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This paper examines the predictability of the changes in Brent oil futures prices using a multilayer perceptron artificial neural network that exploits the information contained in the largest possible set of economic indicators. Feature engineering is employed to identify the most important predictors of the change in Brent oil futures prices. We find that oil‐market‐specific variables are important predictors. Our findings also suggest that forecasts of the change in the Brent oil futures prices from the multilayer perceptron that exploits the informational content of all and oil‐market‐specific predictors exhibit higher statistical forecast accuracy than the random walk. Tests of forecast optimality indicate that the forecasts generated using oil‐market‐specific predictors are optimal. We discuss the policymaking and practical relevance of our results. [ABSTRACT FROM AUTHOR]

Extending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning‐generated forecasts provide better forecasting quality and that portfolios that are constructed with these forecasts outperform their competing models resulting in economic gains. Analyzing the selection process, we show that information channels vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels. [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]

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 paper examines the impact of oil price and quantity shocks on income inequality in the USA. Using micro income data, we construct measures of pre‐tax money income inequality at the state level for the period 1980–2017. We find that a $10 increase in oil price increases inequality, measured by the ratio of pre‐tax income of the 90th percentile to 10th percentile (p90/p10), by 3.8 percentage points in states with high levels of oil endowments per capita. The increase in capital income to households at the top of the distribution drives the observed change in inequality. In addition to price shocks, we study the impact of quantity shocks that arise from oil discoveries. Quantity shocks have no effect on the overall income distribution. [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 study investigated the potential non‐linear impacts of fluctuations in oil prices on domestic prices in Nigeria. Utilising the non‐linear autoregressive distributed lag (NARDL) model, the study revealed the presence of asymmetry in the behaviour of domestic prices. Both forms of oil price shocks were observed to have a negative influence on domestic prices in the short term, with only positive oil price shocks demonstrating statistical significance. However, in the long run, a distinct pattern emerged where a decrease in oil prices significantly affected domestic prices. Additionally, analysis of VAR impulse response showed a consistently negative reaction of domestic prices to oil price shocks, falling below the steady‐state equilibrium. The study concluded that changes in oil prices, in conjunction with other macroeconomic variables, affected the prices of domestic goods in Nigeria, whether in the short or long term. This underscored the complexity of Nigeria's economic landscape. Consequently, it is advisable to prudently invest excess revenue generated from rising oil prices to mitigate the adverse impacts of negative oil shocks on the economy. [ABSTRACT FROM AUTHOR]

We conduct a comparison of three portfolio investment strategies in the US stock market following the implementation of Regulation Fair Disclosure in October 2000. The strategies analyzed are analyst‐recommended, recommendation changes, and momentum portfolios. Across various time periods, company sizes, and industry sectors, the momentum portfolio consistently outperforms the other strategies. Portfolios based on analyst recommendations exhibit poor performance in industries such as consumer staples and materials, which are strongly correlated with oil prices. These industries are susceptible to external demand and supply‐side price shocks that are not adequately captured by analyst recommendations. The findings highlight firstly, the efficacy of the momentum strategy and the limitations of relying solely on analysts' recommendations, particularly in oil‐dependent sectors; and secondly, the varying dynamics and performance of different investment strategies for investors seeking to optimize their investment decisions across different sectors and market conditions. [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]

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All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non‐Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a locally robust semiparametric approach to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non‐Gaussianity when it is present, but yields correct size/coverage for local‐to‐Gaussian densities. Empirically, we revisit two macroeconomic SVAR studies where we document mixed results. For the oil price model of Kilian and Murphy (2012), we find that non‐Gaussianity can robustly identify reasonable confidence sets, whereas for the labor supply–demand model of Baumeister and Hamilton (2015) this is not the case. Moreover, these exercises highlight the importance of using weak identification robust methods to assess estimation uncertainty when using non‐Gaussianity for identification. [ABSTRACT FROM AUTHOR]

We investigate the effects of giant oil discovery and boom‐bust price cycle on manufacturing using a large dataset of up to 49,481 two‐digit industry‐years across 136 countries over the period 1962 to 2012. We find that oil discovery reduces growth in manufacturing value added and wages. The effect on employment is insignificant. We also find strong association between oil discovery and manufacturing slowdown episodes. Oil price boom and bust both negatively affects manufacturing perhaps due to increasing input cost (boom) and declining demand (bust). We do not find any evidence in favour of a real exchange rate appreciation driven effect as outlined in standard Dutch Disease models. We speculate that the effect is primarily driven by an increase in the cost of locally sourced manufacturing input. [ABSTRACT FROM AUTHOR]

This paper examines whether there exists a structural change in the relationship between oil shocks and US industrial production with the rapid development in oil industry due to the shale revolution. Using a structural VAR model, I identify supply and demand shocks in global crude oil markets and estimate changes in their effects on the US industrial production. The key finding is that the US industry output has become more responsive to oil price shocks since the shale oil boom. Oil supply shocks have been more important and heterogeneous effects on the industrial production depending on the US or non‐US oil supply component. There has also emerged the positive effect of oil demand shocks, stimulating aggregate economic activity. In particular, I find that there have been strong positive spillover effects from an oil price increase to other industries not only through direct purchases of inputs for oil production and investment, but also through indirect knowledge and technology transfers created during the shale revolution. These findings have profound policy implications for the fiscal and monetary authority who copes with exogenous oil shocks. [ABSTRACT FROM AUTHOR]

This article investigates whether time variation in the returns' co‐movement of oil and Baltic Dirty Tanker Index can be linked to oil market uncertainty. We measure uncertainty using a battery of different proxies considering both parametric and non‐parametric methods and study its role from both statistical and economic perspectives. Using a regression framework combined with regime switching analysis, we show that oil price uncertainty and the future correlation of oil and dirty‐tanker returns are negatively associated. This negative association is more pronounced in highly volatile periods. The identified regimes are directly linked to high‐low crude oil volatility periods with implications on the level of correlation they exhibit to oil returns. Results are robust across crudes and volatility measures. Additional robustness checks corroborate that results hold for individual dirty‐tanker routes and clean‐tanker cargoes. [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]

Using the Morlet Wavelet approach and other time‐frequency decomposition techniques, this study investigates the dynamic relationship between energy prices, economic growth, and ecological footprint in Malaysia from 1975 to 2021. Our analysis reveals several key findings. Firstly, despite the absence of oil price volatility, the size of Malaysia's ecological footprint varies significantly over the study period. Secondly, energy prices contribute significantly to the ecological footprint. Thirdly, the association between energy prices and ecological footprint is stronger during periods of economic growth. Lastly, energy prices contribute to economic growth and inflation in the short run, but after factoring in the ecological footprint, the association extends into the long run. These findings suggest that Malaysia's economic growth is driven not only by energy prices but also by its ecological footprint. Moreover, the size of Malaysia's ecological footprint is driven by factors at the microeconomic level rather than energy prices alone. Therefore, the impact of energy prices on the macroeconomy, especially when considering the impact on the environment, is non‐linear. [ABSTRACT FROM AUTHOR]

The considerable influence of crude oil prices on the international economy has motivated numerous scholars to develop various prediction models. Two difficulties are encountered in forecasting. One is that the time series of crude oil prices show massive jumps in high frequency. The other is that the time series of crude oil prices are characterised by nonlinearity, structural breaks and highly volatile states. Therefore, we propose a hybrid model that incorporates the principle of decomposition–integration. Its overall process can be divided into the steps of decomposition and integration. In decomposition step, modified ensemble empirical mode decomposition is used to decompose the data on crude oil prices. Next, K‐means and principal component analysis are utilised to extract the optimal intrinsic mode function (IMF) from diverse frequencies amongst decomposed IMFs. Following decomposition, different forecasting methods are used to match IMFs with various frequencies for prediction. In accordance with structural breaks and the highly volatile states of high‐frequency series, hidden Markov regression is applied to describe predicted values in a probabilistic manner, providing objective interpretation and forecasting. A neural network is taken to predict intermediate‐frequency IMFs by using a fully connected network. Owing to the low frequency and tendency of the residual sequence to be more stable than other frequency sequences, the autoregressive integrated moving average model is applied as the forecasting method for prediction. Finally, all individual predictions for the eventual results are ensembled as final predictions. Results confirm that the proposed model is better than several normal hybrid and single models in terms of prediction accuracy and Diebold–Mariano test results. All results confirm that the newly proposed hybrid model is a promising tool for the analysis and forecasting of crude oil prices. [ABSTRACT FROM AUTHOR]

The article explores the non‐linear relationship between oil prices, inflation, and GDP in eight African countries that import oil from other nations. The study uses various econometric techniques, including symmetric and asymmetric dynamic panel ARDL models, mean group, and pooled mean group approaches, to examine quarterly data from 1983 Q2 to 2020 Q4. The analysis looks at both short‐term and long‐term variations to measure the positive and negative effects of oil prices and inflation on GDP. The findings reveal that the variables are related, but there are significant non‐linearities in the long run. While both rising oil prices and inflation have a positive impact on GDP in most instances, lower oil prices and inflation might have a neutral or negative impact. [ABSTRACT FROM AUTHOR]

This study examines the nexus between oil prices (Oil) and economic output (IIP) asymmetrically in the Indian context using quarterly data from 1996–1997:Q1 to 2019–2020:Q4. To this end, we have used a nonlinear autoregressive distributed lag (NARDL) model. The main findings of the study are as follows—firstly, the Wald test confirms the nonlinearities among the negative and positive shocks in Oil, EX and IR in the long run. Secondly, the NARDL model reveals that negative shock in oil price stimulate the IIP, whereas positive shock in Oil fails to influence IIP. The positive shock in EX strengthens the Indian economy by having a positive and significant impact on IIP, while the negative shock in EX is insignificant. The positive and negative shocks in IR negatively impact IIP in the long run. The study demonstrates that Oil, EX and IR exert asymmetric pressure on IIP in the long run. Concisely, India's economic output is more responsive to oil prices decrease than oil prices increase. Therefore, the asymmetric oil prices‐economic output nexus has substantial policy implications, particularly for prudent energy policy in India. [ABSTRACT FROM AUTHOR]

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]

BACKGROUND: The development of processes for transforming vegetable oils into useful products has received increasing attention in the biotechnological field. The reactor used for this purpose should meet adequate mass transfer, mixing, and oil dispersion conditions. In this work, comprehensive research into the use of a jet‐loop reactor as a potential alternative to airlift and stirred tank reactors was conducted in the presence of avocado oil in a water‐air system. Additionally, the assessment of the jet‐loop for biomass growth in the presence of oil was performed. RESULTS: The increase of avocado oil from 1% to 5% v/v resulted in a reduction in the performance of the three reactors. Despite the airlift reactor having the lowest power input, it exhibited the worst performance in terms of mass transfer, mixing, and oil dispersion. The estimation of the energetic oxygen transfer efficiency (Kg O2 KW−1 h−1) allowed the determination that the jet‐loop reactor was superior to the stirred tank. This superiority was attributed to the low values of the mixing time and oil droplet diameter observed in the jet‐loop reactor. Finally, all these parameters had a positive impact on the growth of Wautersia eutropha in the jet‐loop reactor compared with the other two reactors. CONCLUSION: The results showed that the jet‐loop reactor presented superior performance in most cases compared to the stirred tank and airlift reactors. Particularly, the jet‐loop exhibits an interesting potential to reduce the size of the avocado oil droplets, which could lead to a significative increase in the growth rate of Wautersia eutropha. © 2023 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

We provide new evidence on the response of investment to uncertainty, using granular and high‐frequency (weekly) data on domestic oil drilling and oil prices since 2012, corresponding to the period of widespread horizontal drilling and hydraulic fracturing in the United States. Weekly data permits much weaker identifying restrictions than is required with monthly data that is common in the literature. We measure domestic drilling activity by the number of rigs drilling for oil, and we measure oil uncertainty by implied volatility from options on oil futures and the return on delta‐neutral straddles from options on oil futures. We show that the number of oil drilling rigs are tightly linked to both oil prices and oil uncertainty, and we find that oil uncertainty significantly decreases the number of drilling rigs, with a one standard deviation increase in uncertainty reducing the number of drilling rigs by up to 5%. [ABSTRACT FROM AUTHOR]

This study examines the impact of crude oil and fertiliser price changes on maize crop prices in India using monthly time series data from May 2007 to September 2022. The best‐fitted Autoregressive Integrated Moving Average model with the lowest Akaike's Information Criterion value is selected, and the Box–Ljung test is used to validate the prediction accuracy. Empirical results suggest that maize price is driven by crude oil and fertiliser prices since it has been found that maize prices are highly positively correlated with the prices of crude oil and fertiliser. Also, a strong correlation has been found between crude oil and fertiliser prices. The investigation for forecasting the next 15 months from November 2022 also revealed that maize prices showed no volatility because of a constant trend, but crude oil prices showed a declining trend, while di‐ammonium phosphate prices showed an increasing trend over the period from November 2022 to February 2023. They then declined to June 2023 but again showed an increasing trend from July 2023 to January 2024 and achieved the highest price in December 2023. Because of a constant trend, urea prices showed no volatility over the 15 months. The estimates can aid the government in formulating policies to maintain agricultural crop production and control input price changes to meet the growing population's food demands. [ABSTRACT FROM AUTHOR]

Brazil's significant commodity production is internationally recognized, yet the absence of a mature futures market exposes it to price risks and international shocks. This study explores the dynamic connectedness between commodity futures and the Brazilian cash markets, using a time‐varying parameter vector autoregressive model. We also assess COVID‐19's impact on this connectedness. We find a significant influence of oil prices on Brazilian ethanol prices, and particularly emphasize the Heating Oil spillover effect on ethanol in the post‐COVID‐19 era. We also note the ascension of Brazilian soybean spot markets' international significance since 2017, amplifying their role in global grain price discovery. Finally, by computing hedge ratios and effectiveness between commodity futures contracts and Brazilian spot prices, our study reveals soybean cash price as the most effective hedge. These insights deepen comprehension of connectedness within Brazilian commodity markets, thereby guiding investors and policymakers in strategic energy policy decisions. [ABSTRACT FROM AUTHOR]

According to the article "Feature Article: Three reasons why inflation will quickly return to target," the UK inflation outlook has changed due to decreases in oil and gas prices and a softening in core pressures. The forecast for CPI inflation in 2024 has been revised down to 2.1% from the previous forecast of 2.9%. Wholesale gas prices and the Ofgem energy price cap are expected to fall, which will further reduce inflation. Services inflation has been lower than expected, and the link between wages and prices is weakening. The Bank of England is likely to lower its inflation projections and may consider rate cuts. The recent decline in oil and gas prices has contributed to the change in the inflation outlook. Lower oil prices and the expected fall in the Ofgem price cap will reduce inflation. The article suggests that inflation will return to the 2% target in April, which is earlier than the Bank of England's forecast. The softer inflation outlook has implications for interest rate cuts and economic growth. Lower inflation is expected to lead to a stronger pickup in activity and an increase in real incomes. Consumer spending growth and GDP growth forecasts have been revised upwards. [Extracted from the article]

Summary: This paper proposes an identification strategy for international oil price shocks while accounting for the heterogeneous sources of oil demand from global, developed, and emerging economies. Unlike existing works, we isolate global oil demand shocks, associated with a global real economic activity factor, from oil demand shocks originating specifically from developed and emerging economies, associated with real economic activity factors within these two groups of economies. The paper uses a structural factor‐augmented vector autoregression (FAVAR) model with latent global and specific factors to model crude oil demand and supply. To identify the shocks, we extract real economic activity factors from a large panel of emerging and developed economies' real activity variables using a two‐level factor model. The paper shows how structural shocks can be identified by solving equations that arise from economically meaningful zero restrictions on the impact matrix of the reduced‐form FAVAR model innovations. The empirical application shows that identifying the international oil demand shocks based on the global and specific latent factors is essential to appropriately quantify their heterogeneous impacts on these factors, the crude oil production, and the real oil price. [ABSTRACT FROM AUTHOR]

Using the exogenous event of oil price sharp decline in 2014–2015, this paper employs the difference‐in‐difference method to establish a causal link between the oil price decline and the Chinese firms' labour investment. Data of listed companies in China from 2012 to 2016 are used to explore this relationship. We show that the employment for firms in industries with significant negative oil price risk exposure increases 16.4% after the oil price plummeted, that is, the oil price decline significantly promotes the firms' labour force. Additionally, the positive effect of oil price decline on the firms' labour force is more pronounced in firms with higher risk‐taking, financing constraints, and industry competition. Lastly, we also document that the effect of oil price decline is through sales growth channels to increase labour demand. However, firms tend to overinvest in labour after the oil price plummeted. Based on these findings we suggest that oil price fluctuation should be an important factor for the Chinese government and enterprises when they make an economic decision related to the labour force. [ABSTRACT FROM AUTHOR]

As the largest oil consumer and sixth largest oil producer in the world, China's growing oil import is a premise to its energy security concerns. For China, petroleum security means supply security. The paper disaggregates petroleum security into three dimensions (domestic petroleum price—DOP, petroleum supply—PS and petroleum reserve—PR) and examines the impact of Renminbi internationalization (RMBI) on these dimensions based on linear auto regressive distributed lag (ARDL) procedure, and monthly data spanning from 2011M01 to 2022M03. The empirical results reveal evidence of long run equilibrium relationship between PR, PS and RMBI. On the long run and short run dynamics, RMBI is inversely related to DOP both in the short run and long run, but the impact is less than proportionately. However, RMBI and PS co‐move in the short run with 0.24% margin. In the same vein, RMBI and PR co‐move both in the short run and long run significantly. The impact of international oil price is positive on DOP but negative on PS and PR at least in the short run. Our finding signalled to the fact that increasing acceptance of the RMB in trade settlements and crude oil invoicing serve as a comparative advantage for China to engage in oil trade and complement the oil demand growth threatening the country's energy security drive. Therefore, we suggest diversification of crude oil import sources especially to regions/countries where RMB has been widely accepted. Prominence should be given to government controlled Strategic Petroleum Reserves (SPRs) and large oil companies should be compelled to raise the level of mandatory oil reserve for sustainable supply that keep pace with the growing demand for oil. [ABSTRACT FROM AUTHOR]

We examine the directional predictability of energy stock returns on exchange rates and stock market in the E7 + 1 emerging market economies, which include India, China, Indonesia, South Korea, Turkey, Brazil, Mexico, and Russia, over the period 4 January 2000 to 31 May 2018. To achieve this, we carried out a cross‐quantile analysis in the static and dynamic frameworks, using the bi‐variate cross‐quantilogram (CQ) and the partial cross‐quantilogram (PCQ) approaches and a dynamic variant of such approaches. The predictability of the stock returns on the energy prices for WTI, Brent, OPEC, heating oil and natural gas is examined. Further relationships are also conditioned by using two measures of geopolitical risk, including the general geopolitical risk (GPRD), and the geopolitical risk threats (GPRD_Threat). The overall results highlight the importance of employing the PCQ approach in examining the predictability of different pairs of the energy prices, exchange rates and stock markets. They also indicate that controlling for GPRD and GPRD_Threat significantly improves the predictability of these variables. Policy implications of the empirical findings have been elaborated and discussed. [ABSTRACT FROM AUTHOR]

BACKGROUND: Owing to the continuous growth of petroleum prices over the last few decades, there has been a renaissance in interest in the large‐scale manufacturing of platform chemicals via the fermentation route. The present work emphasizes the extraction of itaconic acid from an aqueous solution by dissolving reactive tri‐n‐butyl phosphate (TBP) in natural, non‐toxic diluents such as sunflower oil, rice bran oil, and sesame oil. RESULTS: The experimental results were explained in terms of distribution coefficient (Ɗ), extraction efficiency (η%), equilibrium complexation constant (Ke), and loading ratio (ϕ). At the highest concentration of itaconic acid 0.250 mol L−1 and TBP 1.099 mol L−1, the maximum extraction efficiency and distribution coefficient for sunflower oil were obtained, at 88.72% and 7.94%, respectively. The diffusivity (DIA−S) of itaconic acid towards the interface of organic and aqueous phases was obtained as 1.11 × 10−6 cm2 s−1 with the Wilke–Chang equation and 4.40 × 10−7 cm2 s−1 using the Reddy–Doraiswamy relation. The lowest value of solvent‐to‐feed (S/F) proportion was estimated as 6.66, with the minimum number of stages required for counter‐current extraction being ~4. CONCLUSION: In reactive extraction of itaconic acid with TBP, the highest extraction efficiency and distribution coefficient was attained with sunflower oil, followed by rice bran oil and sesame oil. Higher concentrations of itaconic acid (0.250 mol L−1) and TBP (1.099 mol L−1) are suitable combinations for all the natural diluents. Diffusivity values revealed the extent of diffusion of itaconic acid in the solvent phase and strongly depended on the density and viscosity of the solvent. The experimental results could be utilized to design a continuous extraction column. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Flotation of fine coal particles usually uses oil collectors, but the micromechanisms need to be more defined due to the complex structure and abundant functional groups on the surface of coal bodies [Correction added after first online publication on 16 October 2023: "refined" was changed to "defined" in the first sentence of the article abstract.]. The adsorption spreading behavior and interfacial properties of nonpolar oil drops on flat low‐order coal (LOC) and high‐order coal (HOC) surfaces were investigated in depth using molecular dynamics (MD) simulations, while the effects of different functional groups on LOC surfaces were also considered. The results showed that the contact angle, contact area and interaction energy of oil drops adsorbed on the LOC and HOC surfaces at simulated equilibrium in aqueous environment were 77.68°, 621.49 Å2, −140.94 kcal/mol; 53.98°, 962.14 Å2, and −195.13 kcal/mol, respectively. The smaller the equilibrium contact angle between the oil drops and the surface, the larger the contact area and the larger the absolute value of the interaction energy, the better the spreading effect of the oil drops and the easier the surface is to flotation. Compared with the HOC surface, the oil drops could not displace the water molecules on the LOC surface better and spread poorly on its surface, and the migration rate was higher. This was caused by the abundant functional groups on the surface of LOC. The type of functional group significantly affects the interaction of nonpolar oil drops with hydrophilic surfaces, with the order of adsorption strength being CH3 > COCH3 > OH > COOH. The formation of a dense hydrated film of oil drops on the COOH surface was an important reason for the difficulty of flotation on the LOC surface. MD elucidated the mechanism of action of nonpolar oil collectors on LOC and HOC surfaces, which is a guide for efficient flotation on LOC surfaces. [ABSTRACT FROM AUTHOR]

While energy risk is increasingly recognized as a systemic risk, there is limited comprehensive analysis of the risk propagation in regional contexts. In this study, we examine oil and natural gas price changes and shocks in relation to equity market returns and volatility for 24 European Economic Area (EEA) countries. In addition to traditional panel regressions, we also deploy the Diebold‐Yilmaz (2014) spillover index for a closed network analysis. We differentiate in the cross‐section across the core EU block, PIIGS countries, EU enlargement countries joining after 2004, and other non‐EU countries, to provide insights into the ongoing debates on the European energy market stability. While we find evidence of the manifestation of energy risk throughout the sample period, we find that until 2019 the primary sources of volatility spillover in the EEA economic network arose from economic or political uncertainty. Energy risks, measured by large crude oil and natural gas price shocks also significantly contributed to equity market volatility, with increasing volatility risk arising from natural gas, a green labelled energy source after 2019. Last, we show that CEEC equity markets are more sensitive to oil and natural gas price shocks when domestic currencies depreciate against the Euro. [ABSTRACT FROM AUTHOR]

The study's objective was to investigate how changes in oil prices affect stock market performance. The study's population consisted of selected African countries chosen based on factors like the size of their stock markets and whether or not they import or export oil. The world development indicator (WDI) and the OPEC bulletin or website served as sources for the data. The vector error correction model was used to analyse the collected data. Regarding the impact of oil price fluctuations on the performance of the stock market, the findings of the study were mixed. Oil price fluctuations have a positive impact on stock market performance for countries that import oil. For oil‐trading countries, the impacts were found to differ across examined countries. Oil price fluctuations had a mixed effect on both large and small stock markets. This demonstrates how crucial it is to investigate changes in the price of oil and how they might impact stock market performance, particularly in African countries. [ABSTRACT FROM AUTHOR]

The current study investigates the impact of real oil price shocks on the Eurozone banking sector from February 2009 to February 2022, using the Structural Vector Autoregressive method to distinguish the reaction between Eurozone bank indices and three structural indicators in the oil market shocks. The shocks include global oil demand, supply shock, and specific oil market shocks. Our findings show that the banking sector in the Eurozone responds positively toward an oil‐specific demand shock (oil price) in the short run, but it responds negatively in the medium and long run that lasts for a long time. The banking sector, on the contrary, does not react to supply shocks, which provide a significant policy implication for regulators and policymakers. [ABSTRACT FROM AUTHOR]

This study employs the quantile‐on‐quantile method, casualty‐in‐quantiles method, and rolling window regression to investigate the impact of international crude oil future prices on the stock prices of both traditional and new energy sectors in China. The empirical results reveal that the effect of oil future prices on the energy stock market in China varies across quantiles and is easily affected by extreme events. Specifically, the impact of oil future prices on the new energy stock market is significant and volatile, while it is less volatile and displays a negative correlation with the traditional energy market. Furthermore, a concentrated positive correlation is observed in the middle and low quantile stages of the energy stock market. A significant Granger causality exists between oil future prices and the energy stock market in different quantiles. Those findings can provide useful guidance for policymakers, investors, and consumers. [ABSTRACT FROM AUTHOR]

Electrocoalescence has long been known for the separation of a water‐in‐oil emulsion. An associated challenge with electrocoalescers is the undesired noncoalescence and consequently chain formation of aqueous phase droplets. This leads to low separation efficiency and damage of electrical equipment. Recently Hasib et al. proposed an electric field modulated scheme that showed significant improvement in dehydration of water‐in‐oil emulsions. They investigated the range of modulation parameters when the scheme is most effective. The fundamental process in electrostatic dehydration of an emulsion is the interaction between a pair of water droplets. In the present study, two suspended aqueous drops in insulated oil experiments are compared for their behavior under unmodulated and modulated electric fields. Further, a model is developed and the experimental behavior under unmodulated and modulated electric fields is compared with numerical solutions. The model predicts the experimental observations accurately by balancing electrostatic, electrophoresis, dipolar, and resisting viscous drag forces, ignoring the end (bridge) effect during the contact. The study shows that the increase in the rate of dehydration of a water‐in‐oil emulsion under modulated electric fields with an increase in duty ratio and its near independence on the modulation time period can be explained by the two‐drop studies. However, several other processes such as multidrop interactions as well as scavenging of fine droplets by charged droplets created as intermediates in the interaction of two droplets cannot be explained by the two‐drop studies. [ABSTRACT FROM AUTHOR]

As the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making. [ABSTRACT FROM AUTHOR]

When quantifying the importance of supply and demand for oil price fluctuations, a wide range of estimates have been reported. Models identified via a sharp upper bound on the short‐run price elasticity of supply find supply shocks to be minor drivers. In turn, when replacing the upper bound with a weakly informative prior, supply shocks turn out to be substantially more important. In this paper, I revisit the evidence in a model that combines weakly informative priors with identification by non‐Gaussianity. For this purpose, a SVAR is developed where the unknown distributions of the structural shocks are modeled nonparametrically. The empirical findings suggest that once identification by non‐Gaussianity is incorporated into the model, posterior mass of the short‐run oil supply elasticity shifts toward zero and oil supply shocks become minor drivers of oil prices. In terms of contributions to the forecast error variance of oil prices, the model arrives at median estimates of just 6% over a 16‐month horizon. [ABSTRACT FROM AUTHOR]

This paper uses the default return spread (DFR) to predict crude oil price returns over the period January 1986 through December 2020. Results of insample and out-of-sample analyses show that the DFR can predict oil price returns and significantly outperform the benchmark and other competing variables. In an asset allocation exercise, a mean-variance investor can obtain considerable certainty equivalent return (CER) gains based on the return forecasts of DFR relative to the benchmark. We also perform a series of robustness tests to confirm our previous conclusion. We further investigate the source of the DFR's predictive ability from oil market sentiment, in which we provide some theoretical basis to explain. [ABSTRACT FROM AUTHOR]

This study investigates the effects of the crude oil implied volatility index (OVX) upon emerging market financial stress (EMFS). We resort to a quantile regression framework as this approach is a better alternative to disentangle the relationship under different market conditions. Besides, we also examine how EMFS responds to the lags and asymmetries in the OVX. The empirical results show significantly positive impacts of OVX upon EMFS. Further, the effects of OVX become more assertive in the upper quantiles of EMFS, implying higher sensitivity to OVX when stress levels are high. In terms of the lagged effects, the relationship is transient as the OVX coefficients become weaker with increasing lag sizes. We further find that only positive impulses in OVX can significantly predict EMFS. Lastly, we report evidence that the Credit market stress is a crucial driver of EMFS. [ABSTRACT FROM AUTHOR]

This paper empirically explores the impact of oil price uncertainty on the strategic investment of China's renewable energy enterprises from the perspective of certain substitution relationships between renewable energy enterprises and traditional energy enterprises by using the systematic GMM method. In addition, we propose three specific influence mechanisms for the renewable energy industry. The results of our mechanism analysis show that the uncertainty of oil price will increase the market's expectation of renewable energy enterprises and thus stimulate their investment. While the uncertainty of oil price will restrain the investment expenditure of renewable energy enterprises through risk transmission. The scale of enterprises will affect the above two influencing mechanisms. However, the uncertainty of oil price can hardly influence the strategic investment decision through the stimulation of the renewable energy industry by the consumer market. Our results are helpful to the strategy makers of renewable energy enterprises and the macro policy‐makers of the government. [ABSTRACT FROM AUTHOR]

This paper examines the impact of a rise in oil prices on the wages of workers in the unorganized sector of a developing economy. The model economy is comprised of two non‐traded transport sectors, formal and informal, along with other sectors. The main results that we obtain are as follows. The informal transport sector contracts when fuel price rises and lowers the real income of the informal workers. The per‐unit return to land rises, and the factor readjustments even raise the output of other sectors in the economy. We also show why inclusion of non‐passenger transport services does not alter the main outcomes of the model. [ABSTRACT FROM AUTHOR]

According to the article "Global: Global outlook boosted by US GDP upgrade," there is evidence of a soft landing in the US, which is expected to lead to central banks around the world cutting interest rates in the first half of 2024. However, global GDP growth is still expected to be subdued, with a forecasted growth rate of 2.3% for 2024. The US is driving this revision, with an upgraded forecast for US GDP growth to 2% from 1.2%. Inflation forecasts have been revised downward, with an expected average world CPI inflation of 4.1% for this year. The article also discusses the potential for policy rate reductions by the US Federal Reserve and other major advanced economy central banks. [Extracted from the article]