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This paper studies the volatility dynamics of futures contracts on crude oil, natural gas, and gasoline. An appropriate Bayesian model comparison exercise between seven stochastic volatility (SV) models is estimated using daily prices for our futures contracts between 2005 and 2023. Moreover, to assess the impacts of COVID‐19 and the Russia–Ukraine conflict on volatility, we analyze these two subsamples. Overall, we find that: (i) the Bayes factor shows that the SV model with t $t$‐distributed innovations outperforms the competing models; (ii) crude oil contracts with different expiry dates may require the introduction of leverage effects; (iii) the t $t$‐distributed innovations remain the appropriate model for the COVID‐19 subsample, while jumps are needed in the conflict period; and (iv) other Bayesian criteria more appropriate to short‐term predictive ability—such as the conditional and the observed‐date deviance information criterion—suggest other rank order to model our futures contracts, despite the agreements for the best models. [ABSTRACT FROM AUTHOR]

As the second-largest oil producer and natural gas exporter, Russia's war with Ukraine has severely impacted the energy market. To what extent has the war influenced crude oil prices, and has it altered the long-term dynamics of oil prices? An objective analysis of the effects of the Russia–Ukraine war on the crude oil market can assist relevant entities in developing both short-term emergency strategies and long-term response plans. This study establishes an analytical framework of the event analysis method based on multiresolution causality testing (EMC). The results of the multiresolution causality testing reveal a significant one-way causality between the Russia–Ukraine war and crude oil prices. Afterward, using the event analysis based on variational mode decomposition (VMD), from October 1, 2021, to August 25, 2022, as the event window, we found that the war and its chain events caused the West Texas Intermediate (WTI) crude oil prices to increase by $37.14, a 52.33% surge, and the Brent crude oil price to rise by $41.49, a 56.33% increase. During the event window, the Russia–Ukraine war can account for 70.72% and 73.62% of the fluctuation in WTI and Brent crude oil prices, respectively. Furthermore, the war amplified oil price volatility and fundamentally altered the trend of crude oil prices. Consequently, this study proposes four recommendations: the establishment of an emergency management mechanism for the oil market, the diversification of oil and gas imports by energy-importing countries, the steady advancement of energy transformation, and the judicious use of financial instruments by enterprises to hedge risks. [ABSTRACT FROM AUTHOR]

Due to the war between Russia and Ukraine, oil and gas prices and food prices have risen worldwide. This disruption has affected the distribution and production of trade commodities for which Ukraine and Russia are key players. Among the reasons for the price increase were oil and food items, such as wheat, oilseeds, and gas. Due to this, people who are low-income survivors have a hard time surviving. In response, policymakers provided relief for these people with subsidies or lowered taxes. However, these short-run subsidies are inadequate and ineffective remedies that will lead to a shortage of supplies. Through targeted measures, such as cash transfers, policymakers can reduce the impact of these higher prices on lower-income households. Historically, commodity price shocks have induced policy and market responses that led to a larger supply of resources and, in the case of oil price shocks, greater efficiency in consumption and substitution away from oil. Likely, the recent price spike will once again spur more efficient energy consumption and a faster transition away from fossil fuels, especially if policy responses are supportive. We collected data from different sources, including the World Bank, the IMF, and the International Energy Agency, and reports from British Petroleum and the European Commission. The trends show that besides the rise in energy prices, prices of food items are also increasing, and this trend is making people's lives very hard. These price hikes will have the most significant impact on low-income countries, and this will also lead to food shortages worldwide. Changes in relative prices will also affect global food production. As a result of the war, however, there are many uncertainties related to food supply availability, and low-income countries may have a prolonged need for international assistance due to the war. [ABSTRACT FROM AUTHOR]

This study is concerned with forecasts of GDP growth for the Visegrad countries (V4-hereafter) and oil prices simultaneously. The series for GDP and Oil prices are quarterly, covering the period from December 1, 2000, to October 1, 2023. Neural network techniques were performed to generate individual forecasts. The forecasts for oil prices maintain higher accuracy than the GDP ones due to autoregressive lags. In other words, current oil prices absorb significant influence from past prices. The GDP forecasts for the V4 group indicate tremendous shock and moving growth to a lower steady state. The lower growth observed by the end of the period under review can be attributed to two subsequent non-economic shocks: the Russia-Ukraine war and the COVID-19 pandemic. The V4 countries faced difficulties after the outbreak of COVID-19, mainly due to the stringency measures. In addition to accelerating inflation, the war also disrupted energy commodities within supply chains. These and other facts justify the low growth that awaits these countries until 2028. Ultimately, the oil price until 2028 is estimated to oscillate between $60 and $90 per barrel. [ABSTRACT FROM AUTHOR]

This study aims to explain how the response of the Indonesian government by increasing the price of fuel oil (BBM) because of the Russian invasion to Ukraine. By using the theory of the decision-making process, this article discusses how the Indonesian government formulates and implements policies regarding the increase in fuel prices as a result of the Russian invasion of Ukraine which resulted in a supply shock and an increase in world crude oil prices. This article may be the first article that discusses in detail the Indonesian government's response to the increase in world crude oil prices, along with a discussion of the response of the affected Indonesian people. This article uses a qualitative research method, with data collection techniques using literature, and data processing from Twitter is done with NVivo 12 Plus which produces Word Cloud data. The Indonesian government's response to the raising of subsidized fuel prices was caused by several rational considerations that had two sides, namely advantages and disadvantages. The advantage is that by increasing fuel prices, the government can reduce the burden that must be borne by the state budget. While the losses incurred are the burden on the lower middle-class people in meeting their needs for fuel, both for mobility and other needs. The Indonesian government adopts policies that maintain the stability of the country's economy for providing social assistance to the people so that people's fuel needs are still met. [ABSTRACT FROM AUTHOR]

The energy markets, particularly oil and gas, have been significantly affected by the outbreak of the COVID-19 pandemic in terms of price and availability. In addition to the pandemic, the Russia-Ukraine war has contributed to concerns about the reduction in the oil supply. AI techniques are widely employed for prediction oil prices as an alternative to traditional techniques. In this paper, an AI-based optimization model called adaptive fox-inspired optimization (AFOX) model is presented, then recurrent neural network (RNN) is combined with AFOX to form a hybrid model called recurrent neural network with adaptive fox-inspired (RNN-AFOX) model. The proposed model is used to predict Crude Oil Prices. In the proposed model, AFOX is used to find the best hyper-parameters of the RNN and employed these hyper-parameters to build best RNN structure and use it to forecast the closing price of the oil market. The results show that the RNN-AFOX model achieved a high accuracy prediction with very small error and the coefficient of determination (R-squared) equal to 0.99 outperforming the RNN model in terms of accuracy prediction by about 24%, the FOX model by about 20% and the AFOX model by about 14%. Moreover, RNN-AFOX was evaluated under the impact of the COVID-19 pandemic and the Russia-Ukraine war. The results show the efficiency of RNN-AFOX in forecasting the closing prices of oil with high accuracy. In general, the proposed RNN-AFOX model overcomes other studied models in terms of Mean Absolute Percentage Error, Mean Absolute Error, Mean Square Error, Root Mean Square Error, coefficient of determination (R-squared) and consumption time. [ABSTRACT FROM AUTHOR]

We investigate the effect of gas/oil markets (spot/futures) on herding in stock markets in BRICS over 15 years (2007–2022). We consider the effect(s) of crises (Global financial, European debt, COVID-19, and Russia–Ukraine war), bull/bearish energy markets, volatility, and speculation. The effect of gas and oil markets on herding in stock markets is minimal, and investors herd selectively during crises. Even during the ongoing Russia–Ukraine war, the effect of energy markets on herding in BRICS is minimal. Causality tests show that oil (spot/futures) Granger causes CSAD during COVID-19 only. Gas (spot/futures) has no effect. We also find that energy (spot/futures) market states (bearish/bullish) have no effect on herding in stock markets. Low volatility in energy markets can trigger herding (consistent with previous research in US, China) in all BRICS. Speculative activities during (non)crises appear to have minimal impact on herding. However, as the degree of intensity (volatility) in speculative activities increases in oil/gas, it causes herding in all countries (India is affected mostly), except Brazil. It is not the speculation activity per se in (non)crises that causes herding, but the intensity/volatility in speculation activity. Overall, oil/gas markets (especially gas markets) appear to have a smaller impact on herding than expected, contrary to public belief; however, as the intensity/volatility in speculative activities increases, then herding also increases, which is expected given the uncertainty that speculation causes. [ABSTRACT FROM AUTHOR]

The aim of the article is to analyze the oil and petroleum products markets in Ukraine and tendencies in meeting the country’s needs for motor fuel. To achieve this aim, an analysis of the state of the oil and gas condensate market in Ukraine was carried out; the dynamics of oil and gas condensate consumption in Ukraine is studied; the main consumers and producers of motor fuel in Ukraine are identified; the dynamics of oil and gas condensate refining volumes is analyzed. The article also examines the dynamics of exports and imports of oil and gas condensate in Ukraine. The demand for crude oil and gas condensate in Ukraine is analyzed and it is determined that the dependence of domestic producers of light oil products on imported oil supplies significantly affects the oil market of Ukraine. The tendencies of oil and gas condensate production in Ukraine have been studied and their steady decline in recent years has been noted. The results of the study of the current state and trends in the development of the oil products market in Ukraine are presented. The dynamics of consumption and production, as well as the features of demand for motor fuel both in general and for its types in particular – motor gasoline, diesel fuel and liquefied natural gas (LNG) – are analyzed. The geographical structure of motor fuel imports by its individual types is analyzed. Their main importers in the Ukraine have been identified. The balance of the market for the main types of motor fuel has been studied. [ABSTRACT FROM AUTHOR]

As rapid inflation, continuing supply chain disruptions and the war in Ukraine impact petroleum prices worldwide, fuel supply disruptions have become an increasing concern. This paper describes Washington State's geographical, political and organisational context as it influences fuel disruption planning, as well as the history and philosophy of Washington's fuel-planning programme. Finally, the paper discusses planning best practices and gives some examples of their real-world use. [ABSTRACT FROM AUTHOR]

The Russian military operation in Ukraine triggered international turmoil, and had major repercussions on the entire world, including oil and gas prices, goods and services, migration policies, political alliances, and regional and international relations. It is pushing towards a change in the international system, but the features of the new system are not yet clear. This paper discusses the role of the Arab Gulf states in the stability of global energy markets, and sheds light on the Gulf-Chinese relations, and the role of OPEC+. The research identifies the main opportunities and challenges for policy makers and companies in the Arab Gulf states. It also shows the new political challenges facing these countries in terms of the future of their relations with the United States of America, some Western European countries, and China. [ABSTRACT FROM AUTHOR]

This study aims to examine the drivers of investment in renewable energy production. These include the development of the financial system, the price of Brent crude oil and the energy dependency of EU economies. Following the outbreak of the Russia-Ukraine war and the resulting increase in the price of traditional energy sources, it is necessary to understand how to promote the development of alternative energy sources to ensure greater EU energy independence from Russia and meet green energy targets. The PMG-ARDL panel model is conducted to analyse the short-run and long-run relationships between variables. This model can solve the problems of endogeneity, heteroscedasticity, autocorrelation and multi-collinearity. The results obtained are consistent with the literature. However, they add several contributions. First, the study shows how the development of the financial system has an impact on the production of renewable energy sources (RES). Second, the study analyses how the degree of energy dependence of EU countries on export countries influences the production of RES. Finally, the study confirms that geopolitical risk is also a criterion in the assessment of investments in renewable energy. Therefore, this study contributes to the existing literature. Specifically, the study shows that EU countries with more developed financial systems produce more renewable energy. In addition, the study also has practical implications for policymakers and financial authorities. [ABSTRACT FROM AUTHOR]

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Copyright of Business Consultant / Poslovni Konsultant is the property of FINconsult Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This paper investigates the influence of the ongoing crisis of Russia's incursion on Ukraine on the risk dynamics of energy futures contracts with high-frequency data on four different futures contracts using risk metrics of value at risk (VaR) and conditional value at risk (CVaR) for the USA market. Design/methodology/approach: The author used different generalised autoregressive conditional heteroscedasticity - Extreme Value Theory (GARCH)-EVT models and compared the performance of each of the competing models. Backtesting evidence shows that VaR and CVaR combined with GARCH-EVT better estimate risk. Findings: The study results show that combined risk metrics are efficient and adaptive to estimating the risk dynamics and backtesting of the models, revealing that the autoregressive moving average (ARMA) (1,1)-asymmetric power autoregressive conditional heteroscedasticity (APARCH) model performs relatively better than other models. Practical implications: The paper has practical implications for different market participants. From the risk manager's and day traders' angles, the market participants can estimate the risk exposure in the energy futures contract and take positions accordingly. The results are important for oil-importing countries due to the developing supply crisis and price escalation, which can brew inflation in the economy. Originality/value: To the best of the author's knowledge, the paper is the first to throw light on the risk angle of energy futures contracts during the ongoing crisis of the Russia–Ukraine war. [ABSTRACT FROM AUTHOR]

Purpose: The fluctuation in the price of crude oil on the global market has created a lot of attention to the researchers to investigate its price movement. This study tries to address the problem of predicting crude oil prices in a situation of unusual circumstances. Methodology: In this study, Box Jenkins methodology was used to analyze monthly dynamics of the Brent oil price from January 2002 to February 2022. Data were first differenced to achieve stationarity, and then ACF and residual diagnostics were utilized to choose models that were used for analysis. Findings: The performance of various models were evaluated and ARIMA (0, 1, 1) was found to be the best model for forecasting crude oil prices. This study further reveals that despite the corona virus and the Ukraine war having a considerable impact on crude oil prices, such a model is still capable of capturing the underlying volatility in crude oil prices. Originality/Value: Oil demand suddenly decreased as a result of the corona outbreak, but then abruptly increased as a result of the conflict in Ukraine. Therefore, there is a need to update the ARIMA model in order to best predict the price of crude oil in a time of exceptional circumstances. Because of the nature of world oil market, predictions for the medium and long term are often therefore, we have limited the scope of our forecasts in this study to a single year in order to achieve the highest level of accuracy. [ABSTRACT FROM AUTHOR]

This study examines how the rise in energy prices following the Russian invasion of Ukraine (which began on February 24, 2022) has spread to other sectors using the monthly input–output model (56 sectors in 44 countries). The estimated results demonstrate that if prices increase by 20% exclusively in Russia's mining and quarrying (M&Q) sector, there will be almost no global effect. However, if prices increase by 20% in every M&Q sector around the world, global prices (the weighted average across all sectors) will rise by 3.15%, damaging 6.83% of monthly GDP at the pre-invasion level as a surplus loss ($551,080 million per month). Because oil prices rose for almost five months from February 28 to August 3, 2022, the world suffered five times this damage, or 2.85% of annual GDP ($2.7 trillion). This scenario is roughly equivalent to a price change of 5 times in the Russian M&Q sector alone. • Two scenarios for +20% energy prices (fossil fuel) in Russian or global mining sector. • Input–output analysis in 56 sectors/44 countries to seek price rise and surplus loss. • If energy prices increase by 20% only in Russia, global overall prices rise 0.13%. • If increased 20% globally, overall prices rise 3.15% and surplus loses 6.83% of GDP. • To rise global energy price by 20%, the Russian energy price alone must be +5 times. [ABSTRACT FROM AUTHOR]

Copyright of ifo Dresden Berichtet: Ueber Konjunktur, Struktur, Wirtschaftspolitik is the property of ifo Institute for Economic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Energy markets began to tighten in 2021 because of a variety of factors, including the extraordinarily rapid economic rebound following the pandemic. But the situation escalated dramatically into a full-blown global energy crisis following Russia's invasion of Ukraine in February 2022. The price of natural gas reached record highs, and as a result so did electricity in some markets. Oil prices hit their highest level since 2008. [ABSTRACT FROM AUTHOR]

Crude oil is, without a doubt, one of the most significant commodities in the modern world. The highly contagious coronavirus, the conflict between Ukraine and Russia, and not to mention the unusual turn of events worldwide have all significantly impacted crude oil prices. Since oil is required for all critical economic activities, such as production and transportation, a forecast for crude oil prices is essential. Using a range of GARCH models at such an intense time, this study attempted to close this gap by forecasting crude oil volatility. To forecast the returns of Brent crude oil prices from January 2002 to February 2022, this study uses a family of GARCH models. In the respective family of models, GJRGARCH (1,1) was the most effective in predicting the volatility of crude oil prices. The GJRGARCH model was chosen since it had a higher likelihood value and a lower information criteria value. A diagnostic check was done to evaluate the produced model further to ensure that the proposed model was good enough for forecasting crude oil volatility. The study suggests employing the GJRGARCH technique to predict future fluctuations in exceptional circumstances. [ABSTRACT FROM AUTHOR]

Since 2020, two major crisis impacted the global environment: the COVID-19 pandemic and the Russia-Ukraine war. Both had a major impact on international trade and on the evolution of oil prices which has a direct effect on the logistics industry. The scope of our paper is to investigate the effects of oil price evolution on logistics activities, especially transport. Costs of shipment represent an important component of final prices for consumer products and they can influence the general rate of inflation. This is a major concern among government officials in all countries, as high inflation rates can have negative effects on national economies. Our paper is focused on analyzing price trends on each mode of transport, but also prices for integrated logistics services. The results of our analysis offer insight on the logistic market trends and can be useful to professionals from companies, but also for academics interested in this field and consumers worldwide concerned about prices going up. [ABSTRACT FROM AUTHOR]

The effect of the COVID-19 pandemic on crude oil prices just faded; at this moment, the Russia–Ukraine war brought a new crisis. In this paper, a new application is developed that predicts the change in crude oil prices by incorporating these two global effects. Unlike most existing studies, this work uses a dataset that involves data collected over twenty-two years and contains seven different features, such as crude oil opening, closing, intraday highest value, and intraday lowest value. This work applies cross-validation to predict the crude oil prices by using machine learning algorithms (support vector machine, linear regression, and rain forest) and deep learning algorithms (long short-term memory and bidirectional long short-term memory). The results obtained by machine learning and deep learning algorithms are compared. Lastly, the high-performance estimation can be achieved in this work with the average mean absolute error value over 0.3786. [ABSTRACT FROM AUTHOR]

Food price has continued to increase significantly and the oil price continues to be volatile in the midst of a downward trend in the external value of most currencies. This study purposes to shed light on the dynamic interaction between oil prices, exchange rates and food prices in BRICS and investigate the direction of the causality. The study also aims at determining the existence of an oil price threshold on the oil price and food price nexus. In order to achieve the objectives of the study, panel VAR and dynamic threshold models were employed using monthly data from January 2003 to March 2022. The findings show that oil prices and exchange rate shocks exert a significant influence on food prices. Our findings also show that oil prices and exchange rates are significant sources of variations in food prices. The findings confirm the existence of unidirectional causality from oil price to food price, and from exchange rate to food price. This study also establishes the existence of an oil price threshold on the nexus between oil price and food price. It can be recommended that the implementation of sound macroeconomic policies in the oil sectors keep the domestic oil price below the threshold level. [ABSTRACT FROM AUTHOR]

The conflict between Russia and Ukraine has been causing knock-on effects worldwide. The supply and price of major commodity markets (oil, gas, platinum, gold, and silver) have been greatly impacted. Due to the ongoing conflict, financial markets across the world have experienced a strong dynamic regarding commodities prices. This effect can be considered the biggest change since the occurrence of the financial crisis in the year 2008, which explicitly influenced the oil and gold markets. This study attempts to investigate the impacts of the Russian invasion crisis on the dynamic connectedness among five commodities and the G7 and BRIC (leading stock) markets. We have applied the time-varying parameter vector autoregressive (TVP-VAR) method, which reflects the way spillovers are shaped by various crises periods, and we found extreme connectedness among all commodities and markets (G7 and BRIC). The findings show that gold and silver (commodities) and the United States, Canada, China, and Brazil (stock markets) are the receivers from the rest of the commodities/market's transmitters of shocks during this invasion crisis. This research has policy implications that could be beneficial to commodity and stock investors, and these implications could guide them to make many decisions about investment in such tumultuous situations. Policymakers, institutional investors, bankers, and international organizations are the possible beneficiaries of these policy decisions. [ABSTRACT FROM AUTHOR]

There are many challenges facing our Arab world at this stage, and perhaps the most prominent of them are those related to food security. This problem has recently emerged as one of the strategic issues, which carries with it different dimensions and consequences that affect various aspects of life. It also has become a fundamental dilemma, especially in light of the political and military conflicts witnessed by the world. This report attempts to describe food security concerning the Arab countries, in addition to the Russian-Ukrainian war and its repercussions on Arab food security, as food security is an issue that must occupy its realistic place in the programs and strategies of countries and societies. Regarding the food security of the Arab countries, the report indicates that Arab countries are far from achieving self-sufficiency in food commodities. Additionally, many of them suffer from the inability to achieve food security, increasing the import burdens that are increasing steadily. Such a situation reflects the citizens' life level and the economic growth in various Arab countries. The report also stresses that the repercussions of the Russian-Ukrainian war on food security are catastrophic, as Russia and Ukraine are among the largest producers of primary commodities. Therefore, supply chain disruptions have led to a sharp rise in global prices, especially oil and natural gas prices, and a historic leap in food costs. The report concludes that several Arab countries will be able to adapt to the harmful effects of this war, thanks to the financial surpluses they will obtain from the rise in oil prices. However, non-oil countries will suffer trying to deal with the rise in oil prices, in addition to the rise in food prices. The report recommends that dealing with food security difficulties should not be limited to short-term measures but rather should be addressed according to a strategic vision that considers the various current and future variables and in a manner that suits the circumstances of each country. In addition, the report recommends the need to strengthen the social protection network in non-oil countries and improve fiscal policies through prudent guidance on funding sources and conscious identification of priorities. [ABSTRACT FROM AUTHOR]

Crude oil plays a vital role in industrial and social development and has become an integral part of the economic development. However, influenced by policies, wars, etc., it is hard to capture the trend of complex and volatile crude oil price if only one model is used, which often leads to poor forecasts. To enhance the prediction accuracy and robustness of forecasting models, a novel combined forecasting method with time-varying weights, i.e., Jaynes weight hybrid (JWH) model incorporating the Shannon information entropy and several forecasting methods is proposed in this paper. In the selection of the baseline models, the autoregressive integrated moving average in classical statistical forecasting strategies, back propagation neural network, extreme learning machine in neural network and long short-term memory neural network in deep learning models are chosen to fit the crude oil price. Four datasets and three experiments are constructed to verify the prediction ability of the novel combined forecasting model. Empirical results are calculated by five measurement criteria, suggesting that the prediction accuracy of the novel combined method is significantly higher than several comparison models and the mean absolute percentage error of the model has arrived 2.81 % in detail. Particularly, the proposed model has achieved satisfactory performances in Covid-19 and the War in Ukraine, further verifying the robustness of the combined methodology. • A time-varying weight combined forecasting model is proposed for crude oil price. • The proposed model outperforms the comparison models. • The proposed model shows good robustness during special period: COVID-19. • Hypothesis testing is used to test the validity of the proposed combination model. [ABSTRACT FROM AUTHOR]

Russia is a major energy and commodity producer. The current conflict in Ukraine escalates inflation to extreme levels causes a disruption and provokes high risks to global growth. Brent oil and natural gas prices spiked early in March before falling back, with Brent oil ending the month at $103 per barrel and European gas prices at €121 per megawatt hour, up 33% and 55% respectively since the start of the year. The European Commission announced ambitious plans to reduce imports of gas from Russia by two-thirds before the end of the year via more diversification, energy efficiency and by accelerating investments in wind and solar power plants. Despite this ambition, the March EU summit in Versailles highlighted that there are few near-term alternatives to Russian gas and that reducing European dependency on Russian gas requires a long-term strategy. [ABSTRACT FROM AUTHOR]

This paper aims to provide analysis of current state and perspectives of the economic efficiency of sunflower seed production in Ukraine. There are investigated economic efficiency indicators of sunflower production in contemporary conditions; price analysis; main factors of further development of sunflower growing. According to the research results, the world market of sunflower seed and products of its processing (i.e. oil and meal) is developing rapidly. Ukraine occupies a significant place in this market segment. Our research has shown the close integration of the Ukrainian sunflower, oil and meal markets with world markets. The supply of sunflower seed depends primarily on production volumes and demand is determined by the consumption level of Ukrainian processing enterprises. Production cost influence significantly on the economic efficiency of sunflower seed. In this framework we analyzed one of production cost component - crude oil prices that in turn reflect on sunflower seed prices. Co-integration results showed that in the long-run Ukrainian sunflower seed could follow crude oil prices in common path. [ABSTRACT FROM AUTHOR]

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Copyright of Economic Annals-XXI / Ekonomìčnij Časopis-XXI is the property of Institute of Society Transformation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research background: The analysts of the petroleum product markets of industrial countries believe that the elasticity of demand varies at different periods, which gave rise to the hypothesis that behavioral and structural factors have changed the consumers' reaction during the last few decades, with a change in prices of petroleum products. Purpose of the article: The purpose of this article is to study the elasticity of demand and prices in order to identify changes in consumer behavior in the oil market after significant socioeconomic shocks and to establish a correlation between changes in elasticity and price volatility, with the Ukrainian petroleum products market as an illustrative example. Methods: Based on the time series of the petroleum product market of Ukraine, static and dynamic models for assessing the demand elasticity were constructed. It was found that the time series of demand for petroleum products is non-stationary but then the time series of the first differences is stationary according to the extended Dickey-Fuller test; further, the fact of cointegration between time series of consumption, income, and prices was established by the Johansson test. This made it possible to construct co-integration dependence, allowing, in turn, the development of models for assessing the elasticity of demand for petroleum products, on the basis of which objective assessments of changes in consumer behavior were established. Analysis of the monthly calculation of petroleum products' price volatility during the period 2008 to 2018 has showed that the values of volatility increased abnormally in the period between the beginning of 2014 and the middle of 2015. The estimates of price and demand elasticities obtained for the two periods up to the beginning of 2014 and the second half of 2015 differ significantly from the values of the corresponding elasticities between the beginning of 2014 and the middle of 2015. Findings & Value added: Assessments of income elasticities and price elasticities for petroleum products in the Ukrainian market were obtained by three co-integration models, both short and long term, for each of the three previously defined time intervals. In one of them, characterized by a high level of price volatility conditionally referred to as a crisis, the value of elasticities differed markedly from the corresponding values in the other two periods, in particular, -0.383 for price elasticity and 1.068 for a long-term bond. In the other two periods, these were, respectively, 0.543 for price elasticity and 0.274 for long-term pre-crisis elasticity, and -0.470 for price elasticity and 0.235 for long-term post-crisis elasticity. Appropriate elasticity estimates were obtained for both the short-run and the dynamic model, for the same defined intervals. A comparison of these estimates showed the closeness of the values of elasticities for the pre-crisis and post-crisis intervals and a marked difference from the estimates of the elasticities in the crisis interval. Thus, it was found that a significant change in elasticities is accompanied by an increase in price volatility. [ABSTRACT FROM AUTHOR]

Copyright of Economic Annals-XXI / Ekonomìčnij Časopis-XXI is the property of Institute of Society Transformation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study analyses the effect of geopolitical tensions, oil prices, and carbon emission futures on green metal prices using the SVAR model. The study focuses on the Russia-Ukraine conflict and utilises a real-time daily dataset from December 1, 2021, to March 7, 2023. The findings show that geopolitical tensions and carbon emissions futures returns have a negative effect on green metal market returns, with oil price returns having a more significant negative impact than the observed temporary upticks. The study also identifies copper, nickel, silver, and zinc returns as negatively associated with geopolitical risk, while aluminium demonstrates a significant positive correlation after temporary downticks. The research further reveals that aluminium, copper, and zinc returns respond positively to carbon emissions futures, while nickel and silver returns correlate negatively. Additionally, oil price shocks negatively impact aluminium, copper, and zinc returns while exerting mixed influence on nickel and silver price returns, with more pronounced positive and negative shocks, respectively. The results have particular relevance in the context of the Russia-Ukraine War, with the positive response of carbon emissions futures to geopolitical risk potentially due to possible disruptions of policies aimed at reducing carbon emissions. The practical policy implications of the findings are discussed. Note: Sig denotes significance. [Display omitted] • The study examines effects of geopolitics, oil prices, and carbon futures on green metal prices. • Geopolitical risk and carbon emissions futures negatively affect green metal market returns. • Geopolitical risk negatively affects copper, nickel, silver, zinc; aluminium is positive. • Carbon futures positively affect aluminium, copper, zinc and negatively affect nickel, silver. • The findings offer practical policy implications for investment and risk management. [ABSTRACT FROM AUTHOR]