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Volatility forecasting is an important issue in the area of econophysics. The information content of implied volatility for financial return volatility has been well documented in the literature but very few studies focus on oil volatility. In this paper, we show that the CBOE crude oil volatility index (OVX) has predictive ability for spot volatility of WTI and Brent oil returns, from both in-sample and out-of-sample perspectives. Including OVX-based implied volatility in GARCH-type volatility models can improve forecasting accuracy most of time. The predictability from OVX to spot volatility is also found for longer forecasting horizons of 5 days and 20 days. The simple GARCH(1,1) and fractionally integrated GARCH with OVX performs significantly better than the other OVX models and all 6 univariate GARCH-type models without OVX. Robustness test results suggest that OVX provides different information from as short-term interest rate.

Affecting a significant portion of the world economy, the commodity market is the world’s largest ‘nonfinancial’ market. In addition to the other macroeconomic variables, commodity prices have a special importance for Islamic stock indices, which have improved in recent years because Islamic finance is a financial system based on the profit/loss sharing principle and supports the real sector’s activities. In this study, cointegration and the causality relationship between commodity markets (the Gold ounce and Brent oil) and the Participation-30 Index established by taking Islamic criteria into account were investigated in Turkey. As a result of the analysis, no cointegration relation was found between the Participation-30 Index and the commodity markets. According to these results, an investor investing in gold or oil will, in addition to the Participation-30 Index, diversify its portfolio with this commodity investment and minimize its risk. The fact that there is no relation between the variables according to the causality results indicates that the Participation-30 Islamic Indices’ structure is independent of commodity markets and conventional stock markets.

This paper investigates whether investor sentiment has stronger predictive power than VIX and uncertainty indices in predicting the realized volatility (RV) of energy assets. The five representative energy assets we consider are natural gas spot and futures, WTI oil spot and futures, and Brent oil spot. Several significant findings appear. First, the in-sample results suggest that VIX has a significantly positive impact on the five energy assets and investor sentiment has a significantly positive impact only for WTI oil futures and spot. Second, the out-of-sample results show that investor sentiment performs best predictions for the RV of three crude oil-related assets, followed by VIX, however, they have almost no predictive ability on natural gas futures and spot. Third, the use of VIX can increase economic returns for natural gas spot, and the use of investor sentiment can achieve higher economic returns for three crude oil-related assets. Finally, the results are supported by numerous robustness checks.

Forecasting crude oil prices is an essential research field in the international bulk commodities market. However, price movements present more complex nonlinear behavior due to an increasingly diverse range of risk factors. To achieve better accuracy, this study explores a novel multiscale hybrid paradigm to estimate crude oil prices. The method takes advantage of the variational mode decomposition method to decompose the crude oil price into several simple models, which can be explained using regular factors, irregular factors and trends. Data characteristic analysis is conducted to identify the complexity of different components of the time series. It is important for a multiscale model to select an appropriate model to produce the optimal forecasts. Thus, the final forecasted values are generated by reconstituting all these forecasting items. By investigating the West Texas Intermediate and Brent crude oil prices, this paper presents how data characteristic identification and analysis are conducted in a multiscale paradigm. The empirical analysis proves that the proposed model can achieve superior forecasting results, which indicates the effectiveness of the multiscale model at forecasting complex time series, especially crude oil prices.

This paper investigates the dynamic relationships between Bitcoin, Oil, Natural Gas, Gold and Coal in the short- and long-run over the period 2011–2018. For this end, we use a Smooth Transition Error Correction Model (STECM) to accommodate the presence of some stylized facts (the nonlinearity and asymmetry) in the adjustment process of Bitcoin towards their long-run equilibrium. We show significant long-run equilibrium links for Bitcoin, which seems to be asymmetric and nonlinear. Gold and Oil Brent Crude logarithmic prices potentially and significantly influence the Bitcoin logarithmic prices. Our empirical results also indicate that the current Bitcoin logarithmic prices have a significant and positive impact on their lagged values for both regimes, implying some symmetric memory effects over time. Such analysis of the interlinkages between different assets could be interesting from investment and academic perspectives.

The role of non-financial sector market fluctuations such as the role of oil price uncertainty either on financial stability or on policy-related economic uncertainty has not been investigated extensively. This study examines the connectedness of financial stress and economic policy uncertainty with a non-financial market of Brent oil and its prices, by employing a Diebold and Yilmaz (2012, 2014) generalized framework and a TVP-VAR model specification. We use monthly series of 7 advanced countries, namely the US, UK, Canada, Japan, Germany, France, and Italy and 2 different indices for measuring financial stress (FSI) and economic policy uncertainty (EPU), over the period 2007–2020. Furthermore, the literature on oil price uncertainty and its effect on financial stress and on economic policy uncertainty is scarce. We additionally, examine the impact of oil price volatility on FSI and on EPU, by employing a structural VAR-GARCH-M model specification and IRFs analysis. Our results from the dynamic connectedness analysis indicate that during the COVID-19 pandemic, spillovers have increased substantially but not exceeded the Global Financial Crises 2007 levels. Finally, our estimation results from the IRFs analysis reveal that oil price uncertainty is associated with higher financial stress for some of the G7 countries while it is not related to economic policy uncertainty. © 2021 Elsevier B.V.

International audience; This paper provides an analysis of crude oil, diesel, and gasoline prices for the period from November 1, 2019 to December 31, 2020. We apply Log Periodic Power-Law Singularity (LPPLS) and Discrete Scale LPPLS bubble indicators to explore the dynamic bubbles of oil prices and predict their crash times. The results indicate that West Texas Light crude oil and North Sea Brent crude oil experienced a statistically significant negative financial bubble during the COVID-19 outbreak. In addition, gasoline and diesel prices are mainly driven by fundamentals. Our findings are expected to be useful to oil market investors, policymakers, and energy experts.

Oil is an important source of energy and strategic materials. Understanding the function of price discovery in the futures market, the role of the futures market can be better played, and it is of great significance to ensure the security of energy supply. In this paper, the relationship between futures price and spot price is investigated by means of econometrics, to study price discovery modes on the futures market. The Brent crude oil (2007 to 2016) future price and spot price data were used in the study. It is found that, in GS, IS and PT models, the IS and PT model have their own advantages, which can be combined with two models to calculate the level of price discovery while the GS model is invalid. Comparing the price discovery level from 2007 to 2016, most of the price discovery of the oil futures market is higher. At the same time, the volatility of price is not the main reason behind price discovery. The main factors leading to the decrease of price discovery are the development of the macroeconomy and the degree of price volatility.a

We study causal relations between the return of Brent crude and the oil-intensive equity indices (agricultural, construction, industrial and transportation) in China using daily data from 2005 to 2019. We unite the extensive Granger causality tests proposed over the past two decades that are based on the cross-correlation function approach to uncover causality in mean, variance, risky quantiles and distribution. We use subsample analysis to study changes of causal links after the US subprime financial crisis in 2008 and the Chinese domestic refined oil pricing reformation in 2013. We find bidirectional causality in variance and distribution between the Chinese industries and global oil in each subperiod. Before the oil reform, most of the sectors cause crude oil in mean, which coincides with the rapid economic growth in China. As the Chinese markets become more exposed to international oil after the reformation, extreme negative returns of global oil benefit most oil-intensive industries while positive outlook in Brent adversely affects most sectors.

Reasonable carbon price can effectively promote the low-carbon transformation of economy. The future carbon price has an important guiding significance for enterprises and the country. However, the nonlinear and high noise characteristics inherent in carbon price make them challenging to predict accurately. A hybrid decomposition and integration prediction model is proposed using the Hodrick-Prescott filter, an improved grey model and an extreme learning machine to solve this problem. First, a large number of factors that influence carbon price are collected by meta-analysis. The final input is selected through a two-stage feature selection process. Second, the HP filter is used to decompose the input into long-term trends and short-term fluctuations predicted by the improved GM and ELM, respectively. Finally, the two prediction sequences are compared to obtain the final result. European Union Allowances futures price data are applied for empirical analysis. The results show that the prediction performance of this model is better than the other 10 benchmark models, the T-bill, Stoxx50, S&P clean energy index and Brent oil price in the financial and energy markets are helpful in the carbon price's prediction. T-bill affects carbon price frequently, Stoxx50 has a negative correlation with the carbon price in the influence period. Under normal circumstances, the S&P clean energy index is positively correlated with the carbon price. However, when the economic situation is depressed, resulting in a short-term negative correlation between them. In general, carbon market is significantly affected by cross spill over between different markets. The method not only improves the accuracy of carbon price forecast, but also the application of the improved GM explains the reasons for the change of carbon price, which is helpful to promote the realization of carbon neutralization by market-oriented means.

The launch of the China's Shanghai International Energy Exchange (INE) oil futures market in 2018 has shed new light on the role of China in international crude oil market. Understanding the dynamics of the newly arrived RMB denominated crude oil futures market not only facilitates the international market participants in risk management and hedging, but also provides helpful information for policy-makers, especially for those from emerging countries, to financialize its energy market, along with the currency internationalization and financial market liberalization. However, the literature on the China crude oil futures is quite scant compared with abundant literature on international benchmarks. In this regard, we make attempts to capture the dynamics of the China crude oil futures by: (1) adopting Markov switching analysis to uncover the regime switching of the INE crude oil futures market; (2) investigating the dynamic connectedness of INE, WTI, and Brent crude oil futures; (3) forecasting the realized volatility of INE crude oil futures. The results have shown that: (1) the outbreak of global pandemic at the beginning of 2020 has switched the crude oil futures market from a stable regime to a volatile regime; (2) the increasing financial uncertainty originated from the world, U.S., other advanced countries and emerging countries could significantly negatively affect the movement of crude oil futures. However, China suffers the least; (3) the dynamic conditional correlations between INE vs WTI, and INE vs Brent are high but lower and more volatile than that of WTI vs Brent; (4) Brent crude oil futures contribute to improving the accuracy of volatility forecasting of INE crude oil futures; and more importantly (5) we highly recommend carrying out volatility forecasting by incorporating intraday realized measures into mixed data sampling approach, in particular, when intraday transactions present extremely different behavior across the time.

This paper studies the impact of oil prices on financial market volatilities and clean energy investments by including the energy democracy approach to its analysis. Our main intention is to question whether a carbon free sustainable financial market environment. Since crude oil is still the primary energy source and the markets heavily depend on oil prices, any surge in the prices creates uncertainty and increases the fear in the markets. In this study, we contribute to the existing literature in two ways. First, rather than seeking a direct relationship with oil-clean energy returns, we include market fear represented by CBOE volatility index (VIX) to our models. Secondly, we utilize VAR-VECH-TARCH models with three different model systems and two different VAR system specifications for two different time periods. According to our asymmetric volatility model results, long-term volatility spillovers exist between clean energy, brent oil and volatility index. Moreover, considering the cross relationship between clean energy, brent oil and VIX, bad news contributes more to the systemic risk of energy company stock returns than good news. These findings state that the relationship between energy democracy and clean energy investments should be considered in more details while policy implications should support renewable energy investment policies and reduce the pressure of oil suppliers on both financial markets and clean energy investments.

Existing studies have investigated the Chinese Shanghai crude oil futures (INE) from price efficiency, cross-futures transmission, etc., but neglected the potential links with China. This paper is committed to filling this gap by conducting an initial discussion from the perspective of macro-financial factors. Applying the dynamic model averaging (DMA) approach, we examine the time-varying importance of the six potential factors that drive the INE prices. The results based on the overall conditions of all determinants and based on individual predictors both support the crucial roles of some Chinese macro-financial factors. The pricing effects of these factors almost display upward features within 6 months since the INE establishment. Thereafter, it maintains an overall stable trend, though some abnormal turmoil is found after the COVID-19 outbreak. According to the multi-scale analysis, the importance of China's macro-financial factors mainly reveals the INE market at the low-frequency components. To confirm the robustness of the estimation and the uniqueness of such effects on the INE, we utilize an alternative forecast accuracy criterion to confirm stability, accommodate the DMA estimation on the WTI and Brent oil futures prices as comparisons, and discuss the frequency domain through another decomposition procedure. These all mirror our findings.

This paper examines the dynamic frequency co-movements and volatility spillovers between crude oil, gas oil, gasoline, heating oil, and natural gas futures markets during the global financial crisis and European crisis (GFC & ESDC), recent oil price crash, and COVID-19 pandemic crisis. We apply the spillover index by Diebold and Yilmaz (2012) and wavelet methods. The results show significant risk spillovers among the leading energy futures markets. Moreover, the spillovers are intensified during the financial crisis, oil crisis and COVID-19 outbreak. WTI crude oil is the highest net contributor of volatility spillovers to the other markets, whereas the other energy markets are net receivers of spillovers for the different sub periods, with the exception of natural gas before the GFC and during COVID-19 as well as Brent oil during COVID-19. Furthermore, the results show significant integration and multiscale co-movements among energy futures. Natural gas asset offers better diversification benefits to crude oil, heating oil, gasoline and gas oil under short term. Finally, the diversification gains diminish as scales rise. The optimal weight, hedge ratios and hedging effectiveness are time varying and crises-sensitive. These findings have significant implications for energy traders and policymakers to inform their decision-making.

We examine the risk transmission from the COVID-19 to metal (precious and industrial) and energy markets using the BEKK-MGARCH model. The findings reveal the significant and negative volatility transmission from the COVID-19 to gold, palladium, and brent oil markets, suggesting the safe-haven properties of these markets. The COVID-19 risk is not transmitted to the industrial metal market, whereas the rise in COVID-19 volatility leads to an increase in WTI oil market volatility. These results provide useful insights to investors and policymakers regarding risk management, asset pricing, and financial market stability during the COVID-19 pandemic.

This paper examines the volatility spillover effects between precious metals futures (gold, palladium, platinum, and silver), Brent oil futures, and ASEAN stock markets (Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam) at multiple time horizons and during bear and bull market periods. The results show that precious metals, crude oil, and the Vietnamese stock market are net receivers of spillovers irrespective of time horizons and during bearish market conditions. In contrast, during the bullish market conditions, Brent oil futures and Vietnamese stock markets become net contributors to short-term spillovers. Furthermore, the stock markets of Indonesia, Malaysia, and the Philippines are the largest contributors to spillovers under bear and bull market conditions and regardless of the time investment horizons. Moreover, adding precious metals or oil futures to the ASEAN equity portfolio provides better hedging effectiveness during different considered crises. The role of Brent oil and precious metals futures serves as diversifier, hedge, and safe haven assets is discussed under major events.

Oil plays a pivotal role in the growth of agriculture as a combustion lubricant for machineries and equipment used in the farming enterprise. Several studies have shown that a relationship exists between oil prices and agricultural growth without clear boundaries beyond which these prices are detrimental to the growth. Therefore, this study is conducted to identify the threshold above which oil prices will adversely affect agricultural growth in South Africa. Real West Texas Intermediate (WTI) and Real Brent crude oilprices in both Dollars and Rands were used as threshold variables in the threshold regression model of agricultural growth. The findings showed that beyond the threshold values of 12.99%, 15.68%, 15.69% and 15.70%, the prices of Real WTI crude oil in Dollars, Real Brent crude oil in Dollars, Real WTI crude oil in Rands and Real Brent crude oil in Rands respectively will have significant negative effects on agricultural growth in South Africa.

Accurately forecasting the energy price has increasingly attracted attention of researchers. A novel hybrid forecasting model, termed as ICEEMDAN-R-AttGRU, is for the first time proposed to enhance the forecasting accuracy of energy price series (Brent oil, DCE coke and NYMEX natural gas). In the proposed model, the improved CEEMDAN is taken to decompose the raw prices into multiple subcomponents. Then, a novel regrouping method based on frequency is put forward to reconstruct the subcomponents to reduce the forecasting workload and diminish the chance of errors. The attention-based GRU network is adopted to perform the forecasting task for each component, in which the attention mechanism is applied to allocate and optimize weights to the input elements in GRU. The empirical results measured by various performance metrics verify that the predictive accuracy is evidently improved by ICEEMDAN-R-AttGRU model in most cases compared with single models, ICEEMDAN-based and ICEEMDAN-R-based hybrid models. Besides, a new multi-scale composite complexity synchronization (MCCS) statistic is introduced into model measurement, which further confirms the competitive prediction ability of ICEEMDAN-R-AttGRU in different exponent and time scales.

Crude oil has an important role in the financial indicators of global markets and economies. The price of crude oil influences the income of a country, both directly and indirectly. This includes affecting the prices of basic needs, transportation, commodities, and many more. Therefore, understanding the future price of crude oil is essential in helping to budgeting and planning for a better economy. The contribution of this research is in finding the best hyperparameters and using early stopping methods in the LSTM model to predict oil prices. This research implemented Long Short-Term Memory (LSTM), an artificial neural network that can handle long-term dependencies and the problems of time series data. The LSTM method will be used to predict Brent oil prices on daily and weekly time frames. The experiment has been conducted by tuning some parameters to obtain the best result. From the daily time frame experiment, the model obtained RMSE and MAE of 1.27055 and 0.92827, respectively, while the weekly time frame has RMSE and MAE of 3.37817 and 2.60603, respectively. The results show that the LSTM model can improve to the trends that occur in the original data.

The prediction of crude oil prices has important research significance. The paper contributes to the literature of hybrid models for forecasting crude oil prices. We apply ensemble empirical mode decomposition (EEMD) to decompose the residual term (RES), which contains complex information after variational mode decomposition (VMD), further combining with a kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) to construct the VMD-RES.-EEMD-PSO-KELM model. In order to verify the validity of the model, this paper conducts empirical analyses of Brent crude oil and West Texas Intermediate (WTI) crude oil. The empirical results show that the prediction model proposed in this paper improves the prediction accuracy of crude oil prices.

This paper examines the asymmetric volatility connectedness amongst the Dow Jones Islamic Market Index (DJIM) and the Brent crude oil, gold, and silver markets. We use the Diebold and Yilmaz methodology to examine asymmetric volatility connectedness associated with bad (negative semi-variance) and good (positive semi-variance) volatility in these markets. We identify significant volatility connectedness between the DJIM and commodity markets, with the DJIM and Brent oil markets being the largest net contributors of spillovers. Furthermore, the evidence on semi-volatility connectedness displays asymmetric behavior. Bad volatilities are associated with net transmission of spillovers to other markets, except for silver. Our results have significant implications for investors dealing with the DJIM and commodity markets in terms of asset management and diversification.

Oil price forecasts are of crucial importance for many policy institutions, including the European Central Bank and the Federal Reserve Board, but projecting oil market evolutions remains a complicated task, further exacerbated by the financialization process that characterizes the crude oil markets. The efficiency (in Fama’s sense) of crude oil markets is revisited in this research through the investigation of the predictive ability of technical trading rules (TTRs). The predictive ability and trading performance of a plethora of TTRs are explored on the crude oil markets, as well as on the energy sector ETF XLE, while taking a special focus on the turbulent COVID-19 pandemic period. We are interested in whether technical trading strategies, by signaling the right timing of market entry and exits, can predict oil market movements. Research findings help to confidently conclude on the weak-form efficiency of the WTI crude oil and the XLE fund markets throughout the 1999–2021 period relative to the universe of TTRs. Moreover, results attest that TTRs do not add value to the Brent market beyond what may be expected by chance over the pre-pandemic 1999–2019 period, confirming the efficiency of the market before 2020. Nonetheless, research findings also suggest some temporal inefficiency of the Brent market during the 1 and ¼ years of pandemic period, with important consequences for energy markets’ practitioners and issuers of policy. Research findings further imply that there is evidence of a more intense financialization of the WTI crude oil market, which requires tighter measures from regulators during distressed markets. The Brent oil market is affected mainly by variations in oil demand and supply at the world level and to a lesser degree by financialization and the activity of market practitioners. As such, we conclude that different policies are needed for the two oil markets and also that policy issuers should employ distinct techniques for oil price forecasting.

This paper investigates risk spillovers and hedge strategies between global crude oil markets and stock markets. In the paper, we propose a multivariate long memory and asymmetry GARCH framework that integrates state-dependent regime switching in the mean process with multivariate long memory and asymmetry GARCH in the variance process. Our results first show that there are linear risk spillovers running from the US stock markets to the WTI oil market in the short term. However, the linear risk spillover effect running from the oil market to the US stock market can only exist in the long term. In addition, there is a bidirectional linear risk spillover effect between the European stock markets and the Brent oil market in the short and long terms. Furthermore, there is no linear risk spillover effect between the Dubai oil market and the Chinese stock market. Second, the nonlinear risk spillovers running from the WTI oil market to the US stock market can be found in the tranquil regime. Moreover, there is also a nonlinear risk spillover effect running from the European stock markets to the Brent oil market in the tranquil regime. In addition, the nonlinear risk spillover effect running from the Brent oil markets to the European stock market can be found in the crisis regime. Furthermore, there is bidirectional nonlinear Granger causality between the Dubai crude oil market and the Chinese stock market in the tranquil regime. Finally, dynamic hedge effectiveness shows that the regime switching process combined with long memory and asymmetry behavior seems to be a plausible and feasible way to conduct hedge strategies between the global crude oil markets and stock markets.

This paper investigates the time-frequency dependence, extreme risk spillovers, and dynamic linkages between oil and China’s commodity futures markets, using wavelet coherence, quantile connectedness approach, and the DECO-FIAPARCH (1,d,1) model. The results suggest that the oil market exhibits higher coherence with the copper, natural rubber, and fuel oil futures but low coherence with corn, soybean, soybean meal, and white sugar futures on a long-term scale. The crude oil market leads most of China’s commodity futures. The results of the DECO model point to the time-varying and low average equicorrelations between oil and China’s commodity futures. The total risk connectedness at the extreme lower quantile level (0.01) is higher than the conditional mean and conditional median level. WTI oil, Brent oil, soybean oil, and copper futures are the main spillover net-transmitters whereas the white sugar, soybean, soybean meal, cotton, corn, aluminum, natural rubber, and fuel oil futures are risk spillover net-recipients. The dynamic extreme negative risk spillovers are highly volatile and vulnerable to major international events such as the GFC, oil price plunge, and the COVID-19 pandemic. Finally, Brent oil offers better portfolio diversification benefits than WTI oil and the optimal-weighted portfolio illustrates the highest risk and downside risk reduction effectiveness.

This paper investigates volatility spillover in the Nigerian sovereign bond market arising from oil price shocks, using Vector Autoregressive Moving Average - Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (VARMA-AGARCH) model. The paper covers the period March 22, 2011 to April 14, 2016 and makes use of the daily data of the Nigerian Sovereign Bond, Brent oil and West Texas Intermediate (WTI), respectively. We endogenously and sequentially detect structural break points using the test of Bai and Perron (2003) framework. In order to accurately estimate the model, we modify it by incorporating the break points into the VARMA-AGARCH model, a process which if ignored would lead to model misspecification. The results obtained demonstrate a significant cross-market volatility transmission between oil and sovereign bond market with ample sensitivity to structural breaks. The study also computes optimum weight portfolio and hedge ratio both with and without structural breaks and results equally indicate sensitivity to structural breaks.

In order to investigate the impacting mechanism of investors’ attention and crude oil prices, we construct a direct, timely and unambiguous proxy for investor attention in crude oil market by aggregating the Google search volume index (GSVI). Based on the GSVI, we employ the Structural Vector Autoregression (SVAR) model to empirically explore the impact of investor attention on WTI crude oil price from January 2004 to November 2016. The results indicate that: (1) investor attention does have significant negative impact on WTI crude oil price during the sample period; (2) investor attention shocks contributes 15% to the long-run fluctuation of WTI crude oil price during the sample period, which is second only to that of supply shocks (69%) among the contributors concerned; and (3) when the business cycle stays in expansion, it has positive influence on both investor attention and WTI crude oil price. Meanwhile, our robustness check, using Brent crude oil price and a different construction form of the GSVI, confirms that the central results are reliable.

This study examines intricate interplay between crude oil, copper and gold futures prices, and market-implied inflation expectations that are proxied by the breakeven inflation derived from the 5-year US Treasury Note yields. We perform the Bai-Perron multiple breakpoint tests, Bayesian VAR with impulse response functions and GARCH with GED parameterization tests on daily data for the sample period January 3, 2003 – March 26, 2015. The results show a strong causal impact of shocks in the breakeven inflation on West Texas Intermediate and Brent crude oil as well as copper futures prices, albeit to varied degrees during the examined sample period. At times of low market risk and highly liquid markets West Texas Intermediate and copper futures prices move in tandem with the 5-year breakeven inflation. Prices of Brent and gold futures move in the opposite direction to the market-implied inflation.

Crude oil intoxication is a major threat among people and animals living around the crude oil producing regions of the world, hence the search for ameliorating agents. Forty-four male Wistar rats assigned into three groups were used to investigate the effects of vitamin E supplementation on crude oil-induced reprotoxicity (reproductive toxicity) in male rats. Group A represented the unexposed control, whereas groups B and C were exposed orally to 0.15 and 0.3 ml of crude oil respectively every other day for 56 days. Both the low dose and high dose oral administration of crude oil caused a significant reduction in the serum testosterone level (STL) and cauda epididymal sperm reserve (CESR) of the exposed rats when compared to the control. Crude oil withdrawal and vitamin E supplementation significantly improved the cauda epididymal sperm reserve (CESR) in all the subgroups. The serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities of the control and low dose group were significantly lower than those of the high dose group. The high dose crude oil administration significantly decreased the mean serum total protein (STP) and sodium ions (Na+) concentration. The mean serum total cholesterol (STC) value of the low dose group was significantly higher than those of the control and high dose group. However, crude oil withdrawal and vitamin E supplementation did not significantly alter the mean serum total protein (STP) and mean serum total cholesterol (STC) values in all the subgroups. Vitamin E supplementation following low dose crude oil withdrawal enhanced the mean serum Chloride ions (Cl-)concentration. The present findings revealed that Nigerian Qua Iboe Brent crude oil induced serious reprotoxic effects in male rats which vitamin E administration within 28 days did not completely reverse.

The degree to which spot and futures oil markets are integrated is an important issue to producers and consumers of oil, as well as speculators, arbitragers and policy makers. In this paper, we utilize intraday data to investigate the degree to which spot and futures markets in Brent oil are integrated. Our evidence confirms the previous findings that spot and futures markets are highly integrated. Economic shocks that arise in one markets are typically transmitted to the other oil markets approximately one for one, typically within minutes.

Recently, variational mode decomposition (VMD) has been proposed as an advanced multiresolution technique for signal processing. This study presents a VMD-based generalized regression neural network ensemble learning model to predict California electricity and Brent crude oil prices. Its performance is compared to that of the empirical mode decomposition (EMD) based generalized regression neural network (GRNN) ensemble model. Particle swarm optimization is used to optimize each GRNN initial weight within the ensemble system. Experimental results showed that the VMD-based ensemble outperformed EMD-based ensemble forecasting system in terms of mean absolute error, mean absolute percentage error, and root mean-squared error. It also outperformed the conventional auto-regressive moving average model used for comparison purpose. As a result, the VMD-based GRNN ensemble forecasting paradigm could be a promising methodology for California electricity and Brent crude oil price prediction.

This article examines the upside and downside risk spillover effects among crude oil (WTI and Brent) and Henry Hub natural gas markets. We consider value-at-risk (VaR) as a measure of risk and model both upside and downside 95 per cent, 99 per cent and 99.5 per cent VaR using various VaR approaches. The VaR models are evaluated using Christoffersen’s (1998) conditional coverage test and Lopez’s loss function approach to select the best-performing VaR model. Finally, we apply Hong, Liu and Wang’s (2009) approach to examine the upside and the downside risk spillover among crude oil and Henry Hub natural gas markets. We find significant two-way as well as one-way upside and downside risk spillover between WTI and Brent crude oil. Our results provide weak evidence of upside risk spillover from natural gas market to crude oil markets for 99.5 per cent VaR.

Esse artigo tem como principal objetivo, calcular nos últimos 12 trimestres (2013-2015) a estimação de oferta e demanda de petróleo, utilizando um modelo econométrico OLS indicando a oferta e demanda do mercado, em um segundo momento será direcionado um esforço para indicar as atuais curvas de oferta e demanda do mercado de petróleo pelo preço médio do petróleo Brent no período supracitado acima. Para que posteriormente, seja calculado o “peso morto” do modelo econômico de preço fixados da OPEP e como esse peso-morto afeta economicamente o mercado de petróleo e gás mundial, retirando economicamente os lucros que os principais produtores poderiam auferir caso a OPEP não fixasse o preço artificialmente em situações de cenário político instável. Indicando, por fim, quanto à economia global e, mais precisamente, o setor energético perde em termos financeiros com as ações tomadas pelo cartel guiado pela Arábia Saudita e seus parceiros no Oriente Médio, indicando as falhas de mercado propiciadas por um cartel.

The study investigates the dynamic relationship of global crude oil and gold price in a two-regime vector error-correction model with a single cointegrating vector and a threshold effect on error-correction terms as proposed by Hansen and Seo (2002) . The study is conducted with weekly spot prices of Brent crude oil and gold for the period January 2, 2009 to November 20, 2015. The empirical results suggest the effectiveness of threshold cointegration model over a linear cointegration model signifying non-linearity in long-term relationship between gold and oil price. It also establishes different long-run and short-run dynamics of gold and oil in two regimes, termed as ‘typical’ and ‘extreme’ regimes based on the threshold parameter. The study reveals a lead lag relationship between gold and oil price suggesting ‘gold’ as investors’ safe haven against inflation. This phenomenon, however, exists only in a typical regime. In contrast, in an extreme regime, with gold price dominating the market, investors’ are found to switch between gold and oil in order to diversify the portfolio risk. This indicates that the relationship between gold and oil price is non-linear and asymmetric. The study surpasses previous studies by establishing the fact that the relationship between gold and oil is regime dependent, and hence does not remain constant during the entire period of the study. The findings have important implications for policy makers and investors. The study also demonstrates robustness in empirical findings by maintaining profound statistical congruency.

Keywords: peak oil, crude oil, petroleum, Energy Returned on Investment, EROI, Energy Returned on Energy Invested, EROEI, hydraulic fracturing, oil sands, tar sands, ultra deepwater drilling, unburned carbon, global oil production, unconventional oil, conventional oil. 1. Introduction It is fair to say that the 'peak oil' scenario does not appear to have played out as straightforwardly as was being anticipated around a decade ago (1). At that time, the prediction was that the global production of oil, having peaked (1) around 2005 (to precede the bumpy plateau that it has been on ever since (2)), would fall into an inexorable decline and be unable to keep pace with demand for it, leading to enormous hikes in the oil price, perhaps to $200 a barrel. The (Brent) oil price (Figure 1) did indeed rise to $ 147 on 11 July 2008, but this had fallen to around $32 a barrel by the end of December, in that same year. The prices subsequently rose (3) to above $100 a barrel in March 2011, and remained high for the next few years, before crashing from a record $115 for Brent crude in June 2014, to just under $60 (a fall of nearly one hall) by the end of the year. Having declined to a low of $30 a barrel in Januaiy 2016, Brent crude is today (4) (29 March 2017) trading at $51.Thus, there are complex and interlocking forces at play, to which the price of oil is highly sensitive, which have included such events as the Lehmann Brothers financial collapse, the death of King Abdullah in Saudi Arabia (with attendant uncertainties over the future of the Kingdom), and the relative oversupply of oil against demand for it, primarily due to hydraulic fracturing of shale in the U.S., along with Canadian oil sands production, which caused the price of oil to initially plummet and then to languish as it has for the past three years. However, whatever may be quibbled over the cost/price of oil in $, the true measure of production viability is Energy Returned on Energy Invested (1-5,6) (EROEI)--sometimes the equivalent term, Energy Returned on Investment (EROI) is used--since it takes energy to produce energy, and ultimately it is primary input energy that is the determining factor in the production rate of a resource (i.e. physics trumps economics). The 'peak oil' concept (1) was never about 'running out of oil' per se, rather maintaining the global oil supply given not only prevailing technical, but also economic limits. An increasing reliance on unconventional oil--i.e. 'tight oil' produced by hydraulic fracturing ('tracking') of shale, oil from oil sands, (ultra) deepwater drilling etc.--means falling EROEI (1,5,6), higher production costs, higher carbon emissions, and less available energy for society from the total primary energy that is available, if more of that energy is consumed by overall energy production. However, it was only the very high oil price (3) that prevailed prior to its crash in 2014, which rendered alternative sources, such as recovering oil from shale by hydraulic fracturing, and processing bitumen from Canadian oil sands, economically viable. In the present period of low oil prices, the high costs of producing oil from these sources are pushing companies to the verge of bankruptcy (7), and further investment in them is stalling. 2. The global oil supply in overview The majority of the total energy (8) used by humans on Earth is provided by crude oil (33%), followed closely by coal (30%), and natural gas (24%); hence we are reliant on the fossil fuels for 87% of our primary energy. In terms of low-carbon energy, nuclear power provides 4%, and hydro-power 7%, with renewable energy (mainly wind and solar) contributing just above 2% of the total. Prior to about 1930, oil could be produced at an EROEI of 100, but this has declined over time (it is now nearer to 17 as a global average (6)), and is falling further (9,10) as unconventional oil sources increasingly make up for the decline in conventional production. …

Objective - The crude oil, also known as black gold, is an essential commodity for the sustainability of various industries in the world. Oil prices play an important role in world economy because it causes repercussions. For example, world oil prices plummeted at the end of 2013 and its impact created fluctuations in prices which had affected world economy badly. The aim of this research is to locate a good model that can help to predict oil price fluctuations so that industries can avoid potential negative impacts. Methodology/Technique - Data of world oil prices from 1987 to 2016 were extracted from West Texas Intermediate (WTI) and Brent Oil sources. A comparative analysis using Empirical Decomposition and Autoregressive Integrated Moving Average (ARIMA) was applied toidentify differences and data were then analysed through SPSS 23. For this research, a set of models based on the smallest MAPE (Mean Absolute Percentage Error) was proposed. Findings - Results indicate that the Empirical Decomposition was a more appropriate method for predicting oil prices due to the non-linearity of oil price data. In addition, the MAPE also produced a lower error rate than the ARIMA. Novelty - In this research, world oil price volatility fromWest Texas Intermediate (WTI) and Brent Oil Price data were examined to predict oil price movement for future anticipations. Type of Paper: Empirical Keywords: Forecasting, Oil Prices, Autoregressive Integrated Moving Average, ARIMA, Empirical Decomposition, West Texas Intermediate, Brent Oil Price.

This article aims the analyses of the causality and temporal precedence relationships between the spot and futures prices of Brent oil, those last ones inherent to financial markets. In order to achieve this objective, the main tools used were: Johansen cointegration test; Granger causality/Block Exogeneity Wald test (GCBEW); generalized impulse response function and variance decomposition of forecast errors, those three last ones were estimated based in a Vector Error Correction Model (VEC), adjusted to the analyzed variables. The results indicated that, for the stipulated period, there was a pricing lightweight leadership from the futures contracts to the prices of spot market. However, as a conclusion of the article, the understanding is that this small difference, calculated in several econometric tools, cannot be considered enough to indicate that the Brent oil future market would be distorting its respective spot prices, despite the economic fundamentals of the physical market, such as production, consumption and stockpiling processes. Therefore, management decisions in industries exposed to crude oil prices should be aware of both physical and future markets’ prospections.

One of the promising approaches of macroeconomic modeling and quantitative assessment of the impact of external and internal factors on macroeconomy of a country, which is actively used abroad, is a Bayesian approach to the description of macroeconomic processes. In this paper we examine Bayesian vector autoregression model (BVAR) to assess the impact of external shocks, such as the price of Brent crude oil, the volatility index VIX and the Shanghai Stock Exchange Composite index, on Russian macroeconomic indicators. The results allow us to estimate the contribution of external factors as a significant in the dynamics of Russia economic variables. This approach can be successfully applied for the analysis of Russian data, which was confirmed by the results presented in the article.

The European carbon emission trading market is critical in achieving planned carbon emission reduction for global sustainable growth. This paper investigates various statistical methods in forecasting the European carbon emission (CO2 hereafter) price movements. The paper builds a predictive regression model of CO2 price movements with past returns of various commodities and financial products. In the paper, 22 functional forms of five different classifiers are employed and CO2 price movements are forecast. Results indicate that the past returns of Brent crude futures, natural gas (NG), Financial Times Stock Exchange 100 (FTSE100), Deutscher Aktienindex (German stock index) 30 (DAX30), Cotation Assistee en Continu (French stock index) 40 (CAC40) and Standard & Poor's 500 (S&P500) are statistically significant in forecasting the current CO2 price movements. The authors also found that the bagged decision tree of the ensemble classifier best forecasts the CO2 price movements. The result should be re...

The oil price is a relevant variable for economic policy makers in countries where this commodity is the main energy source as well as in other countries where crude oil is not the only energy source. The sudden variations in the crude oil price cause direct influence in the national economies bringing changes in foreign trade, investments and productive activities. Therefore, the crude oil market is very important for the economic development. Furthermore, crude oil is directly or indirectly present in all productive activities. This way the crude oil market is related to the industrial production indicators. Many researches aim at establishing the stochastic process that can represent the movements of macroeconomic indicators through the oil price returns or variations that have been done in recent years. The purpose of this work is to study the relationship between crude oil prices and selected industrial production indicators of the Brazilian economy. To do that this work carried out cointegration and causality tests, from VAR estimations, and impulse response analysis. The data used in this study is monthly macroeconomic indicators, mentioned above, and the Brent crude oil type price negotiated in the London Market. All data used is in US$. The period of the sample used is from January 2002 to October 2015.

Objective of this study is to empirically investigate the time varying co-movements between crude oil and Indian stock market returns both at aggregate and sector level. This study uses weekly closing prices for Brent Crude, BSE-Sensex and seven sector indices of Bombay Stock Exchange namely Automotive, Energy, Financial, Industrial, Metal, Oil & Gas and Power as data input. The data span of this study runs from January 1, 2006 to Feb 28, 2015, which encompasses the booming, recessionary and the recovering phase of global as well as Indian economy. The paper deploys VAR-DCC-GARCH framework. Three versions of GARCH namely standard, threshold and exponential and both symmetric and asymmetric versions of dynamic contemporaneous correlations have been used. Results of the study indicate that direct volatility spill over from oil market to Indian stock market is not significant at the aggregate level; however, it is significant in case of auto, power and finance sector. Parameter of dynamic correlations and volatility were significant thereby providing empirical evidence of the time varying differential dependence of Indian stock sector indices on oil price fluctuations. Outcomes of this study highlights that investors attempting to diversify their investments should always consider dynamic volatility and correlation linkages so as to maximize returns and minimize risk.

The oil price time series data can be affected by major global political and economic events, which would result in structural changes that could lead to biased estimations. By adopting the Bai and Perron model this paper found that there were six structural breaks in the Brent oil price due to major global events and that ARDL-ECM cointegration exists only between oil price and stock market volatility index (VIX) throughout the sampling period. However, cointegration relations were found between oil price and Crude Oil Volatility Index (OVX) in the second and fourth sub-periods, and also between oil price and VIX in the second, third, fourth, sixth, and seventh sub-periods. Furthermore, the cointegration relation coupled with correlation analysis indicates a long-term equilibrium positive (negative) relation between the two variables. Such relations existed between the price and the two fear gauges, respectively, only for some specific sub-periods, implying that OVX seemed to be better than VIX in predicting oil price changes. We suggest that the investors in the global oil market must pay attention to not only the impacts of major global political and economic events on oil price, but also the positive or negative correlations between oil price and fear gauge.

This paper proposes a hybrid Bayesian Network (BN) method for short-term forecasting of crude oil prices. The method performed is a hybrid, based on both the aspects of classification of influencing factors as well as the regression of the out-of-sample values. For the sake of performance comparison, several other hybrid methods have also been devised using the methods of Markov Chain Monte Carlo (MCMC), Random Forest (RF), Support Vector Machine (SVM), neural networks (NNET) and generalized autoregressive conditional heteroskedasticity (GARCH). The hybrid methodology is primarily reliant upon constructing the crude oil price forecast from the summation of its Intrinsic Mode Functions (IMF) and its residue, extracted by an Empirical Mode Decomposition (EMD) of the original crude price signal. The Volatility Index (VIX) as well as the Implied Oil Volatility Index (OVX) has been considered among the influencing parameters of the crude price forecast. The final set of influencing parameters were selected as the whole set of significant contributors detected by the methods of Bayesian Network, Quantile Regression with Lasso penalty (QRL), Bayesian Lasso (BLasso) and the Bayesian Ridge Regression (BRR). The performance of the proposed hybrid-BN method is reported for the three crude price benchmarks: West Texas Intermediate, Brent Crude and the OPEC Reference Basket.

Oil price volatility forecasts have recently attracted the attention of many studies in the energy finance field. The literature mainly concentrates its attention on the use of daily data, using GARCH-type models. It is only recently that efforts to use more informative intraday data to forecast oil price realized volatility have been made. Despite all these previous efforts, no study has examined the usefulness of futures-based models for oil price realized volatility forecasting, although the use of such models is extensive for oil price predictions. This study fills this void and shows that futures-based forecasts based on intra-day data\ud provide informative forecasts for horizons that span between 1-day and 66-days ahead. More importantly, these results hold true even during turbulent times for the oil market, such as\ud the Global Financial Crisis of 2007-09 and the oil collapse period of 2014-15.

The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.