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This study investigates the relationship between consumer sentiment (CONS), inflation expectations (INEX) and international energy prices, drawing on principles from behavioral. We focus on Brent crude oil price and Henry Hub natural gas prices as key indicators of energy market dynamics. Based on the monthly data from January 2003 to March 2023, three wavelet methods are applied to examine the time-frequency linkage, while the nonlinear distributed lag model (NARDL) is used to verify the asymmetric impact of two factors on energy prices. The results highlight a substantial connection between consumer sentiment, inflation expectations and international energy prices, with the former in the short term and the latter in the medium to long term. Especially, these correlations are particularly pronounced during the financial crisis and global health emergencies, such as the COVID-19 epidemic. Furthermore, we detect short-term asymmetric effects of consumer sentiment and inflation expectations on Brent crude oil price, with the negative shocks dominating. The positive effects of these factors on oil prices contribute to observed long-term asymmetry. In contrast, inflation expectations have short-term and long-run asymmetric effects on natural gas price, and both are dominated by reverse shocks, while the impact of consumer sentiment on natural gas prices appears to be less asymmetric. This study could enrich current theories on the interaction between the international energy market and serve as a supplement to current literature. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic, multi-scale relationship between sentiment related to the COVID-19 pandemic and extreme returns in crude oil. The recently developed COVID-19 indices are employed to gauge pandemic sentiment. Utilizing daily data spanning from January 2020 to December 2021, Granger's linear and nonlinear causality tests reveal that indices nonlinearly influence extreme fluctuations in West Texas Intermediate and Brent crude oil prices. Interestingly, a reciprocal causation is also identified: extreme crude oil returns significantly affect the indices. Furthermore, the wavelet transform coherence analysis sheds light on the indices' ability to predict extreme crude oil price volatility across specific time-frequency domains, displaying diverse distributions and lead-lag patterns among the sub-indices. Our study underscores the efficacy of indices in anticipating extreme fluctuations in crude oil values during the COVID-19 pandemic, carrying important implications for investors, scholars, and policymakers. [ABSTRACT FROM AUTHOR]

Crude oil is an essential energy source that affects international trade, transportation, and manufacturing, highlighting its importance to the economy. Its future price prediction affects consumer prices and the energy markets, and it shapes the development of sustainable energy. It is essential for financial planning, economic stability, and investment decisions. However, reaching a reliable future prediction is an open issue because of its high volatility. Furthermore, many state-of-the-art methods utilize signal decomposition techniques, which can lead to increased prediction time. In this paper, a model called K-means-dense-sparse-dense long short-term memory (K-means-DSD-LSTM) is proposed, which has three main training phrases for crude oil price forecasting. In the first phase, the DSD-LSTM model is trained. Afterwards, the training part of the data is clustered using the K-means algorithm. Finally, a copy of the trained DSD-LSTM model is fine-tuned for each obtained cluster. It helps the models predict that cluster better while they are generalizing the whole dataset quite well, which diminishes overfitting. The proposed model is evaluated on two famous crude oil benchmarks: West Texas Intermediate (WTI) and Brent. Empirical evaluations demonstrated the superiority of the DSD-LSTM model over the K-means-LSTM model. Furthermore, the K-means-DSD-LSTM model exhibited even stronger performance. Notably, the proposed method yielded promising results across diverse datasets, achieving competitive performance in comparison to existing methods, even without employing signal decomposition techniques. [ABSTRACT FROM AUTHOR]

The impact of climate risk on economic and financial fields has attracted considerable interest from academics and practitioners. Recent literature documents that climate risk can affect oil volatility through the dual channel of economic activities and financial markets. Inspired by this, this paper uses climate policy uncertainty (CPU) to examine the predictability of crude oil price volatility. The empirical results indicate that changes in the CPU, rather than the original CPU, can forecast oil volatility. The revealed volatility predictability is both in-sample and out-of-sample. Asset allocation exercise shows that this finding is also economically significant. Furthermore, the predictive ability is mainly concentrated during economic expansions and the CPU contains climate risk that is not available for other predictors. Finally, CPU change indicators can explain the changes in crude oil consumption, which is the economic source of their predictive power. [ABSTRACT FROM AUTHOR]

The past decade witnessed a surge in anti-corruption activities in post-Soviet oil-dependent countries. Despite the scale of the anti-corruption measures, and their political implications, the extant literature paid limited attention to the comparison of these campaigns, including differences in their motivations, functions, objectives, and targets. Aiming to fill this research gap, this study employs a mixed-methods research design that combines fixed-effects panel data analysis with a paired-case qualitative analysis of Russia and Kazakhstan. I argue that anti-corruption policies are the result of either economic necessity or the desire to secure power through buttressing regime legitimacy. Consequently, a significant drop in oil price due to the COVID-19 crisis is likely to result in a major anti-corruption campaign targeting high-level officials in oil-rich countries. [ABSTRACT FROM AUTHOR]

The subject of predicting global crude oil prices is well recognized in academic circles. The notion of hybrid modeling suggests that the integration of several methodologies has the potential to optimize advantages while reducing limitations. Consequently, hybrid techniques are extensively used in contemporary research. In this paper, a novel decompose-ensemble prediction approach is proposed by integrating various optimization algorithms, namely biography-based optimization (BBO), backtracking search algorithm (BSA), teaching-learning-based algorithm (TLBO), cuckoo optimization algorithm (COA), multi-verse optimization (MVO), and multilayer perceptron (MLP). Furthermore, the aforementioned approaches, namely BBO-MLP, BSA-MLP, and TLBO-MLP, include the de-compose-ensemble technique into the individual artificial intelligence model in order to enhance the accuracy of predictions. In order to validate the findings, the forecast is conducted using the authoritative data on oil prices. This study will use three primary indicators, including EMA 20, EMA 60, EMA 100, ROC, and AUC assessments, to assess and evaluate the efficacy of the five methodologies under investigation. The below findings are derived from the conducted research: Based on the achieved AUC values of 0.9567 and 0.9429, it can be concluded that using a multi-verse optimization technique is considered the most suitable strategy for effectively handling the dataset pertaining to crude oil revenue. The next four approaches likewise have a significant AUC value, surpassing 0.8. The AUC values for the BBO- MLP, BSA-MLP, TLBO-MLP, and COA-MLP approaches were obtained as follows: (0.874 and 0.792) for training and testing stages, (0.809 and 0.792) for training and testing stages, (0.9353 and 0.9237) for training and testing stages, and (0.9092 and 0.8927) for training and testing stages, respectively. This model has the potential to contribute to the resolution of default probability and is very valuable to the credit card industry. Broadly speaking, this novel forecasting approach serves as a notable predictor of crude oil prices. [ABSTRACT FROM AUTHOR]

This study investigates the extent and persistence of major crisis events in the crude oil market and economy and searches for general rules of event impact. Although the short-term effects of such crises may quickly become evident, their long-term implications can be challenging to uncover. To this end, we analyzed 50 major crisis events across four categories using a Proxy structural vector autoregressive (Proxy-SVAR) model. The results show that these events substantially impacted crude oil production, world crude oil prices, and inflation in China and the United States. Generally speaking, major crisis events have a more severe impact on the crude oil market, but their impact on the economy lasts longer. Notably, geopolitical conflicts have led to a sharp decline in S&P500, resulting in a simultaneous reduction in the industrial production indices of the world, China, and the US. Among the different types of crises, the financial crisis had the most prolonged impact, persisting for approximately four years. The public health emergency represented by COVID-19 resulted in a decline in actual oil prices, with its impact on the oil market continuing for over three years. Considering our findings, we suggest four policy measures to enhance economic resilience. [ABSTRACT FROM AUTHOR]

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

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]

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data. [ABSTRACT FROM AUTHOR]

Copyright of Revista Espacios is the property of Talleres de Impresos Oma 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.)

Crude oil is a vital energy source for industrialised and developing countries. Both investors who trade crude oil derivatives and firms that use oil as raw material for production monitor oil supply and demand. Disruption in the flow of oil in the commodity markets, therefore, leads to oil price volatility that affects major economies worldwide. Often, geopolitical and natural conditions could adversely impact the oil price and the financial markets react to such fluctuations in oil prices. This paper analyses the effects of oil price changes on sectorial stock returns in India. Using daily returns of S&P BSE Sensex, S&P BSE Oil & Gas and S&P BSE-Auto sectoral indices from January 2000 to March 2020, we examined the impact of Brent crude oil price fluctuations on the two major sectorial stock indices and overall market index. The results of the Granger causality test show significant results on S&P BSE-Sensex and S&P BSE-Oil and Gas Index Returns except S&P BSE-Auto Index Returns. Further for contemporaneous association between the oil price changes and sectorial returns, we operationalised the simple regression analysis. The results from the regression analysis show statistically significant impact of crude oil price fluctuations on the sectorial returns. [ABSTRACT FROM AUTHOR]

Addressing the interconnectedness of oil prices and foreign exchange rates poses a substantial challenge and raises significant questions within economic research. Existing studies reveal a fragmented understanding of the dynamics between these crucial variables, highlighting a need for deeper investigation. This study extends previous research by employing the time-varying parameter VAR extended joint connectedness model and using the baseline approach called the generalized connectedness approach. Our research findings indicate that exchange rates and crude oil prices exhibit pairwise and total connectedness, varying over time. Furthermore, this connectedness experiences substantial increases during severe crisis periods. We uncover that exchange rates and crude oil prices not only share a dynamic, pairwise, and total connectedness but these results are stronger for oil exporting countries. However, these methods yield inconsistent results for various countries. Moreover, there is compelling evidence that fluctuations in exchange rates and oil prices can cause bi-directional and time-dependent shocks. [ABSTRACT FROM AUTHOR]

Unprecedented increases in European natural gas prices observed between late 2021 and mid 2022 raise a question about the sources of these events. In this article we investigate this topic using a time-varying parameters structural vector autoregressive model for crude oil, US and European natural gas prices. This flexible framework allows us to measure how disturbances specific to the analyzed markets propagate within the system and how this propagation mechanism evolves in time. Our findings are fourfold. First, we show that oil prices are hardly affected by shocks specific to natural gas markets, whether in the US or Europe. Second, we demonstrate that oil shocks have limited impact on US natural gas prices, which points to the decoupling of both markets. Third, we evidence that over longer horizons natural gas prices in Europe are still mostly determined by oil shocks, with idiosyncratic disturbances leading to short-lived decoupling of both commodity prices. Fourth, we illustrate that along the gradual shift from oil price indexation to gas-on-gas competition, the contribution of idiosyncratic shocks to European natural gas prices has increased. Nonetheless, we discuss why the notion that EU natural gas and crude oil prices have decoupled might be premature. [ABSTRACT FROM AUTHOR]

Although the recent debate in energy economics on the importance of oil price indexation versus shale gases suggest that big data can be used in predictive analysis in energy economics, little is known particularly in the context of shale gas and oil price interlinkages. Grounding our investigations in such directions we investigate in this paper the relationship between gas and crude oil markets and the impact US shale gas by employing time-varying causality method by Shi et al. (J Time Ser Anal 39(6):966–987, 2018; J Financ Econom 18(1):158–180, 2020) and cross-quantilogram correlation approach by Han et al. (J Econom 193(1):251–270, 2016). In particular, as a representative of the crude oil market, we use OPEC oil; WTI; Crude oil Oman; Crude oil Dubai while for the gas market, we use natural gas prices of UK NBP (National Balancing Point), NYMEX HH (Henry Hub) and US shale gas prices. Data period is from 11th January 2013 to 8th September 2020. We find significant negative spillovers from crude oil markets to natural gas markets particularly during moderate market conditions. The results suggest crucial implications in energy economics literature, to diversify assets to hedge against risks. We further find strong causality association between oil markets, natural gas markets and further oil markets and shale gas markets. Our findings describe that aftermath of the shale-gas boom the predictability nexus between oil and natural gas increased. Once we condition for shale gas the significant negative spill overs from oil markets to natural gas markets increases in the long-run. We suggest important policy prescriptions which have interconnected market repercussions. [ABSTRACT FROM AUTHOR]

Over the years, oil prices and financial stock markets have always had a complex relationship. This paper analyzes the interactions and co-movements between the oil market (WTI crude oil) and two major stock markets in Europe and the US (the Euro Stoxx 50 and the SP500) for the period from 1990 to 2023. For that, I use both the time-varying and the Markov copula models. The latter one represents an extension of the former one, where the constant term of the dynamic dependence parameter is driven by a hidden two-state first-order Markov chain. It is also called the dynamic regime-switching (RS) copula model. To estimate the model, I use the inference function for margins (IFM) method together with Kim's filter for the Markov switching process. The marginals of the returns are modeled by the GARCH and GAS models. Empirical results show that the RS copula model seems adequate to measure and evaluate the time-varying and non-linear dependence structure. Two persistent regimes of high and low dependency have been detected. There was a jump in the co-movements of both pairs during high regimes associated with instability and crises. In addition, the extreme dependence between crude oil and US/European stock markets is time-varying but also asymmetric, as indicated by the SJC copula. The correlation in the lower tail is higher than that in the upper. Hence, oil and stock returns are more closely joined and tend to co-move more closely together in bullish periods than in bearish periods. Finally, the dependence between WTI crude oil and the SP500 stock index seems to be more affected by exogenous shocks and instability than the oil and European stock markets. [ABSTRACT FROM AUTHOR]

Uncertain time series models constitute a pivotal tool for analyzing phenomena evolving over time. However, most of the prevailing research centers on parameter models, which exhibit limitations in addressing intricate temporal dynamics. To address this gap, this paper introduces a non-parametric uncertain time series model. Primarily, we propose the definition of the non-parametric uncertain time series models. Subsequently, tailored to the non-parametric uncertain additive autoregressive model, we propose a non-parametric estimation method based on linear polynomial splines. Following this, the effectiveness of these estimations is verified through residual analysis and uncertain hypothesis tests. Finally, we apply the introduced non-parametric estimation approach to the Brent crude oil spot price. [ABSTRACT FROM AUTHOR]

This paper combines the Generalized Autoregressive Conditional Heteroskedasticity-Extreme Value Theory-Value at Risk (GARCH-EVT-VaR) method in conjunction with the Time-Varying Parameter Diebold-Yilmaz (TVP-VAR-DY) model to investigate the contagion of extreme risks between fossil and green energy markets in China. Specifically, the study concentrates on coal, crude oil, and natural gas markets as representative sectors for fossil energy, while green bonds, investments, green power, and associated new energy markets are chosen as representatives for the green energy sector. Our analysis reveals that extreme events can rapidly propagate risks between fossil and green energy markets, particularly during significant shifts in the external environment. Notably, green energy markets exhibit greater susceptibility to severe risks compared to their fossil energy counterparts, indicative of their instability and immaturity. Moreover, the analysis highlights the green bond market's heightened sensitivity to extreme risks, with green investments playing a pivotal role in propagating such risks throughout the system. These insights underscore the intricate dynamics of risk contagion between fossil and green energy markets, emphasizing the need for comprehensive risk management strategies in both sectors. [ABSTRACT FROM AUTHOR]

This article explores how declining oil revenue might shape the amount of international conflict initiated by major oil producers (petrostates). We analyze four potential mechanisms through which variation in oil prices could affect petrostate conflict initiation: emboldenment, battling over a smaller market, signaling strength, and diversionary conflict. The empirical findings suggest that higher oil prices are associated with lower rates of petrostate conflict initiation. From one standard deviation below the mean oil price to one standard deviation above it, the predicted number of militarized interstate disputes declines twofold, from.025 [95% CI:.016–.034] per petrostate per year to.012 [.007–.016]. Moreover, the evidence suggests that petrostates are more likely to target other petrostates when oil prices are low. This suggests that the energy transition may not be a boon for international peace among petrostates, and for a time, it may even prove to be the opposite. [ABSTRACT FROM AUTHOR]

Precisely forecasting the price of crude oil is challenging due to its fundamental properties of nonlinearity, volatility, and stochasticity. This paper introduces a novel hybrid model, namely, the KV-MFSCBA-G model, within the decomposition–integration paradigm. It combines the mixed-frequency convolutional neural network–bidirectional long short-term memory network-attention mechanism (MFCBA) and generalized autoregressive conditional heteroskedasticity (GARCH) models. The MFCBA and GARCH models are employed to respectively forecast the low-frequency and high-frequency components decomposed through variational mode decomposition optimized by Kullback–Leibler divergence (KL-VMD). The classification of these components is performed using the fuzzy entropy (FE) algorithm. Therefore, this model can fully exploit the advantages of deep learning networks in fitting nonlinearities and traditional econometric models in capturing volatilities. Furthermore, the intelligent optimization algorithm and the low-frequency economic variable are introduced to improve forecasting performance. Specifically, the sparrow search algorithm (SSA) is employed to determine the optimal parameter combination of the MFCBA model, which is incorporated with monthly global economic conditions (GECON) data. The empirical findings of West Texas Intermediate (WTI) and Brent crude oil indicate that the proposed approach outperforms other models in evaluation indicators and statistical tests and has good robustness. This model can assist investors and market regulators in making decisions. [ABSTRACT FROM AUTHOR]

Copyright of International Interactions is the property of Routledge 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 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 study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market. [ABSTRACT FROM AUTHOR]

Using a diagonal BEKK model, this paper estimates a spillover effect from the international crude oil market to the Korean stock market. Empirical results suggest that shocks and volatility in Dubai oil prices are significantly transmitted into twenty portfolios of the Korean stock market. Also, it was found that these spillover effects dramatically rose during the year 2020, when the threat of COVID-19 was the most serious. More specifically, oil-oriented portfolios, such as the power and gas firms' portfolio and chemical firms' portfolio, had a greater spillover effect from the international crude oil market rather than other portfolios. Further, compared to larger-capitalization firm portfolios, small-capitalization firm portfolios had a relatively greater spillover effect. Several implications and important avenues for further research are identified. [ABSTRACT FROM AUTHOR]

It has been widely documented that the relationship between crude oil prices and the value of US dollar changes over time (Beckmann et al. in Energy Econ 88:104772, 2020). However, the underlying economic driver for the time-varying relationship is not clear. Based on the competitive storage theory, we provide theoretical evidence that greater inelasticity of market demand for crude oil induced by low inventories is expected to lead to higher responsiveness of crude oil prices to exchange rate changes. We empirically test this hypothesis using the threshold vector autoregressive (TVAR) model and show that crude oil prices respond to shocks to the US dollar exchange rates in an asymmetric manner. Changes in the US dollar exchange rates have greater and more significant influence on crude oil prices in the low-inventory regime than in the high-inventory regime. [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 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]

This research investigates the connectedness and the tail risk spillover between clean energy and oil firms, from January 2011 to October 2021. To this, we use the Tail-Event driven NETworks (TENET) risk model. This approach allows for a measurement of the dynamics of tail-risk spillover for each sector and firm. Hence, we can provide a detailed picture of the existing extreme relationships within these markets. We find that the total connection between the markets varies during the period analysed, showing how the uncertainty in oil price plays a critical role in the risk dynamics for oil companies. Also, we find that relationships between energy firms tend to be intrasectoral; that is, each sector receives (emits) risk from (to) itself. These results can have important practical implications for risk management and policymakers. [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 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]

This paper attempts to investigate the dynamic linkages between Saudi crude oil prices to the select GCC (Gulf Cooperation Council) equity market during COVID-19 outbreak. We measure the Saudi oil market as Saudi Aramco share price while equity market of GCC economies is proxied as DFM General Equity, MSCI Qatar Equity, Kuwait Main Market 50, MSCI Oman Equity and Bahrain All Share Equity respectively. The data examined markets is collected extending from December 31, 2019 to July 31, 2022. Empirically, the result unfolds that there is no linkage of Saudi crude oil with Bahrain equity market in both short and long run; rest market is witnessed with mixed of linkage in short and long run. Additionally, it reveals that Oman stock market is the highest contributor while Bahrain equity market is the highest receiver of the shock. The more connectedness is found in the long run comparatively. This study furnishes an insight to the various stakeholders of the crude oil markets and financial markets. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers. Design/methodology/approach: With daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets. Findings: The empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What's more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to "same fall but different rise" scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors. Originality/value: The MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What's more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices. [ABSTRACT FROM AUTHOR]

This paper investigates how structural oil market shocks transmit through real oil price changes to the U.S. core prices. Separating out the sources of oil price increases reveals that supply shocks lead to significant and persistent increases in core prices while oil inventory demand shocks pull them down. Other demand-driven shocks do not cause any significant core price fluctuations. Examining different sample periods, we find empirical evidence supporting the strengthening of the oil price pass-through to some degree since mid-1980; however, not much change in the pass-through has been observed since the outbreak of Covid-19. Our findings highlight that understanding the sources of oil price changes is crucial for gauging their impacts on core prices and furthermore, for the conduct of monetary policy. [ABSTRACT FROM AUTHOR]

The Shanghai crude oil futures market, as the largest crude oil market in the Asia-Pacific region, is an important complement to the global crude oil market. We use the autoregressive distributed lag model (ARDL) to examine long-term and short-term cointegration relations between the Shanghai crude oil futures market and China's crude oil spot market (Daqing), international (WTI and Brent) benchmarks. Based on the price data of the Shanghai crude oil futures contract covering a long period, we find that, in the long run, the Shanghai crude oil futures price is greatly affected by international crude oil markets. In the short run, both international and China's crude oil spot markets have a significant influence on the Shanghai crude oil futures prices. Moreover, our dynamic simulation results show that there is a significant time lag for the Shanghai crude oil futures market to respond to the shocks from the domestic and international markets. [ABSTRACT FROM AUTHOR]

The primary contribution of the present paper is to explicitly take into consideration the role that the three different components of crude oil shocks – oil supply shocks, aggregate demand shocks, and oil-specific demand shocks – when studying the nexus between oil price shocks and the South Korean won (KRW). We discover that oil demand shocks have had significant impacts on KRW over the past two decades: that is, an upsurge in oil prices driven by positive shocks in aggregate demand and oil-specific demand appears to appreciate KRW. However, oil supply shocks turn out to have negligible impacts. We also unveil that two oil demand shocks seem to asymmetrically influence KRW in the short run. [ABSTRACT FROM AUTHOR]

This study investigates the impact of oil price uncertainty (OPU) on corporate profitability in China, the world's largest crude oil consumer. Most importantly, we examine how the Chinese government's oil price reform affects this relationship. Using the yearly data of Chinese-listed companies, we find that the uncertainty of oil prices negatively affects corporate profitability but positively impacts operating expenses from 2007 to 2020. This finding holds after robust tests, including alternative profitability metrics and endogeneity model. Most interestingly, implementing the 2013 market-oriented oil pricing reform amplifies the adverse impact of OPU on corporate profitability owing to increased operating costs in the post-2013 period. Moreover, the detrimental effect of uncertain oil prices on corporate profitability is less prominent for large-capitalized companies. This research adds to the body of knowledge on the factors affecting corporate profitability by highlighting the volatility effect of oil prices and government pricing mechanisms. The results offer grounds for legislators and corporate managers to consider how to control the uncertainty surrounding oil price matters to ensure stable corporate profitability. [ABSTRACT FROM AUTHOR]

This paper examines spillover from financial exchange-traded funds (ETF) futures to energy ETF futures using adjusted daily data extending from April 2, 2009, to November 23, 2020. We also explore the portfolio hedging-based conditional variance and co-variance derived from dynamic conditional correlation. The proxies for the financial ETF futures are financial select sector SPDR fund (XLF) and generic 1st S&P 500 index futures (SP1) while generic 1st crude oil WTI futures (CL1), generic 1st natural gas futures (NG1), and energy select SPDR fund (XLE) are proxies of energy ETF futures. The results obtained from Granger causality indicate that there is unidirectional causality from RXLF to RSP1 while bidirectional causality between RXLF and RCL1 at a 5% significance level. Further, dynamic conditional correlation indicates the spillover effect from RXLF to RCL1, RXLF to RXLE, RSP1 to RCL1, and RSP1 to RXLE both in the short-run and long run. The spillover from RXLF to RNG1 is witnessed only in the short run while the spillover from RSP1 to RNG1 is present in long run. The present study corroborates with the studies of Chang et al. (Int J Finan Stud 6(2): 1–24, 2018) and Lau et al. (Int Rev Finan Anal 52: 316-332, 2017). We notice that the average optimal hedge ratio of the RXLF/RNG1 pair is the most expensive while the cheapest hedging strategy is of RSP1/RCL1 pair. [ABSTRACT FROM AUTHOR]

This paper extends the literature on the relationship between oil price shocks and financial markets by examining the effect of supply and demand oil shocks on emerging energy markets, stocks of emerging markets, and gold and exploring the impact of unpredictable oil events on the degree of connectedness among these markets. We show that the effect of supply oil price disturbances drives volatility spillovers in emerging markets with prominent medium- and long-term effects, unlike demand oil price unanticipated changes, particularly during turbulent periods such as the 2008 subprime crisis, the COVID-19 pandemic, and the 2015 oil price crash. These volatility spillover effects are influenced by a marked relationship between supply oil disturbances and emerging energy markets. We also expose that the COVID-19 pandemic volatility spillover consequences in emerging markets are unprecedented compared to the 2008 financial crisis. This can be attributed to the different nature of the related oil price disturbances and financial crises. Overall, the findings highlight the role of crude oil supply shocks as drivers not only of volatility dynamics in energy and equity emerging markets but also of financial connectedness patterns in these economies. [ABSTRACT FROM AUTHOR]

Global shocks potentially distort economy's achieved equilibria. Considering the 2020 global crude oil price shock and the 2019 coronavirus disease pandemic, this study proposes an energy and environment integrated general equilibrium model to analyze the economic, energy, and environmental effects of these global shocks on Nigeria, a developing, oil-producing, oil-dependent, and oil-exporting country. Furthermore, the mitigating roles of a fiscal stimulus–response package (palliative) are investigated and analyzed. Generally, the developed model predicts a decline in the level of economic activities. The study results are unsurprising due to Nigeria's heavy reliance on crude oil. However, sectorial-specific impacts exist as some sectors experience output declines while others do not. Environmental quality is improved since more carbon is abated, nonetheless. Carbon intensities increased given that the price effects outweighed the quantity effects— reduced emission results from reduced economic activities and not from technological progress. The results further show a revenue-abatement paradox; a fixed carbon tax approach minimizes the tax revenue loss but may discourage carbon abatement. Conversely, the ad valorem and specific carbon tax systems encourage carbon abatement but reduce carbon tax revenues. The government's fiscal policy stimulus–response (palliative) action dampens the impact of these global shocks on both the domestic agents and the overall economy. The results are robust and can be applied to the experiences of other developing oil-producing, oil-exporting, and oil-dependent economies. [ABSTRACT FROM AUTHOR]

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

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

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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]

Empirical research exploring the relationship between capital markets and energy prices plays a crucial role in shaping policies for the growth of the Islamic financial system. This study aims to investigate potential shock transmission and volatility spillover effects among Islamic stock indices from selected Middle East and Northern Africa countries as well as crude oil prices and natural gas, over the period from August 2007 to September 2020. Applying VAR-BEKK-GARCH representation, the results reveal the evidence of bidirectional cross-market shock and volatility spillover effects between Kuwait and Qatar Islamic stock indexes, crude oil prices, and natural gas. Moreover, the results indicate the existence of bidirectional/unidirectional shock and volatility spillovers between Islamic indexes and all other variables, meaning there are information flows between these variables in all four countries except Turkey. Regarding the results of volatility spillovers, there is no spillover effect between Turkey’s MSCI Islamic index and Brent crude oil. These findings bear significant implications for portfolio management, offering valuable insights to financial market participants for making improved portfolio allocation decisions. Also, comprehending the volatility transmission mechanism across these markets is vital to provide policymakers and regulatory authorities with insight into the impact of energy prices on Islamic stock markets. [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]

In the intricate and dynamic landscape of the Crude Oil Products Industry, it is imperative for companies to comprehend customer needs and desires thoroughly. This understanding enables proactive responses to customer needs, fostering enduring, mutually beneficial business relationships, and leveraging customer dissatisfaction as opportunities for service recovery and retention. This study aimed to develop a B2B customer complaint management model for Iraq's crude oil products industry, employing a mixed-exploratory research method. The qualitative phase encompassed documentary analysis and theme analysis, revealing several critical components as influential factors in the customer complaint management model through literature review and in-depth interviews. The quantitative phase utilized Interpretive Structural Modeling (ISM) to identify and evaluate these key components. The ISM analysis provided valuable insights into the hierarchical relationships and influences of these factors, identifying timely product delivery and effective customer interaction as the most influential factors at the highest level. [ABSTRACT FROM AUTHOR]

This article describes a method for measuring the viscosity of oils that behave as Newtonian at low shear rates, by using only 80 μl of oil. This method is particularly useful when only such small quantities of oil are available—for example, when oil is extracted from grease taken from bearings. The method consists in placing an oil drop on blotter paper where an oil stain will grow in time. The rate of growth is then a measure of viscosity. Master curves were generated expressing the relation between viscosity and oil stain growth, both at a given time and as a function of time, each with its own accuracy. The viscosity measurements with this method were compared with those obtained in a conventional viscometer using poly-alpha-olefin (PAO), ester, and mineral oils. [ABSTRACT FROM AUTHOR]