlations between crude oil and implied volatility indices suggest that, due to existing of market inefficiency and multifractality,​ investors can, to some extent, predict the future trends of crude oil price movements based on changes in the volatility indices, thereby adjusting their investment strategies to avoid risks.

Heterogeneity dependence between oil prices and exchange rate: Evidence from a parametric test of Granger causality in quantiles

This paper re-examines the nexus between crude oil price and exchange rate by investigating their heterogeneity dependence structure within the framework of Granger causality in quantiles for a sample of developed and emerging economies (namely UK, Canada, Brazil, Russia, Mexico, Norway, India, Japan, South Africa, South Korea and European Union (EU)). The results indicate no distinct causality between the crude oil price changes and the real exchange rate returns for all countries besides Russia at the median of the conditional distribution. Besides, the crude oil price changes influence the exchange rate returns in all countries, except Norway and EU, particularly around the tails of the conditional distributions of exchange rate returns. This suggests that the oil price changes influence the real exchange rate returns when the real exchange rate returns are either in extreme appreciation or depreciation. Moreover, the crude oil price movement can be explained by the exchange rate returns for most oil importers only when the crude oil market is bearish or bullish. By contrast, the real exchange rate can permanently affect the crude oil price for most oil-importing countries irrespective of the crude oil market's state. Finally, our findings provide an essential reference for managing the extreme risk dependence between the exchange rate market and the crude oil market.

Oil price shocks and the term structure of the US yield curve: a time-frequency analysis of spillovers and risk transmission.

This paper investigates the influence of oil demand, oil supply, and risk-driven shocks on the yield curve in the US between 1995 and 2020. The US term-structure shape is modeled by three structural factors, the level, slope, and curvature. Their empirical analysis is performed according to the Diebold-Li modified variant of the widely used Nelson-Siegel model. The technique of wavelet analysis allows investigating the interrelation of shocks in oil prices and the US yield curve along time and frequency domains, simultaneously. We report on low, medium, and high coherence zones, relative to the oil price movements and the changes in the three yield-curve factors. The low coherence intervals indicate the potential for the three latent factors to be used for creating diversification strategies capable of hedging adverse dynamics in the oil market, potentially workable through global crises. We document the variability of dynamic patterns observable for the US sovereign yield factors on per-type-of-shock basis, evidencing the potential role of the US sovereign debt investments for designing cross-asset hedge strategies for commodity and fixed-income markets.

Oil price shocks and sectoral stocks in Nigeria: how relevant are asymmetry and structural breaks?

PurposeThis paper aims to model the relationship between oil price and stock returns for selected sectors in Nigeria using monthly data from January 2007 to December 2016.Design/methodology/approachThe authors use both the linear (symmetric) autoregressive distributed lag (ARDL) by Pesaran et al. (2001) and non-linear (asymmetric) ARDL by Shin et al. (2014), and they also account for structural breaks using the Bai and Perron (2003) test that allows for multiple structural changes in regression models.FindingsThe results indicate that the strength of this relationship varies across sectors, albeit asymmetric and breaks. The authors identify two structural breaks that occur in 2008 and 2010/2011, which coincidentally correspond to the global financial crisis and the Arab spring (Libyan shutdowns), respectively. Moreover, the authors observe strong support for asymmetry and structural breaks for some sectors in the reaction of sector returns to movement in oil prices. These findings are robust and insensitive when considering different oil proxies. While further extensions can be pursued, the consideration of asymmetric effects as well as structural breaks should not be jettisoned when modelling this nexus.Originality/valueThis study is one of the very few studies that have investigated the sectoral behaviour of stocks to oil price shocks, particularly in Nigeria. This paper contributes to the oil stock literature using the recent technique of asymmetry and also considering the role structural breaks play in the relationship between oil price and stock returns.

Evaluating Oil Price Movement and Revenue Generation in Nigeria during Covid-19 Pandemic: Experience from Pre and Post Era

The study focusses on evaluating oil price movement and revenue generation in Nigeria with emphasis on the era of pre and post covid-19 pandemic. Measures such as crude oil price, domestic production, crude oil export and revenue, and revenue generation were assessed before, during and after covid-19 pandemic. The evidence shows that oil price and revenue generation were negatively affected during covid-19 pandemic era due to restriction on the movement of people and economic activities. Hence, the study further revealed a drastic fall in crude oil price and export, as well as domestic production (mbd) especially during the first quarter of 2020. While crude oil price appreciated slightly from June, the export and domestic production plummeted with a corresponding decrease in revenue generation which dropped deeply from $47 billion USD in 2019 to $8 billion USD in 2020. Further evaluation revealed that after the introduction of some measures by the government authorities, the Nigerian economic start to recover, which results to a slight improvement in crude oil price in December 2020, but later fell to 40.28 mbd in march 2021. Thus, other indicators such as domestic production and crude oil export has no sign of recovery between this period except for revenue generation which increases by $4 billion USD. Moreso, to control the aftermath of pandemic, we suggest for cohesive policy measures such as educating people on the benefits of covid-19 vaccines and proper enforcement on the use of facemask. In turn, this will reduce the spread and promote economic activities in Nigeria. In addition, policies that could regulate oil price movement should be initiated. Consequently, it may likely encourage increase in revenue generation in Nigeria.

Biodiesel feedstock and crude oil price relationships – The effects of policy and shale oil expansion

AbstractWe disentangle the effects of biodiesel incentives and shale oil expansion on the long-run equilibrium price relationships among biodiesel feedstocks and crude oil in the United States (US) and European Union (EU). We find that the 2005 Energy Policy Act in the US substantially increased the responsiveness of soy oil, canola oil, and corn oil prices to crude oil price movements. However, in recent years, expansion in the global supply of crude oil from shale oil extraction has offset the effects of US biodiesel incentives and blending mandates. In the EU, the Indirect Land Use Change Directive of 2015 substantially reduced the responsiveness of biodiesel feedstock prices to crude oil price movements.

Oil-price Shocks and Economic Growth: A Review of Literature

This study reviews oil-price shocks and economic growth with the aim of providing an in-depth knowledge based on the existing literature. The study uses content analysis coupled with trends analysis from 1981-2020. Brent crude oil price which represents a uniform substitute for oil price at the international oil market is used. Six (6) theories that links oil price and economic growth are considered. Out of the theories, the study anchors its theoretical framework on Dutch Disease Theory because the neglect of other sectors especially the developing oil-exporting/producing countries due to the discovery of natural resources (crude oil) has affected their economic growth. Documented evidences show that shocks in oil price hinder economic growth. The trend analysis also indicates unexpected movements in oil price (increase/decrease) whenever there is a shock. Based on this, necessitates the need for most oil-producing developing countries to look beyond the oil sector to prevent their economies from being mono-culturally based by developing indigenous products and in this process reduce the importation of those goods that can be produced locally.

The dynamic moderating function of the exchange rate market on the oil-stock nexus

Oil price movements have an important impact on the stock market, and this impact could be dynamically moderated by the exchange rate, which could not be effectively depicted by VAR or GARCH methods widely used in previous studies. This paper adopts a more flexible nonlinear model to investigates this dynamic moderating effect of the exchange rate market on the oil-stock nexus for 45 major countries from November 30, 2005 to November 22, 2019. We also compare the differences in this moderating effect between oil-importing and oil-exporting countries and confirm the presence of a wealth transfer effect. Specifically, the empirical results show that (1) In the stage where domestic currency depreciation or appreciation degree is not serious (the growth rate is less than 0.1), an increase in crude oil prices has a positive impact on stock market returns, and this positive impact is weakened when the growth rate of the exchange rate return approaches zero. (2) As the local currency continues to appreciate (the growth rate is greater than about 0.22), the increase in crude oil prices may negatively influence stock market returns to an increasingly greater extent among crude oil importers. (3) The increase in crude oil prices may have a short-term positive impact on stock market returns in oil-exporting countries due to the wealth transfer effect when the domestic currency appreciates at a faster rate. Finally, we discuss the policy implications of our findings to help investors avoid risks due to fluctuations in international oil prices.

Does oil price uncertainty affect earnings management? Evidence from GCC markets

PurposeSince it is believed that economic growth in oil-rich countries is highly influenced by oil price movements, this study aims to explore the relationship between oil price volatility (uncertainty) and earnings-management decisions.Design/methodology/approachFinancial data from oil-exporting countries were used to explore the relationship between oil price volatility and earnings-management decisions. The study used univariate and multivariate analysis. The modified Jones model is the proxy accrual earnings management. Further, the standard deviation of daily oil price returns is used to proxy annualised oil price volatility.FindingsThe results show that there is an association between oil price volatility and accrual earnings management. Specifically, there is a positive and significant relationship between negative accruals and oil price volatility, indicating that firms are inclined to conduct income-decreasing earnings management in periods of high oil price volatility.Research limitations/implicationsThis study’s findings have important implications for regulators and investors because they indicate that the uncertainty of oil price volatility has an influence on earnings quality in oil-dependent economies. This is especially important considering the ongoing debate on transparency issues.Originality/valueTo the best of the authors’ knowledge, this study is the first to investigate the relationship between oil prices volatility and earning management behaviour for non-financial firms. Further, the study uses unique data of oil-dependent economies.

Prediction of crude oil prices in COVID-19 outbreak using real data

The world has been undergoing a global economic recession for almost two years because of the health crisis stemming from the outbreak and its effects have still continued so far. Especially, COVID-19 reduced consumer spending due to social isolation, lockdown and travel restrictions in 2020. As a result of this, with social and economic life coming to a standstill, oil prices plummeted. With the ongoing uncertainty concerning the COVID-19 pandemic, it has been of great importance for all economic agents to predict crude oil prices. The objective of this paper is to improve a model in order to make more accurate predictions for crude oil price movements. The performance of this model is assessed in terms of some significant criteria comparing our model with its counterparts as well as artificial neural networks (ANNs) and support vector machine (SVM) methods. As for these criteria, root mean square error (RMSE) and mean absolute error (MAE) results show that this model outperforms other models in forecasting crude oil prices. Further, the simulation results for 2021 show that the daily crude oil price forecasts are almost close to the real oil prices.Oil price forecasting has become more and more important for economic agents in COVID-19 period. A consistent model is required to cope with the movements in crude oil prices. A novel method combining fuzzy time series and the greatest integer function is developed. The results show that our model outperforms other counterparts or ANN and SVM methods. We capture non-linearity and volatility in crude oil prices.