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This study investigates the return and volatility spillovers between BRICS bonds and oil price volatility in the pre-COVID and during COVID periods by employing the bivariate EGARCH model, cross-quantilogram approach, and wavelet coherence framework. The findings are mixed when comparing the volatility transmissions with various nations and in two periods since variations in return and volatility spillovers between oil prices and bond market returns have co-moved through time. In particular, they increased during the COVID-19 outbreak, which supports the idea that financial market integration intensified during the crisis. Additionally, oil prices have a positive impact on bond markets in Russia and South Africa in both periods, while there are weak and negative relationships in China, India, and Brazil in pre-COVID and pandemic periods. This information has consequences for risk management and portfolio decisions for investors with a substantial position in the BRICS bonds market. [ABSTRACT FROM AUTHOR]

This study explores the effect of green bonds, oil prices, and the coronavirus disease 2019 (COVID-19) pandemic on industrial carbon dioxide (CO2) emissions. In this context, this study examines the United States of America (USA), which is the biggest economy in the world, uses weekly data between March 6, 2020 and September 30, 2022, and applies a novel wavelet local multiple correlation (WLMC) approach under time-varying and frequency-varying perspective. The novel empirical findings shows that (i) there is a strong negative (positive) co-movement between industrial CO2 emissions and green bonds in the short-run (long-run); (ii) there is a strong positive (negative) co-movement between industrial CO2 emissions and oil price in the medium-run (long-run); (iii) there is a strong negative (positive) co-movement between industrial CO2 emissions and the COVID-19 pandemic in the medium-run (long-run); (iv) the oil price is the dominant factor, whereas there are changing effect of the variables on each other at different times and frequencies; and (vi) overall, there are long-run asymmetric and dynamic correlations between industrial CO2 emissions and variables. Hence, the empirical results highlight the asymmetric, time-varying, and frequency-varying effects of green bonds, oil prices, and the COVID-19 pandemic on industrial CO2 emissions by presenting fresh and novel evidence. Moreover, the study proposes policy implications for the USA government. [ABSTRACT FROM AUTHOR]

Recent theoretical developments in economics distinguish between risk and ambiguity (Knightian uncertainty). Using state-of-the-art methods with intraday stock market data from February 1993 to February 2021, we derive financial ambiguity and empirically examine the effect of shocks to it on the price and volatility of crude oil. We provide evidence that ambiguity carries important information about future oil returns and volatility perceived by investors. We validate these results using Granger causality and in-sample and out-of-sample forecasting tests. Our findings reveal that financial ambiguity is a possible factor that explains future drops in oil prices and their increased variability. Our findings will benefit scholars and investors interested in how financial ambiguity shapes short-term oil prices. [ABSTRACT FROM AUTHOR]

We investigated the relationship between supply chain disruptions (SCD) and the financial performance (FP) of European automotive companies across emerging and developed nations. Addressing gaps in the literature, it offers a comprehensive, industry-specific analysis using panel data from 73 automotive firms across 21 European countries from 2013 to 2022. We examined the impact of SCD on return on assets (ROA), return on equity (ROE), and stock returns (SR), while controlling for factors such as age, leverage, size, economic growth, inflation, and unemployment. Our findings revealed that disruptions related to industrial materials and iron prices (IRAW) positively influence stock market performance, with a 99.5% increase in SR per 1% rise in IRAW, suggesting increased demand. Conversely, precious metal prices negatively affected all financial metrics, reducing ROE by 17.3% and SR by 55.5% per 1% increase. Heightened shipping costs showed varied impacts on ROA and ROE but contributed to a 12.9% average increase in SR, indicating effective cost transfer to consumers. The pandemic years significantly decreased ROA, ROE, and SR, highlighting challenges faced by the automotive sector. Rising oil prices showed no significant association, underscoring the importance of hedging strategies. The control variable outcomes emphasize the need for detailed evaluation in assessing financial performance. This study's contribution lies in its detailed analysis of specific disruptions within the automotive industry and their distinct impacts on financial performance metrics, providing a nuanced understanding that addresses significant gaps in the existing literature. [ABSTRACT FROM AUTHOR]

This article estimates the pass‐through of different shocks into different U.S. prices that are important for policy makers. The investigation is based on a structural vector autoregression model, where quarterly data are used. The empirical results depict oil price pass‐through, exchange rate pass‐through, import‐price pass‐through, and producer price pass‐through into import prices, producer prices, and consumer prices for the U.S. economy. Policy implications suggest that achieving and sustaining consumer price stability highly depend on monitoring the developments in oil prices, followed by import prices and producer prices. [ABSTRACT FROM AUTHOR]

This paper examines the volatility risk in the KSA stock market (Tadawul), with a specific focus on predicting volatility using the logarithm of the standard deviation of stock market prices (LSCP) as the output variable. To enhance volatility prediction, it proposes the combined use of the dynamic evolving neural fuzzy inference system (DENFIS) and the nonlinear spectral model, maximum overlapping discrete wavelet transform (MODWT). This study utilizes a dataset comprising 4609 observations and investigates the inputs of lag 1 of the close stock price (LCP), the natural logarithm of oil price (Loil), the natural logarithm of cost of living (LCL), and the interbank rate (IB), determined through autocorrelation (AC), partial autocorrelation (PAC), correlation, and Granger causality tests. Regression analysis reveals significant effects of variables on LSCP: LCP has a negative effect, and Loil has a positive effect in the ordinary least square (OLS) model, while LCL and IB have positive effects in the fixed effect model and negative effects in the random effect model. The MODWT-Haar-DENFIS model was developed as we found that the model has the potential to be an effective model for stock market forecasting. The results provide valuable insights for investors and policymakers, aiding in risk management, investment decisions, and the development of measures to mitigate stock market volatility. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of global geopolitical risks and global supply chain pressures on global inflation for the monthly period of 1999M1–2022M12. The investigation is based on a structural vector autoregression model, where the effects of global oil prices and global monetary policy are controlled for. Four alternative measures of inflation are used, including headline, core, food, and energy inflation. The empirical results show that disruptions in global supply chains are the main drivers of global inflation in the long run as the corresponding shocks explain the lion's share of volatilities in headline inflation (by 32%), core inflation (by 30%), and food inflation (by 22%), followed by oil price shocks and policy rate shocks. In comparison, energy inflation is explained the most by oil price shocks (by 55%) followed by supply chain shocks and policy rate shocks. Positive supply chain pressure and oil price shocks have positive and statistically significant effects on headline inflation even after five years, whereas positive policy rate shocks have negative and statistically significant effects on headline inflation in the long run. In contrast, positive shocks to geopolitical risk result in higher headline inflation only up to one year, with insignificant effects in the long run. Several policy implications follow. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economic Administrative & Legal Sciences is the property of Arab Journal of Sciences & Research Publishing (AJSRP) 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.)

In this paper, we investigate house price shocks on the macroeconomic variables (financial market, inflation, and real sector) of the G7 economies. We use the GVAR to capture the spillover effects from the U.S. housing market and oil prices on these economies from 1991M3–2022M10. We identify the U.S. house price shock using the Structural Generalized Impulse Response Function, house supply and demand variables, and regional divergence. We find that the domestic stock markets and industrial production are the most sensitive to house price shocks. We further compare the importance of house and oil prices on domestic fluctuations. The estimates reinforce the previous findings: U.S. house prices are responsible for a quarter of the domestic volatility of the stock markets and industrial production. In the other macroeconomic segments, the effects of house prices are present, but in lower values. Our results show that house prices provoke more domestic fluctuations than oil prices. Finally, we also found that short and long‐term credit markets, as well as stock markets, transmit the house price shock to industrial production. Consequently, we provide potential channels to comprehend the spillover effect of U.S. house prices on international markets. [ABSTRACT FROM AUTHOR]

This study examines the causality relationship between oil price movements and geopolitical risks for a group of 18 geopolitically sensitive countries, using monthly data by implementing all quantiles distributions. Contrary to earlier studies, which have applied the Granger‐causality through the conditional mean regression, this research estimates the association between the variables through Granger causality within quantiles. Evidence of a two‐way causality is found linking the changes in geopolitical risk and fluctuations in oil prices in the case of Thailand, Argentina, Israel, China, Mexico India, Korea, Indonesia, South Africa, Turkey, Philippines, Venezuela, Ukraine, and others. In addition, it is confirmed that oil prices Granger cause geopolitical risks for the countries like Brazil, Malaysia, and Colombia. Furthermore, a one‐way causality direction is found from changes in geopolitical risk to shifts in oil prices in Russia and Saudi Arabia, which are observed as super oil rich states. This study findings highlight the importance of government policies and business strategies that aim at containing the effects of geopolitical risk and the resulting oil price movements. [ABSTRACT FROM AUTHOR]

This article investigates the effect of global oil and wheat prices, and local price shocks on the real price of wheat flour in Lebanon. We estimate a structural vector autoregressive model with exogenous variables (SVARX) using Bayesian methods. We then compute the impulse response functions and find that global commodity price shocks play a trivial role. Meanwhile, local gasoline price and wheat flour price‐specific shocks trigger large increases in the Lebanese wheat flour price on impact. Furthermore, since 2020, local gasoline price and wheat flour price‐specific shocks have contributed the most to the historical variation in the Lebanese wheat flour price. [ABSTRACT FROM AUTHOR]

The growing interstate rivalry sweeps more and more areas of international relations, including energy issues. While the ‘traditional’ oil and gas exporters are trying to maintain or even strengthen their current positions, new players in the oil and gas market are trying to challenge the status quo. The changing situation on the oil market due to the ‘price war’ between the United States and OPEC countries serves as a clear manifestation of this trend and encourages the OPEC members to coordinate their policies more closely with external partners. This paper aims to identify the prerequisites and assess the dynamics of cooperation between Russia and the Kingdom of Saudi Arabia (KSA) within the OPEC+ framework. The first section of the paper examines the logic behind the OPEC+ agreement in the context of the development of energy cooperation between the Russian Federation and the KSA before the outbreak of the Ukrainian crisis. The authors note that the new format demonstrated stability within the first years of its existence and generated closer cooperation between the hydrocarbon exporting countries. The second section examines the development of OPEC+ in the face of increased geopolitical pressure from the United States and its allies. The authors conclude that the efforts of Washington and oil importing countries to undermine energy cooperation within the OPEC+ format have not succeeded, on the contrary the international stance of the group members has only strengthened. In conclusion, the authors emphasize that OPEC+ not only contributes to the stability of energy prices, but also brings tangible political benefits to its members. At the same time, according to the authors, the future of this format largely depends on the dynamics of international environment and, particularly, on the scale of the US-Russian confrontation.

Abstract Recent theoretical developments in economics distinguish between risk and ambiguity (Knightian uncertainty). Using state-of-the-art methods with intraday stock market data from February 1993 to February 2021, we derive financial ambiguity and empirically examine the effect of shocks to it on the price and volatility of crude oil. We provide evidence that ambiguity carries important information about future oil returns and volatility perceived by investors. We validate these results using Granger causality and in-sample and out-of-sample forecasting tests. Our findings reveal that financial ambiguity is a possible factor that explains future drops in oil prices and their increased variability. Our findings will benefit scholars and investors interested in how financial ambiguity shapes short-term oil prices.

Abstract In 2020, the world experienced several significant events, including the coronavirus (COVID-19) pandemic and the collapse of international crude oil prices. The rapid spread of this pandemic has dramatic impacts on financial markets all over the world, thereby increasing market risk aversion in an unprecedented way since the subprime financial crisis. The decline in stock markets implied volatilities of equity and oil prices, thereby heightening turmoil in global financial markets despite comprehensive and substantial financial reforms. To this end, we investigated the likely effects of this pandemic on the Saudi stock market while controlling for oil prices based on daily data for a period from 1/1/2020 to 19/9/2022. To ascertain the existence of a long-run equilibrium relationship between the variables, we applied autoregressive distributed lag (ARDL) modeling and the error correction model, with this ultimately revealing the existence of strong cointegration in the long run. The ARDL bounds test was found to be robust by combined cointegration tests, thus providing further evidence of a strong relationship in the long run. Granger causality tests also yielded evidence of causality between the variables in both directions. The total COVID-19 confirmed cases and oil prices also caused movements in stock returns in the short run. Our findings have some prominent implications for asset managers and policymakers to improve stock market efficiency and boost global economic activity. Saudi authorities can consequently remove the regulatory and legal obstacles to develop their stock market and better improve the risk management, which will allow to make quick decisions in response to any oil price volatilities. Policymakers should also adopt proactive strategies that can comfort stock investors’ anxieties over the increasing oil price volatilities. Finally, the findings should be treated with some cautions because of the limited sample size and the tests’ statistical inference. Nevertheless, they do open opportunities for further studies to look in more detail at how the COVID-19 pandemic affected, over the short and long run, monetary and fiscal policy coordination, financial stability, and various other macroeconomic indicators in Saudi Arabia.

In 2020, the world experienced several significant events, including the coronavirus (COVID-19) pandemic and the collapse of international crude oil prices. The rapid spread of this pandemic has dramatic impacts on financial markets all over the world, thereby increasing market risk aversion in an unprecedented way since the subprime financial crisis. The decline in stock markets implied volatilities of equity and oil prices, thereby heightening turmoil in global financial markets despite comprehensive and substantial financial reforms. To this end, we investigated the likely effects of this pandemic on the Saudi stock market while controlling for oil prices based on daily data for a period from 1/1/2020 to 19/9/2022. To ascertain the existence of a long-run equilibrium relationship between the variables, we applied autoregressive distributed lag (ARDL) modeling and the error correction model, with this ultimately revealing the existence of strong cointegration in the long run. The ARDL bounds test was found to be robust by combined cointegration tests, thus providing further evidence of a strong relationship in the long run. Granger causality tests also yielded evidence of causality between the variables in both directions. The total COVID-19 confirmed cases and oil prices also caused movements in stock returns in the short run. Our findings have some prominent implications for asset managers and policymakers to improve stock market efficiency and boost global economic activity. Saudi authorities can consequently remove the regulatory and legal obstacles to develop their stock market and better improve the risk management, which will allow to make quick decisions in response to any oil price volatilities. Policymakers should also adopt proactive strategies that can comfort stock investors' anxieties over the increasing oil price volatilities. Finally, the findings should be treated with some cautions because of the limited sample size and the tests' statistical inference. Nevertheless, they do open opportunities for further studies to look in more detail at how the COVID-19 pandemic affected, over the short and long run, monetary and fiscal policy coordination, financial stability, and various other macroeconomic indicators in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekonomik Yorumlar is the property of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekomomik Yorumlar 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.)

Copyright of Kastamonu University Journal of Economics & Administrative Sciences Faculty / Kastamonu Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of Kastamonu University 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.)

We assess the impact of changes in oil prices on building permit applications in the top oil-producing counties, using monthly data between 2004 and 2021. In the long-run, a positive shock in oil prices increases the number of home permit applications in Lea and McKenzie Counties. Long-run point estimates also reveal that a 10 percent increase in oil prices reduces building plans by 2 and 3.4 percent in Kern and Oklahoma Counties, respectively. In Midland and McKenzie Counties, only long-run changes in oil prices impact home investment decisions while short-run changes have no impact. The inverse relationship between oil prices and permit applications hints that investors may dampen construction plans in lieu of higher costs of raw materials during periods of higher energy prices. Threshold Autoregressive model findings demonstrate that changes in permit applications occur during periods of high oil prices. Our findings imply that the expansionary monetary or fiscal policies that would be required to revive the housing market would need to be regime and region specific. Thus, coordination between regional lending units and investors is important and can boost recoveries from economic downturns. [ABSTRACT FROM AUTHOR]

The oil price is a dominant factor to affect the economic growth. This study analyzes the influence of oil prices on economic growth of member countries of South Asian Association for Regional Cooperation (SAARC). The dynamic effects of such oil prices are estimated by the mean of Pooled Mean Group Autoregressive Distributed Lag Model (ARDL) and Panel Fully Modified Least Square (FMOLS). The time series data are used for the period of 1995 to 2023. The results concluded oil prices to significantly affect economic growth and in negative. However, fuel imports are found to favor the economic growth. Likewise, labor force and Gross Fixed Capital (GCF) formation have significant and positive effect on economic growth. The findings on ARDL and FMOLS are similar. Noticeable, in case of short run, oil prices effect on economic growth is insignificant. It shows that effect of oil prices on economic growth is recordable in long run. The study concludes oil prices to have significant role towards economic growth. However, negative affectation of oil prices cannot be taken in a way that SAARC member better start losing their dependency on oil but to explore substitute energy sources. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the possible explanatory effect of geopolitical risks on the oil price changes from February 1986 to December 2019 by employing the nonlinear bivariate Granger causality test and frequency domain Granger causality test based on the geopolitical risk index. The results suggest that there exists a nonlinear causality from geopolitical risks to crude oil prices. Moreover, geopolitical risks have a short-term impact on oil prices, less than 12 months. Actual geopolitical events have a smaller and less lasting impact on oil prices than pure geopolitical risks. [ABSTRACT FROM AUTHOR]

Summary: Predicting carbon and oil prices is recently gaining relevance in the climate change literature. This is due to the fact that conventional energy market analysis and the design of mechanisms for climate change mitigation constitute key variables for artificial carbon markets. Yet, modelling non‐linear effects in time series remains a major challenge for carbon and oil price forecasting. Hence, hybrid models seem to be appealing alternatives for this purpose. This study evaluates the performance of 12 hybrid models, which weigh results from random forest, support vector machine, autoregressive integrated moving average and the non‐linear autoregressive neural network models. The weights are determined by (i) assuming equal weights, (ii) using a neural network to optimise individual weights and (iii) employing deep learning techniques. The findings of our work confirm the salient characteristics of modelling the non‐linear effects of time series and the potential of hybrid models based on neural networks and deep learning in predicting carbon and oil price returns. Furthermore, the best results are obtained from hybrid models that combine machine learning and traditional econometric techniques as inputs, which capture the linear and non‐linear effects of time series. [ABSTRACT FROM AUTHOR]

Exploring the nexus between oil prices and remittances is critical to both remitting and recipient countries. Employing non-linear panel autoregressive distributed lag (NARDL) approach, we examine the relationship for 32 sub-Sahara African countries over the period 1986-2019. The results reveal that: (1) changes in oil prices and remittance inflows are asymmetrically associated only in the short run for the entire sample; (2) when the sample is divided into middle and low-income subgroups, asymmetric effect is confirmed for former subgroup only in the long run, while it is confirmed for latter subgroup only in the short run, (3) in the long run, positive movement in oil prices reduces remittances for the sample and in the middle income subgroup, whereas it increases remittances in the low-income subgroup, (4) Negative oil price movement reduces remittances for the entire sample and the two subgroups; (5) in the short run, a reduction in oil prices increases remittance inflows only for the entire sample and for the low-income subgroup. These results suggest that policymakers in SSA should implement policies that will reduce oil price risks on remittance inflows especially in the long run. [ABSTRACT FROM AUTHOR]

The purpose of this article is to investigate the new driving forces behind China's green energy and further assess the impact of green energy on climate change. The existing literature has used linear methods to investigate green energy, ignoring the non-linear relationships between economic variables. The nonparametric models can accurately simulate nonlinear relationships between economic variables. This paper constructs a nonparametric additive model and uses it to explore green energy. The empirical results show that the impact of green finance on green energy is more prominent in the later stage (a U-shaped impact). Fiscal decentralization also exerts a positive U-shaped impact, meaning that expanding local fiscal autonomy has contributed to green energy growth in the later stage. Similarly, the impact of oil prices and foreign direct investment demonstrates a positive U-shaped pattern. However, the nonlinear impact of environmental pressure displays an inverted U-shaped pattern. Furthermore, this article explores the impact of green energy on climate change and its impact mechanisms. The results exhibit green energy generates a positive U-shaped impact on climate change, meaning that the role of green energy in mitigating climate change gradually becomes prominent over time. Mechanism analysis exhibits that industrial structure and energy structure both produce a nonlinear influence on climate change. [ABSTRACT FROM AUTHOR]

This study deals with the evolution of fuels' prices over time in the EU. The central research inquiry revolves around whether there exists any correlation among the trajectories followed by national prices in the gasoline and diesel markets. The EU, and more specifically the Euro-Area, by its construction, is treated as an OCA (Optimum Currency Area). In an OCA, certain conditions are met to ensure the smooth functioning of a common currency. The fuel price synchronization is essential because it contributes to the effective implementation of policies and promotes stability across the entire macroeconomy. The study covers the period of 2017–2022. For each type of fuel and year, we construct an individual network where network nodes represent the EU member states while the edges connecting these nodes represent strong temporal fuel price correlations among the member states. The properties of the resulting networks are analyzed within a Complex Network framework. Our goal is twofold: first, to detect any potential convergence or divergence in the trajectories of the prices, and second, to investigate the impact of tax and duty implementation on the co-movements of the prices. The empirical findings show that diesel markets have a more pronounced pattern of price co-movements compared to the gasoline markets. Moreover, the application of fuel taxation policies seems to adversely affect the co-movements of prices. [ABSTRACT FROM AUTHOR]

The aim of this article is to examine the relationship among oil prices, foreign trade in foods and exchange rate with the Autoregressive Distributed Lag Model (ARDL). The study uses annual data on exchange rate, oil prices, purchasing power parity, and food trade in the Turkish economy between 1960 and 2021. According to the estimation results, there is a cointegration relationship among oil prices, foreign trade in foods and exchange rate. In the long run, the rises in oil prices and food imports cause the national currency to depreciate. [ABSTRACT FROM AUTHOR]

This study applies a quantile cointegration model to investigate if COVID-19 outbreaks in the BRICS (China, India, Russia, Brazil, and South Africa) and the United States have a long-run equilibrium relationship with the dynamics of oil prices. (1) The standard cointegration models are unstable, indicating the possibility of structural breaks and nonlinearity in the relationship between the COVID-19 pandemic and oil prices; (2) The results of the quantile cointegration model suggest the COVID-19 pandemic and oil prices are nearly cointegrated over whole quantiles of the oil price distribution for the United States, Russia, South Africa, and Brazil. However, the long-run equilibrium relationship between the COVID-19 pandemic and oil prices in China is more likely to occur in the lower quantiles of the oil price distribution; (3) For India, the equilibrium link exists only across the two higher quantiles (0.7 and 0.8 quantiles) of the oil price distribution. Finally, our research has significant policy implications for the governments of the world's largest countries that are concerned about the impact of the COVID-19 pandemic outbreak on oil prices. [ABSTRACT FROM AUTHOR]

This study investigates the regime‐specific relationship between the USD exchange rate and oil prices. We embed the threshold variables of structural breaks and oil financialization into the VAR model of the dollar–oil relationship. Using daily data from 1983 to 2022, we find that a marked structural change in the dollar–oil relation exists in 2001 and that deepening oil financialization drives the structural change, making the portfolio effect the dominant effect of the dollar on oil prices. After 2001, the dollar negatively affects oil prices in both the long and short run. Furthermore, our threshold regression based on the investor self‐adaptive expectation model shows that investor expectations significantly affect the dollar–oil relationship under oil financialization. A sharp increase in USD exchange rate volatility would strengthen investors' unilateral expectation of the trend of the USD exchange rate, which further enhances the portfolio effect, resulting in an even stronger negative effect of the dollar on oil prices. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between the economic policy uncertainty (EPU) and oil prices in OPEC countries utilizing a nonparametric panel data model to allow the data to outfitter the shape offunctional form. The change in oil price reflects the changes in fundamental and non-fundamentalfactors. Using yearly data from OPEC countries from 2003 to 2017, we find that EPU has a positive and significant effect on price. The research findings show renewable energies, oil price expectations, and EPU are the most influential factors, respectively. So policymakers should devote more attention to fundamental aspects. The planning and establishment of the necessary infrastructure for the extensive use of renewable energies will lead to a significant reduction in oil prices. Although the research findings indicate that renewable energy and oil are substitutes, oil and gas will continue to grow in the decades ahead. Also, the rise in the EPU will lead to an increase in oil prices. In this regard, policy authorities try to mitigate the adverse effects of oil price increases, which in turn reduces the success of those policies and causes an increment in economic policy uncertainty. [ABSTRACT FROM AUTHOR]

General topic in this paper is the role of oil industry in global economy and the result of changing oil prices on economic variables in Azerbaijan. The major aim is to analyze the effects of crude oil price changes on Azerbaijan economy in order to measure how changes in oil prices affect its basic economic variables. At the beginning, a general outlook on oil industry will be given and the main oil exporting economies will be analized. However, main focus will be on the structure of Azerbaijan’s economy and it will examine the example of Azerbaijan as a transition economy where oil and gas production and exports have steadily increased from the mid ‘90s. Many critical oil and gas extraction projects have been implemented; while the oil and gas sector's dominance in the economy poses a number of threats to the balanced development of the country's economy. It will show how the economy of Azerbaijan was affected after the oil crisis, such as other developing countries that mainly depending on oil and petroleum products. For this purpose, VEC Model has been applied, consdering Azerbaijan Statistic Data and analized the effects of oil price fluctuations on its economic variables parallel with the oil industry. The main findings indicate that Azerbaijan's economy is strongly dependent on resource exports, which produce high growth rates when commodity prices are favorable, but end up with lower macroeconomic performance when global oil prices fall. It is clear that the main income of Azerbaijan comes from oil and oil products, so the export of oil will have a high impact on the saving level of the population. At the time of high revenues come from export and the low exchange rate of the foreign currency in Azerbaijan has also led to the low cost of imported goods. As well demonstrated in the research, Azerbaijan economy has been widely exposed to major crude oil price distortions. It can be said that in order to avoid increasing overdependence on oil earnings, diversifying the economy should be a top priority.

ABSTRACT: This study explores the impacts of the exchange rate, consumer confidence, oil prices on the stock returns of the Borsa Istanbul (BIST) manufacturing firms for the timeline aftermath of the Covid-19 pandemic (March 2020 – September 2022). As the manufacturing companies comprise the majority of the stock market of the BIST, the composite index of these industrial companies (XUSIN) is selected as the response variable. Implementing the autoregressive distributed lag (ARDL) bounds-testing methodology on the monthly time series data, the cointegration existence is detected among the series. The empirical results also show that oil price is the most significant determinant among these variables affecting manufacturing companies’ returns for the long-run. When considering oil as a vital production input in industries, the decreases in stock prices resulting from oil price rises (i.e. increases in production costs) are inevitable. However, the significant long-run effects of exchange rates and the consumer confidence index on stock returns of this industry cannot be detected for the Covid-19 period. ÖZ: Bu çalışma Borsa Istanbul (BIST) bünyesinde işlem gören imalat sektöründe yer alan şirketlerin hisse senedi getirileri üzerindeki döviz kurunun, tüketici güveninin ve petrol fiyatlarının etkilerini Covid-19 pandemi dönemi ve sonrası (Mart 2020 – Eylül 2022) için araştırmaktadır. İmalat sektöründeki firmalar, BIST hisse senedi piyasasının büyük bir bölümünü oluşturduğu için bu şirketlerin kompozit endeksi olan BISTSINAI endeksi bağımlı değişken olarak seçilmiştir. Gecikmesi dağıtılmış otoregresif (autoregressive distributed lag - ARDL) sınır testi yönteminin aylık zaman serilerine uygulanması ile değişkenler arasında eşbütünleşme ilişkisi tespit edilmiştir. Ampirik bulgular aynı zamanda uzun dönemde petrol fiyatlarının hisse senedi getirileri üzerinde en çok etkiyi yapan değişken olduğuna işaret etmektedir. İmalat sektöründe petrolün en önemli üretim girdilerinden biri olduğu göz önüne alındığında, petrol fiyatlarında oluşan artış dolayısıyla üretim maliyetlerinde meydana gelen artışın hisse senedi fiyatlarını olumsuz yönde etkilemesi kaçınılmaz olmaktadır. Diğer taraftan, bu sektörün hisse senedi getirileri üzerinde döviz kurunun ve tüketici güveninin uzun dönemli ve istatistiksel olarak anlamlı etkileri bulunamamıştır.

Over the last decade, a significant concern in the energy market has been the decline in the USA’s oil imports and fall in energy prices. In this context, we investigate the impact of the shale revolution in the USA on oil prices and examine its potential replicability in other shale-rich countries. Employing the Auto Regressive Distributive Lag Model (ARDL), the influence of the shale energy boom on WTI oil prices has been quantified, using monthly time series data from 2008 to 2019. The results reveal that shale oil output has no effect on oil prices in the short run. However, we found a significant negative impact on oil prices in the long run, as shale development enhances the global energy supply. Additionally, we found that the shale rich countries lack the infrastructure, financing, and have limited property rights, which hampers their chances of replicating the success of shale development in USA. We recommend that countries trying to develop shale resources invest in factors that led to the shale revolution in the USA. Additionally, as low oil prices will reduce future investment in shale oil and gas exploration, the US government should offer adequate incentives for private firms to remain competitive in the energy sector and continue enjoying the benefits of the shale revolution.

AbstractThe performance of bank stocks exhibits a country’s overall financial health and signals economic growth. Therefore, understanding the interaction of macroeconomic factors on bank stock returns is crucial for the valuation of financial assets, especially in a highly volatile stock market. Although macroeconomic factors and their impact on bank stock returns have been extensively investigated, there is still a dearth of comprehensive review articles in this domain. To address this lacuna, we conducted a systematic review to identify the macroeconomic determinants driving bank stock returns. Through a systematic search, 64 articles were identified from two electronic databases for literature synthesis based on inclusion and exclusion criteria from 1980–2023. The review posits valuable insights into the macroeconomic factors that influence bank stock returns, the nuances of the variables’ effects and the methodologies employed in these studies. The key macroeconomic factors identified include interest and exchange rate sensitivity, which has been studied extensively; however, the impact of monetary policies, gold prices and oil prices needs further investigation. Subsequently, the study documents various bank-specific characteristics that influence the relationship between macroeconomic factors and bank stock returns.

AbstractOver the past three decades, Somalia’s economic landscape has witnessed a noticeable dependence on imported goods. The exchange rate was unregulated owing to the collapse of the country’s central bank. This unregulated environment has introduced significant volatility in exchange rates, profoundly impacting consumer prices and fostering a prevalent shift towards the utilization of the US dollar in economic transactions. Hence, this undertaking delves into the asymmetric effects of exchange rates on consumer prices in the presence of dollarization in Somalia from 1995 to 2019. Employing both linear and nonlinear autoregressive distributed lag (NARDL) cointegration methodologies, we explore the short-run and long-run dynamics between exchange rates and consumer price levels. The long-run empirical results from the NARDL demonstrate asymmetrical cointegration between the unregulated exchange rate and inflation in Somalia. Both appreciation and depreciation of exchange rates exert differing impacts on consumer prices, with depreciation exhibiting a more pronounced effect. In addition, the evidence suggests that the exchange rate pass-through is incomplete in Somalia regarding its inelastic coefficient. Oil prices exhibit a substantial and statistically significant association with inflation, both in the long-run and short-run, while GDP remains inconsequential. In the short-run, the most remarkable outcome indicates that dollarization significantly contributes to mitigating inflationary pressures. Based on our empirical insights, the central bank should enhance regulatory oversight of the foreign exchange market by strictly controlling and prohibiting the issuance of counterfeit banknotes to achieve price stability.

This paper aims to investigate the dynamic and asymmetric linkage between crude oil, oil uncertainty, and the United States (US) equity markets across various horizons and tails using a combination of a time-frequency approach, Granger causality, and quantile-on-quantile regression from January 2020 to December 2022. The empirical results indicate that causal relationships and the dynamic co-movement between crude oil, oil implied volatility, and the Dow Jones industrial and transportation indices are confirmed across various frequencies through wavelet-based Granger causality and wavelet coherence. Then, the wavelet-based quantile-on-quantile regression shows that the relationship between oil, oil implied volatility, and both US equity markets is heterogeneous and asymmetric across short-and long-run horizons, in particular. The findings provide new insights into the sensitivity of US stock markets to oil shocks across various time frequencies and tails, offering several portfolio implications useful for heterogeneous investors and portfolio managers. [ABSTRACT FROM AUTHOR]

The objective of the research is to investigate the evolution of food price inflation in the 27 Member States between January 2001 and May 2022. Our paper focuses on the role of global oil prices, exchange rate volatility, and the impact of the Russian-Ukrainian war, which started in February 2022 and their effects on food prices. We compiled a panel data set that includes monthly values. Based on the a priori test results, Granger causality test, panel ADRL model, along with Random Effects (RE), linear regression with panel-corrected standard errors (PCSE), cointegration regression using panel fully modified ordinary least squares (FMOLS) regression methods and panel event study estimation were applied. Our result shows that both the increasing Brent oil prices and the fluctuating exchange rates significantly stimulated food price inflation in the EU-27. Data demonstrates that Russia’s war against Ukraine also contributed to increasing food prices in the EU-27. In this regard, we also provide a country-level analysis. Our results contribute to theoretical analyses, as well as the implication for policymakers, and food service companies. [ABSTRACT FROM AUTHOR]

By combining financial and macroeconomic factors with machine learning techniques, this research paper proposes a novel method for forecasting oil price movements in the Kingdom of Saudi Arabia (KSA). Traditional methods generally struggle to capture the complex dynamics and nonlinear linkages in the oil market, which makes accurate oil price forecasting vital for decision-making in numerous sectors. In this paper, we offer a machine learning framework that leverages financial elements like stock market indices, currency rates, and interest rates, as well as macroeconomic data like GDP growth, inflation rates, and energy consumption, as predictors of oil price movements. These factors were chosen because of their significance and importance to the ways in which the oil market in KSA functions. We use several different machine learning techniques to construct the prediction models, some of which are regression-based (such as linear regression or support vector regression) while others are ensemble models (such as random forests or gradient boosting). The models are tested and refined using historical data spanning a sizable period of time and covering a wide range of market circumstances and pricing movements. Evaluation of the prediction models is carried out using conventional metrics like mean-squared error, mean absolute error, and R-squared ( R 2) , and their robustness is evaluated using sensitivity analysis and cross-validation methods. Incorporating financial and macroeconomic variables vastly enhances the accuracy of oil price predictions compared to models based purely on historical price data, as shown by the preliminary findings, which underline the superiority of our approach. The machine learning models exhibit nonlinear pattern capture and responsiveness to market fluctuations. Insights into the relative significance of various variables and their effect on oil price movements in KSA are also provided by the sensitivity analysis. By demonstrating the usefulness of machine learning methods for oil price forecasting, particularly in the context of Saudi Arabia, this research adds to the body of knowledge already available. Our findings have important policy and market implications for oil price forecasting, benefiting policymakers, energy market participants, and investors. To improve the models' ability to forecast the future, researchers may want to consider including more variables in their analyses, such as geopolitical developments and technology advances. [ABSTRACT FROM AUTHOR]

Although oil prices likely influence the trade balance via macroeconomy channels (i.e. exchange rates and income), less widely recognized is the possibility of such an effect in investigating the hypothesis of a J-Curve. Thus, the primary thrust of this paper is to investigate the effect of oil prices on the J-Curve using bilateral trade data between Korea and her 14 largest partners. We uncover that the price of crude oil is indeed important in affecting the Korean trade balance and thus further validity evidence of the J-Curve. We further discover that incorporating exchange rate asymmetry provides more evidence supporting the J-Curve in the Korean trade balance. [ABSTRACT FROM AUTHOR]

The article proves a number of the author's hypotheses about inflation in the United States: unexpected jumps in inflation are required to reduce the budget deficit and public debt to inflated GDP; these jumps and their fading are caused by greater amplitude of commodity prices; the key leading indicator of inflation is the level and plans to increase the federal minimum wage (FMW), as a reflection of the indexation of budget expenditures; there are usually two inflation spikes, with an intermediate fading to lower inflation expectations. These indicators are studied through data analysis since 1946, as well as a formalized logical-statistical model, similar to fundamental and technical analysis of financial markets. The next jump in US consumer prices is projected for 2025–2027 in the level of inflation in 2021–2022, which will require an increase in commodity prices, important for the Russian economy. [ABSTRACT FROM AUTHOR]

This paper examines the single and the joint influence of environmental policy stringency and oil prices on green innovation, admitting the possibility of different magnitudes of the response of innovation depending upon whether oil prices are increasing or decreasing, and accounting for endogeneity of policies. A panel data set of OECD countries is used over the period 1990–2016. Results suggest that increasing the stringency of environmental regulation can, beyond inducing green innovation, shield the effect of oil prices on innovation. In addition, a more stringent environmental policy reduces the asymmetric response of innovation when oil price increases or decreases. Thus, environmental policy and oil prices can be complements when inducing green innovation. Exploiting these complementarities requires an interdependent use of environmental and energy policies, through dynamic adjustments of subsidies and taxes on oil prices alongside reasonable levels of stringency in environmental policy. [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 explores the impacts of the exchange rate, consumer confidence, oil prices on the stock returns of the Borsa Istanbul (BIST) manufacturing firms for the timeline aftermath of the Covid-19 pandemic (March 2020 -- September 2022). As the manufacturing companies comprise the majority of the stock market of the BIST, the composite index of these industrial companies (XUSIN) is selected as the response variable. Implementing the autoregressive distributed lag (ARDL) bounds-testing methodology on the monthly time series data, the cointegration existence is detected among the series. The empirical results also show that oil price is the most significant determinant among these variables affecting manufacturing companies' returns for the long-run. When considering oil as a vital production input in industries, the decreases in stock prices resulting from oil price rises (i.e. increases in production costs) are inevitable. However, the significant long-run effects of exchange rates and the consumer confidence index on stock returns of this industry cannot be detected for the Covid-19 period. [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]

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