Oil Price Changes Research Articles

Unveiling Extreme Dependencies between Oil Price Shocks and Inflation in Tunisia: Insights from a Copula Approach

We follow a non-linear dynamic correlation approach using a combination of a DCC-GARCH model and a copula model to capture the dependence between oil price changes and inflation in Tunisia. The case of Tunisia is particularly instructive since after having been an exporter and a major producer, it became a net oil importer in the 2000s. The study, based on monthly data spanning decades, selects a Gumbel copula and shows that beyond weak average dependencies, there is a strong correlation between extreme values, suggesting that inflation in Tunisia is more sensitive to extreme (positive) variations in oil prices than to average variations. The implications of these empirical results for economic policy are crucial for the Tunisian economy.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Impact of crude oil price innovations on global stock market volatility: Evidence across time and space

This study investigates the impact of crude oil price innovations on global stock market volatility through a ripple-spreading network model, incorporating four dimensions of crude oil price changes: realized volatility, implied volatility, variance risk premium, and realized skewness volatility. Additionally, we assess the effects of three types of crude oil shocks—oil-specific, aggregate demand, and oil supply shocks. The results indicate that while all four dimensions exhibit similar temporal diffusion patterns, their spatial impacts differ. Global stock markets demonstrate heightened sensitivity to implied volatility and variance risk premium, followed by realized volatility and, lastly, realized skewness volatility. Moreover, we find that realized volatility spreads through multiple transmission pathways, albeit at a slower pace compared to implied volatility and the variance risk premium. Among the crude oil shocks, oil-specific shock induces the most rapid volatility transmission across global markets, with aggregate demand shock following and oil supply shock exerting the smallest influence.

Climate policy uncertainty and economic growth, a moderating role of crude oil price changes: evidence from the Asian economies

Purpose This study aims to inspect the impact of US climate policy uncertainty (CPU) on the economic growth of Asian countries with the moderating role of crude oil price (COP) changes. Design/methodology/approach The Im-Pesaran Sin and Fisher-type tests are used for stationarity check, while Kao and Pedroni tests are used for cointegration analysis. The Hausman test is applied for model selection, where pooled mean group autoregressive distributed lag (PMG/ARDL) has been selected and applied. Besides, the fully modified ordinary least squares is also used for robustness analysis. Additionally, the literature review and descriptive statistics have been used. Findings The main findings disclosed that US CPU negatively impacted the economic growth of Asian economies with high significance in the long run whereas insignificant in the short run. The results further concluded that COP positively affected economic growth both in the short and long run. Furthermore, the results also revealed that COP significantly and positively moderates the relationship between CPU and COP in the long and short run. Originality/value The study is the first of its kind to examine the impact of the US CPU on the economic growth of Asian economies. Second, it further revealed the moderating role of COP between US CPU and economic growth. Third, a large panel of data from Asian countries has been considered. Fourth, the study adds to the current literature by using the PMG/ARDL model to determine the impact of US CPU on economic growth. Additionally, this study focuses on the US CPU because it is a developed country playing a significant role in energy and climate issues, and has been very uncertain.

Cross-Market Volatility Dynamics Among Crude Oil and South Asian Emerging Markets During the COVID-19 Pandemic.

The COVID-19 pandemic had profound impacts on the global economy, initially triggering a severe economic downturn due to widespread lockdowns, this outbreak generated various economic shocks, a major one being the oil price crash. A brief understanding of how this pandemic is affecting the financial markets, empirical analysis is done on three financial markets. Three stock exchange markets are selected from emerging markets in South Asia (Pakistan, China, and India). The world crude oil market is selected as a reference, to how it transmits volatility to South Asian markets during the COVID-19 period. We employ a bivariate BEKK-GARCH model for analyzing the volatility spillover from November 19, 2019, to March 31, 2021. All markets show negative coefficients with the oil market, indicating that the previous day’s shocks in oil prices reduce today's stock market volatility, with significant values for India and Pakistan. China has a negative coefficient while the rest of the two markets show a positive and significant coefficient means past volatilities in the stock market decrease today’s oil market volatility, likely indicating a reactive or adaptive market behavior to stock volatility. Generally, oil price changes have a significant impact on these markets, with both immediate effects (via ARCH terms) and persistent effects (via GARCH terms). The study's findings offer valuable insights for policymakers, as they help identify the direction of spillovers among interconnected economies. Additionally, portfolio investors can use this information to effectively allocate weights to different asset classes when making investment decisions.

Oil Price and Energy Intensity Dynamics in Nigeria: Does Technical Change Matter?

It is critical to understand the mechanism needed to control energy intensity especially in an oilexporting country like Nigeria, because of its consequential effect on carbon dioxide emissions and environmental pollution. Increases in energy prices can lead to promotion of better technology and consequently, a reduction in energy intensity through a reduction in energy demand (consumption). This paper explores the dynamics between energy price and energy intensity to reveal the role of technical change in the equation. The study utilizes an autoregressive distributed lag (ARDL) and Toda-Yamamoto approaches. The study sample covers the period 1980 through 2021. The key contribution of this study to the literature is rooted in an understanding of the dynamics of energy intensity and its interplay with technical change in a country study as a critical piece of information for policymakers. The results indicate a change in oil price significantly affects technical innovation. However, there is no link between technical innovation and energy intensity. The plausible justification for the results is the enormity of oil subsidy policy of the Nigerian government.

Should you buy gold stocks or paper gold?

We analyze which type of gold investment is better for investors by studying the determinants of returns on gold mining stocks (gold stocks) and gold exchange-traded commodities (gold ETCs) over the period from March 2012 to February 2024. We find that gold ETCs outperform gold stocks in both returns and risk, and respond significantly more positively to economic policy uncertainty (EPU), offering investors more favorable safe-haven properties. Gold stocks are also more sensitive to oil price increases than are gold ETCs, and their relative price performance is negatively related to both oil price changes and Gold VIX. We conclude that investing in gold stocks is less suitable for investors who buy gold as a safe-haven asset.

WITHDRAWN: An Integration of Oil Price Volatility, Green Energy Consumption, Import and Export and Economic Performance: Assessing the Mediating Role of Trade

The research investigates the link between the volatility of oil prices, the use of green energy, import and export commerce, and economic performance. The data set was collected from 1990 to 2020 from China. In this work, we use two statistical techniques to examine these relationships: limits methodology to co-integration and autoregressive distributed lag (ARDL). Our results confirm that green energy consumption and import and export have a short-term association, which suggests that when there is a change in one factor ultimately impacts another one. Moreover, there is a short-term association between oil price volatility and green energy consumption, implying that an expansion in oil price volatility could lead to a surge in green energy consumption. In addition, oil price volatility and green energy consumption have a short-term unidirectional association between green energy and oil price, implying that changes in green energy can affect oil prices, but changes in oil prices do not affect green energy consumption. These findings add to the existing research on the connections between these variables and provide insight into their dynamic relationships. Based on the findings, significant policy recommendations are withdrawn.

The Asymmetric Effects of Oil Prices on Stock Returns: Evidence from Hanoi Stock Exchange, Vietnam

This study is devoted to investigating the asymmetric effects of oil prices on stock returns for Hanoi Stock Exchange (HNX). The data used in this study are weekly series of HNX-Index, WTI crude oil prices and geopolitical risks (GPRs) Index covering the period from January 2, 2010 to December 31, 2023. This study employed a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach to estimate the short-term and long-term asymmetric effects of oil prices on the market returns. We found that in the short-term, oil prices have negative asymmetric effects on the market returns. Specifically, one percent increase in positive changes of oil prices immediately leads to 0.0085 percent decrease in the market returns. However, one percent increase in negative changes of oil prices is associated with 0.1487 percent decrease in the market returns. In the long-term, the estimated results confirm that both the negative and positive changes of oil prices have significantly negative effects on the market returns. Finally, the results obtained from the error correction model (ECM) indicate that 81.54 percent of the disequilibria from the previous week are converged and corrected back to the long-term equilibrium in the current week.

Forecasting and unveiling the impeded factors of total export of Bangladesh using nonlinear autoregressive distributed lag and machine learning algorithms

Rising global oil prices are a major challenge for an emerging oil-importing nation such as Bangladesh. The majority of prior research on the economic effects of an oil price shock has concentrated on developed countries, with emerging economies receiving comparatively less attention. Bangladesh is vulnerable to price shocks due to its rising oil consumption over the past decade. This study aims to investigate how changes in oil prices would affect Bangladesh's total export earnings and to forecast the overall export volume. This study utilized a nonlinear autoregressive distributed lag (NARDL) approach to account for the asymmetric behavior of oil prices from 1991 to 2021. To assess the accuracy of predictions, the study employed the Prophet forecasting model and the Long Short-Term Memory (LSTM) method. Additionally, the symmetry test revealed a nonlinear relationship between export volume and oil price but a linear relationship between inflation and export volume. According to the NARDL assessment, both positive and negative oil shocks increase export earnings over the long run. The short run summary clarifies that both positive and negative changes in oil prices exert a significant negative effect on exports. Also, Inflation influences export earnings negatively in the short run but positively over the long term. Moreover, using machine learning methods, it was found that the LSTM method outperforms the prophet model in prediction performance with a low root mean square error (RMSE) of 1.88. Also, the analysis revealed policymakers that the export sector requires diversification to reduce its exposure to oil price shocks.

Fluctuations oil prices in the world and their impact on Indonesian macroeconomics

Abstract Changes in international crude oil prices can significantly impact economic outcomes. This study tested the impact of oil price fluctuations on macroeconomic variables in Indonesia. The data used is annual data from 1990 to 2021 with the specific purpose of solving the current research deficiency about swings in oil prices. study adds five macroeconomic variables as explanatory factors to accomplish this purpose. We use five macroeconomic variables: economic growth, inflation, interest rates, industrial production, and exchange rates. The impact of oil price fluctuations on GDP, inflation, exchange rates, interest rates, and industrial production indices in Indonesia is examined within a structural vector autoregressive (SVAR) framework. Research shows that oil price shocks significantly impact GDP, inflation, and exchange rate. The results also show that oil price shocks hurt two other variables: the interest rate variable and the industrial production index. The findings derived from the analysis of the impulse response function indicate a detrimental effect on the growth of production, while exerting a favourable influence on inflation. However, the impact on industrial production, interest rates, and exchange rates is very modest and characterised by a degree of uncertainty.

THE EFFECTS OF PRICE AND VOLATILITY INDICES OF STRATEGIC COMMODITIES ON STOCK MARKETS: A STUDY ON BRICS ECONOMIES

The effect of gold, oil prices, which are strategic commodities, and gold, oil volatility indices on the BRICS stock exchanges is investigated in this paper. The relationship between the variables is analysed for the short and long-run using the NARDL method. Gold prices have a negative effect on stock markets except Brazil and Russia, in the short and long-run. Changes in oil prices have a negative effect on stock prices in South Africa, which imported oil, and a positive effect in net oil exporters Brazil and Russia, in the short and long-run. It is concluded that positive and negative shocks of strategic commodity volatilities have an asymmetric effect on the stock exchanges of emerging countries in the short and long-run.

Error Correction Model (ECM) For Macroeconomic Factors And LQ45 Stock Price Index

This research aims to test the causal effects of the inflation rate empirically, the Rupiah exchange rate against the United States Dollar, and world crude oil prices on the LQ45 stock price index. In its testing, this research uses the Error Correction Model (ECM) and uses data every month from 2000 to 2022. The research results show that the inflation rate, exchange rate and world gold prices significantly affect the LQ45 stock price index. Furthermore, the inflation rate and exchange rate have a negative influence on the LQ45 index. Not optimal company growth due to high inflation and exchange rates will affect public market share demand, thereby reducing share prices. Then, world crude oil prices positively influenced the LQ45 index. These results support previous research findings that changes in world crude oil prices positively affect stock market prices in developing countries.

ECONOMIC IMPACT ON PALM OIL STOCK RETURNS IN MALAYSIA, SINGAPORE, AND INDONESIA: A NARDL MODEL ANALYSIS

The study examines the relationship between Malaysia’s economic conditions and the returns on palm oil stocks in Malaysia, Indonesia, and Singapore. The non-linear autoregressive distributed lag framework was used to analyse the asymmetric impact of Malaysian macroeconomic factors on palm oil stock returns in each country. The findings reveal that the exchange, inflation, and unemployment rates, as well as gold and crude oil prices, strongly influence the long-term returns of palm oil stock. Specifically, unfavourable changes in crude oil prices significantly impact palm oil stock returns. Other key variables also show significant associations with palm oil stock returns, both positively and negatively. In conclusion, investors and stakeholders should take Malaysia’s general economic situation into account when developing investment plans, in addition to fluctuations in the returns on palm oil stocks.

PREDICTION OF CRUDE OIL PRICES IN INDONESIA USING FOURIER SERIES ESTIMATOR AND ARIMA METHOD

Crude oil is one of the non-renewable natural resources that is crucial for countries around the world in driving economic development. However, the availability of crude oil is decreasing over time. The high demand for crude oil results in scarcity which causes price fluctuations. Low oil prices can reduce state revenues, disrupt development programs, and even trigger budget deficits. On the other hand, an increase in crude oil prices can make a positive contribution to state revenues. Crude oil exports become more profitable, which can increase state revenue through royalties and taxes levied on the oil and gas sector. This additional revenue can be used to support infrastructure development, social programs, and investment in key sectors of the economy. High oil prices can also harm the economy. With the many impacts that can be caused by crude oil prices, the government must be able to anticipate and prepare for it. The data used in this study are data on crude oil prices in Indonesia for monthly periods from January 2018 to October 2023 sourced from the official website of the Ministry of Energy and Mineral Resources (ESDM) of the Republic of Indonesia. The researcher tried to compare two analysis methods, namely the Fourier series and the ARIMA estimator. The results of this study show that the best method in predicting crude oil prices in Indonesia is the Fourier series estimator with Cos-Sin function which produces RMSE and MAPE values of 7.93 and 8.4%. The prediction results can be used as a reference for the government to anticipate and make programs or policies that are more focused and targeted toward the impacts that can be caused by changes in crude oil prices.

Analyzing Overnight Momentum Transmission: The Impact of Oil Price Volatility on Global Financial Markets

Fluctuations in oil prices substantially impact both the real economy and international financial markets. Despite extensive studies on oil market dynamics and overnight momentum, a comprehensive understanding of the link between oil price changes and energy market momentum, as well as their broader influence on global financial markets, remains elusive. This study delves into the intricate mechanics of overnight momentum transmission within financial markets, focusing on its origin in oil price fluctuations and its overarching impact on market dynamics. Employing the quantile VAR method, we analyze daily market data from 3 January 2014 to 17 January 2024. This study emphasizes the significance of overnight momentum on the transmission of volatility, particularly in the tails of the distribution, and highlights the necessity for efficient strategies to govern financial stability. The shale oil revolution, COVID-19, the Russia–Ukraine war, and the Israel–Hamas conflict have significantly impacted the interconnectivity of financial markets on a global scale. It is crucial for policymakers to give priority to the monitoring of the energy market to reduce risks and improve the resilience of the system.

The dynamic effects of oil supply shock on China: Evidence from the TVP-Proxy-VAR approach

This study examines how changes in oil supply impact oil prices and China's macroeconomy. We developed an estimator that uses OPEC's institutional characteristics and high-frequency data to identify an oil supply surprise shock. This oil supply surprise is then incorporated as an exogenous variable into a TVP Proxy VAR model to analyze the effects of oil supply shocks on China's economy. Our findings indicate that negative oil supply shocks lead to higher oil prices, which in turn cause increases in China's interest rates and inflation, a decline in stock prices, a short-term rise in exports, and a long-term decrease in industrial output. When considering time-varying model parameters, we found that periods with larger oil supply shocks have a more pronounced impact on the macroeconomy. Our research provides empirical evidence for a better understanding of the effects of energy supply fluctuations.

Exploring the dynamic connections between oil price shocks and bond yields in developed nations: A TVP-SVAR-SV approach

This paper investigates the impact of oil price changes on bond yields across nine developed nations, focusing on how demand, supply, and oil-specific demand shocks influence bond spreads. Using a Time-Varying Parameter Structural Vector Autoregression with Stochastic Volatility (TVP-SVAR-SV) model, we find that bond yields are short-term responsive to oil price fluctuations, indicating efficient domestic bond markets. However, we observe distinct country-specific reactions, particularly in the US and the UK. In the US, supply shocks cause temporary declines in bond yields, while demand shocks reduce bond yields in both countries. Oil-specific demand shocks have cyclical impacts. Unlike the global financial crisis, the COVID-19 pandemic uniquely influenced bond yields, with minimal short-term effects in Canada, France, Spain, and the US post-COVID-19, contrasting with the severe impacts during the financial crisis. These findings suggest investors should consider the varying impact of oil shocks on bond yields across countries, and policymakers should monitor and address the potential implications of oil price fluctuations for financial stability and market efficiency.

Dynamic linkages and contagion effects: Analyzing the linkages between crude oil prices, US market sector indices and energy markets

This paper aims to assess the dynamic linkages between crude oil futures contracts, renewable energy indices, carbon credit futures indices and several US sector market indices by applying the DECO-GARCH model and the connectedness index of Diebold and Yilmaz (2012). The analysis is conducted on a daily data sample from August 2014 to February 2024 and performed at both the aggregated and disaggregated levels. The disaggregated analysis revealed that the correlation between the variables was lower before periods of stress, such as the COVID-19 crisis and the Russian–Ukrainian conflict as well as a complex correlation structure with a diverse mix of positive and negative values between different pairs of variables. Moreover, the static connectedness results in terms of returns underscore a significant degree of interconnection and transmission of shocks between oil prices and sector markets, renewable energy, and carbon credit futures indices. In addition, the results highlight the responsiveness of the clean energy and carbon credit sectors to global circumstances and economic conditions. The study concludes with a dynamic connectedness analysis, highlighting once again the intricate connections and interactions between all the variables, which are exacerbated during periods of market instability and key events. The net interconnectedness analysis demonstrated that changes in crude oil prices have a significant impact on most of the variables analyzed. The findings of this study have clear implications for a wide range of market participants, policy-makers, and individuals managing portfolios, particularly in terms of diversification opportunities.

Navigating the Dynamics of Brent Crude Oil Prices: Factors, Trends, and Insights

Accurate Brent Crude Oil price prediction is essential for informed decision-making and risk control in the global energy market. This paper examines the dynamics of Brent Crude Oil prices over the previous 35 years, utilizing predictive modelling techniques to project future prices and analyzing historical trends and relationships with the GDPs of major economies. The statistical methods used in this paper include the moving average and Pearson correlation coefficient. While correlation studies show significant positive links between Brent Crude Oil prices and the GDPs of major economies like China, Saudi Arabia, Europe, Russia, and the United States, historical data analysis reveals large price volatility. Highly accurate price forecasting using LSTM (Long Short-Term Memory) modelling approaches provides detailed risk management and decision-making information in the global energy market. The application of the Neural Network and the ARIMA model shows an increase in the price of Brent crude oil in the next year, 2024. Identifying the importance of the Brent crude oil price forecast and its effect on the international market is highly needed. This paper thus might help to increase economic growth. These results highlight the importance of advanced modelling techniques and economic indicators in managing oil price changes and help make informed decisions in the energy industry.

How oil prices impact the Taiwanese economy: Evidence from the stock market

Oil prices are volatile. How do oil prices affect Taiwanese industries? This paper investigates how oil price increases driven by shocks to global aggregate demand and to oil supply affect Taiwanese sectoral stock returns. It uses Hamilton's (2014) approach to divide oil price changes into portions driven by demand and supply factors. The results indicate that the semiconductor sector and Taiwan Semiconductor Manufacturing Company (TSMC) are harmed by oil price increases. Since oil prices are often high and quick to change, these findings imply that TSMC should expedite its goal of switching from depending on fossil fuels to utilizing renewable energy.