Factor affecting the Sino-Russian cooperation in energy trade

Objective – The aim of this paper is to examine the relationship between the CPI, the brent crude oil price, and the PPI for final manufactured goods as well as the Rand/Dollar exchange rate during the past year. The study used South Africa as a proxy for developed countries. The objective is, therefore, to evaluate the effects of the increase in commodity prices on inflation and other macroeconomic variables. Methodology –The study utilised a quantitative methodological approach through the assessment of an econometric model that employed monthly data from January 2017 to May 2022. The paper utilized variables such as CPI, brent crude oil prices, PPI for final manufactured goods as well as the Rand/Dollar exchange rate. Findings – Short- and long-run relationships were established between the variables using the vector error correction model (VECM) and the Johansen co-integration equation methods. The long-run conclusions showed that high brent crude oil prices, high sunflower oil, a depreciating exchange rate and increasingly high PPI levels will lead to an increase in the CPI (Inflation). The results also indicated that oil prices still influence the basic prices of goods and services since all things need to be transported. Novelty – The results of the study showed that a perpetual international and national macro-economic environment is crucial to prevent inflationary pressures and price shocks, while volatile exchange rates unsteady PPI’s and significantly high oil and commodity prices causes cost-push inflation. Policy certainty and political stability is important to keep inflation stable and economic growth positive, which could lead to a more self-sufficient economy which are is reliant on political instability as an obstacle for positive future economic growth. Type of Paper: Empirical JEL Classification: E31, E37, E60, E63 Keywords: Russian-Ukrainian conflict, economic growth, Brent crude oil prices, PPI, CPI, exchange rates, sunflower oil. Reference to this paper should be made as follows: Neethling, J.R; Wyk, A.S.V. (2022). Analysing the rising oil price shock driven by Russia-Ukrainian tensions - effect on inflationary pressure in South Africa, J. Bus. Econ. Review, 7(3), 178–193. https://doi.org/10.35609/jber.2022.7.3(3)

It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencing international crude oil prices and to establish crude oil price models to forecast long-term international crude oil prices. Six explanatory influential variables, namely Dow Jones Indexes, the Organization for Economic Cooperation and Development oil stocks, US rotary rig count, US dollar index, total open interest, which is the total number of outstanding contracts that are held by market participants at the end of each day, and geopolitical instability are specified, and the samples, from January 1990 to August 2017, are divided into six sub-periods. Moreover, the co-integration relationship among variables shows that the contribution ratios of all the variables influencing Brent crude oil prices are in accordance with the corresponding qualitative analysis. Furthermore, from September 2017 to December 2022 outside of the sample, the Vector Autoregressive forecasts show that annually averaged Brent crude oil prices for 2017–2022 would be $53.0, $61.3, $74.4, $90.0, $105.5, and $120.7 per barrel, respectively. The Vector Error Correction forecasts show that annual average Brent crude oil prices for 2017–2022 would be $53.0, $56.5, $58.5, $60.7, $63.0 and $65.4 per barrel, respectively.

The increase in agricultural commodity prices in the recent past has renewed interest in ascertaining the factors that drive agricultural commodity prices. Though a number of factors are possible, higher oil prices are thought to be the major factor driving up agricultural commodity prices, especially as the demand for biofuels production increases. However, empirical evidence of this relationship remain ambiguous and largely depends on the method used. For this reason, there is a need to examine the relationship in the context of different methodologies. Furthermore, information on how South African commodity prices respond to world oil price shocks is less certain. A good understanding of the factors that drive local commodity prices will assist in making sound agricultural policies. In this paper, the Granger causality test is applied to the mean to investigate the causality between oil prices and agricultural (soya beans, wheat, sunflower and corn) commodity prices in South Africa. Daily data spanning from 19 April 2005 to 31 July 2014 is used for Brent crude oil, corn, wheat, sunflower and soya beans prices. Agricultural commodity prices were obtained from the Johannesburg Stock Exchange, and the series of Brent crude oil prices from the U.S. Department of Energy. Results from the linear causality test indicate that oil prices do not influence agricultural commodity prices. However, owing to structural breaks and nonlinear dependence between the variables of study, these results are misleading. As an alternative, the nonparametric test of Granger causality in quantiles, as proposed by Jeong, Härdle and Song (2012) is used. Through this test, we not only look at causality beyond the mean estimates but also accounts for the structural breaks and nonlinear dependence present in the data. Additionally, the method becomes more instructive in the case where the distribution of variables has fat tails. The findings show that the effect of changes in oil prices on agricultural commodity prices vary across the different quantiles of the conditional distribution. The highest impact is not at the median, and the impact on the tails is lower compared to the rest of the distribution. The analysis shows that the relationship between oil prices and agricultural commodity prices depends on specific phases of the market, and therefore contradicts the neutrality hypothesis that oil prices do not cause agricultural commodity prices in South Africa. This implies that policies to stabilize domestic agricultural commodity prices must consider developments in the world oil markets.

Petroleum and natural gas, which are among the most used energy sources in the world, have a significant impact on financial markets and macroeconomic indicators as they are used as raw materials in many fields. For this reason, Russia, Turkey, Brazil, and India, as energy importers and developing countries, may be affected positively or negatively by changes in energy prices. The main purpose of this study is to examine the correlation between Brent oil, crude oil (WTI), and natural gas (NG) prices and Moscow Stock Exchange Index (RTSI), Borsa Istanbul Index (XU100), Bovespa Brazilian Stock Exchange Index (BVSP), and Indian National Stock Exchange Nifty 50 Index (NSEI). In the study, weekly data between 16.02.2020 and 26.12.2021 were examined. Vector autoregressive (VAR) model was used to examine the correlation between the variables included in the analysis, and the direction of the correlation between the variables was determined by the Granger causality test. According to the results of the VAR model, Brent oil and crude oil prices have significant effects on the indices included in the analysis; however, natural gas price does not have a significant effect on indices, Brent oil, and crude oil prices. On the other hand, the results of the Granger causality test confirm the findings of the VAR analysis. Granger causality test results reveal that in Granger's sense, only BVSP and NSEI are the cause of Brent oil price, RTSI, BVSP, NSEI, and XU100 are the cause of WTI, and WTI is the cause of NSEI.

This study provides a brief analysis of time-varying cointegration between the INR–USD bilateral exchange rate and Brent crude oil prices in the post–subprime crisis period. Prior studies established this relationship using the assumption that the long-run relation is intertemporally constant. However, there is much recent evidence demonstrating that this assumption may not be feasible. To address this issue and to go beyond the restrictive time-invariant environment, we employed the time-varying cointegration framework of Bierens and Martins (2010) , which was assessed through orthogonal Chebyshev time polynomials. The result shows that the Rupee was decoupled from oil price shocks in the first two samples. However, the oil price pass-through effect will become stronger in the third and fourth samples. The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in oil prices and the exchange rate over the period under study. This indicates the ability of international crude oil prices to influence domestic economic activities through the exchange rate. Policymakers should consider this factor while making monetary and foreign exchange policies. JEL Codes: E44, G14, G15

In 2020, the outbreak of COVID-19 caused a sharp drop in international crude oil prices. The outbreak of the Russian Ukrainian conflict in 2022 has also had a great impact on the fluctuation of international crude oil prices. So far as an important source of energy, the fluctuation of crude oil price is associated with the entire financial market. Therefore, this paper try to find how the air transport industry will going through the dynamic changes of its main source of energy——crude oil. Then the data of Brent oil price and air transportation industry index are selected from six months earlier before the Russian Ukrainian conflict broke out, used for constructing VAR model and ARMA-GARCH model with their logarithmic sequences. Through testing and observing the constructed Var model and ARMA-GARCH model, find the impact of international crude oil price changes on the yield and volatility of air transportation industry, so that the air transport industry can acquire the initiative of energy source.

This chapter proposes that energy trade is the backbone of sustainable energy security. It argues that energy trade ensures availability of energy, lowers cost, has environmental advantages, supports economic activity, promotes energy equity and is associated with higher efficiency. International trade of oil, natural gas and coal has grown over the last few decades and is forecasted to grow further. The importance of maritime energy trade in the overall energy trade is discussed. The role of energy markets in energy trading is evaluated, and the nature of energy trading on energy commodities exchanges and power exchanges is explained. The mechanisms by which energy trade is undertaken such as long-term contracts and spot buys are presented. Fixing of energy prices, mechanisms for hedging the price of energy such as forwards and futures contract, swaps and options are briefly introduced. Other enablers of energy trade such as political and economic stability and international cooperation which promote SES are also discussed.

Growth in the intensity of the inverse relationship between the US dollar exchange rate and the Brent crude oil price has been observed over the last decade. This may be linked, among other things, to the growing role of commodities as an alternative investment instrument at times of excess liquidity and low interest rates on global markets. This analysis examines monthly data from January 1982 to September 2010. Since 2002 the direction of the relationship in the Granger causality sense has been from the dollar exchange rate to the oil price. A weakening of the dollar of 1% causes the Brent oil price to rise by 2.1%. The contrary movements in the Brent oil price and the dollar exchange rate are a factor dampening the impact of sharp fl uctuations in the dollar price of oil on “non-dollar” economies, including the Czech Republic. This dampening effect was clearly visible in the period of sharp oil price growth in 2007 and 2008.

The present study examines the effects of the steep surge in crude oil prices which has also been considered as an oil price shock on the stock price returns and currency exchange rates of G7 countries, namely Canada, France, Germany, Italy, Japan, the United Kingdom (UK) and the United States (US), in the context of the Russia–Ukraine conflict. Due to the outbreak of the war, the steep surge in Brent crude oil price returns is seen as an exogenous shock to stock price returns and exchange rates during the period from 2 January 2017 to 29 June 2022. The paper applies the Fractionally Integrated GARCH (FIGARCH) model to capture the effect of the crude oil price shock and the Breakpoint unit root test to examine the structural breaks in the dataset. Structural breakpoints in the dataset for the entire stock price returns and exchange rates are observed during the period commencing from the last week of February, 2022, to the last week of March, 2022. Except for TSX, NASDAQ and USD, noteworthy long memory effects running from Brent crude oil price to all the stock price returns along with the currency exchange rates for all G7 countries were also found.

The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China

Exploring the relationship between international oil prices, income, and carbon dioxide (CO2) emissions in Saudi Arabia, this study examines if renewable energy consumption plays a lowering tool in international oil prices' impact on CO2 emissions, employing conventional econometric methods and the functional coefficient approach. The study reveals that the interaction between renewable energy consumption and international oil prices has a negative and statistically significant impact on CO2 emissions. This emphasizes the potential for Saudi Arabia to reduce carbon emissions by prioritizing renewable energy projects. In addition, a positive and statistically significant relationship between income and CO2 emissions is found, emphasizing the need to decouple economic growth from emissions growth. Furthermore, an interesting decoupling effect between oil price elasticity of CO2 emissions and per capita GDP is noted from the early 2000s–2015. This indicates that economic growth driven by rising oil prices can be managed to mitigate environmental impact, showcasing Saudi Arabia's commitment to sustainable development. Policy recommendations involve intensifying efforts to promote renewable energy implementation, lowering fossil fuel dependence in power generation, and incentivizing emissions reduction for a more sustainable energy future.

This paper examined the relationship between oil prices (international oil price and domestic oil price) and stock market price in Nigeria for the period spanning 1985:1 to 2010:4. The study utilised the Johansen's multivariate cointegration test and the vector error correction model (VECM). The Johansen's test showed that the variables are cointegrated, and the cointegration equation revealed that oil prices have significant relationship with stock market price in the long run. The VECM estimate only revealed a unidirectional causality from stock market price to international oil price in the long run. A unidirectional causality was also observed from domestic oil price to stock market price in the long run. The study recommended that policymakers, financial analyst and shareholders should into cognizance changes in international oil price and domestic oil price in their financial decisions given the significant impact of oil prices on stock market price in Nigeria.

Recent oil price changes have significantly influenced the macroeconomic activities of both oil importing and exporting economies. It has shifted the external balances of many economies. This study investigates the short and long run effects of oil price changes on macroeconomic activities of small open economy of Oman. The short-run changes in oil prices affect the economy via exchange rate and terms of trade channels, which in turn, affect the aggregate demand and supply aspects. While in long run, if the shocks remain persistent, the economy experiences the significant revenue windfalls and positive external balances. However, the degree of external shocks to domestic economy depends on the domestic response to external shocks. Vector Auto Regression (VAR) model is used to trace out those dynamics. VAR model is considered suitable methodological option in identifying the structural shocks through generalized innovations of impulse response functions and variance decompositions. The vector of six key macroeconomic variables is expressed in VAR framework. The framework establishes the joint behaviour of key variables through time and determines the structural dynamics of Oman economy. The variance decompositions are generated from a moving average representation of the VAR system and show the forecast error variance for each variable in the system attributable to both of its own innovations and those comes from other variables. Results suggest that changes in oil prices significantly affect the real exchange rate, output and external balances. Impulse response functions and variance decompositions results confirm positive effect of oil price changes on real effective exchange rates, real - output and consumer prices. To contain the inflationary expectations both in short and long run, the governments are usually resorting towards expansionary policies. The long run changes in oil prices is the key determining factor of output growth and subsequent changes in fiscal and monetary policy adaptations. The results further indicate that innovations in real output largely influenced by changes in prices. Innovations in money supply induce by changes in real output. Real output innovations positively affect by crude oil prices. Money supply is positively influenced by real output. In short run, the expansionary policies are consistent and have served well in containing the inflationary expectations and by maintaining the positive external balances. However, in long run, over reliance on stabilization policies in pegged exchange rate economies like of Oman economy may provide fewer options to contain the external shocks.

With the price of international crude oil rises highly, causing the of commodities continued to rise, as a result, we want to make a relationship analyze between international crude oil price and Mutual Fund of “Gold, Silver, Blue, Green”. To determine the oil price and the four theme mutual funds have any relationship. “Gold”on behalf of gold( mining ), “Silver”on behalf of trends of demographic ( elderly healthcare and related industries),“Blue”on behalf of water( ocean ),“Green”on behalf of alternative energy( renewable energy ).the research period from December 2000 to March 2008 includes monthly data, the four types of mutual fund Retrieve the file 17 funds, each of the 88 funds table information, so the total are 1496 samples; oil of the energy used by the Ministry of Economic Affairs put the Dubai crude oil do correlation analysis. The first step we use unit test, to test whether all the information showed stable; then we select the vector autoregression (VAR), to understand whether the variables existence any relationship, and use variance decomposition to understand the variables in the ability of explain. Later, through the vector autoregression that we realize Gold are not susceptible to other types of funds, but it will affect the Green; Silver will get impact of the Green, and it will affect blue, green and the oil price ; Blue will get impact of silver and oil prices, and the blue will impact green, Green will get all of variables impact, and it will affect silver and oil price, prices will get impact of silver and green, the oil will affect the blue and green funds. Total integration of gold, silver, blue, green and oil between the long-run will be equilibrium, and short-run will not balance. By the variance decomposition knows that Gold exgeneity is 77%, Silver exgeneity is 71%, and Blue exgeneity is 51%, Green exgeneity is 47% , Oil exgeneity is 8%.

This study investigated the prediction of crude oil price based on energy product prices using genetically optimized Neural Network (GANN). It was found from experimental evidence that the international crude oil price can be predicted based on energy product prices. The comparison of the prediction performance accuracy of the propose GANN with Support Vector Machine (SVM), Vector Autoregression (VAR), and Feed Forward NN (FFNN) suggested that the propose GANN was more accurate than the SVM, VAR, and FFNN in the prediction accuracy and time computational complexity. The propose GANN was able to improve the performance accuracy of the comparison algorithms. Our approach can easily be modified for the prediction of similar commodities.