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This paper examines the frequency of spillovers between crude oil futures and the Middle East and North Africa (MENA) stock markets. We use the methodologies proposed by Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) and the wavelet coherency approach. The results show time-varying volatility spillovers in the considered markets. The short-term spillovers are higher than their intermediate-term counterparts. The highest jump in spillovers occurs during the COVID-19 outbreak, followed by the global financial crisis and the recent oil price crash. The spillovers are higher for oil-exporting countries than oil-importing countries. Saudi Arabia, Qatar, and the United Arab Emirates (UAE) are the main contributors to spillovers in the short and intermediate terms. Brent oil, Egypt, Morocco, and Turkey are the net receivers of spillovers in the short term, and they switch to become net contributors to spillovers in the intermediate term. Turkey and oil-exporting stock markets receive more spillovers than oil-importing stock markets irrespective of the time frequency. Wavelet analysis shows evidence of co-movements between oil futures and stock markets at intermediate and low frequencies. The lead–lag relationships between crude oil and stock markets are mixed and time-varying. Moreover, a mixed portfolio offers diversification benefits. Hedging is more expensive during the pandemic period and particularly in the intermediate term compared to the short term. Hedging effectiveness is highest during the COVID-19 pandemic in the short and intermediate terms for almost all markets. Refereed/Peer-reviewed

In this paper, we propose a new framework for modeling commodity forward curves. The proposed model describes the dynamics of fundamental driving factors simultaneously under physical ( P ) and risk-neutral ( Q ) probability measures. Our model is an extension of the forward curve model by Borovkova and Geman (2007), into several directions. It is a three-factor model, incorporating the synthetic spot price, based on liquidly traded futures, stochastic level of mean reversion and an analog of the stochastic convenience yield. We develop an innovative calibration mechanism based on the Kalman filtering technique and apply it to a large set of Brent oil futures. Additionally, we investigate properties of the time-dependent market price of risk in oil markets. We apply the proposed modeling framework to derivatives pricing, risk management and counterparty credit risk. Finally, we outline a way of adjusting the proposed model to account for negative oil futures prices observed recently due to coronavirus pandemic.

This paper examines the volatility transmission between crude oil and four precious metals (i.e., gold, silver, platinum, and palladium) and investigates whether oil can be considered as a hedge or safe-haven asset against four precious metals. Our empirical analysis reveals several important findings. First, we determine that the volatility transmission was time-varying and that influence from the Asian crisis, the bursting of the dot-com bubble, the 2008 global financial crisis, the recent oil-price crash, and COVID-19 alternated between negative and positive values over the entire studied period. We further conclude that Brent oil is a diversifier and a weak safe haven for precious metals; and thus, that a combined portfolio composed of Brent-oil and precious-metals futures yields better hedging effectiveness. These findings indicate that oil futures are a useful investment that reduces downside risks and strengthens diversification benefits in portfolio risk management. Refereed/Peer-reviewed

The reduction in oil prices might make crude oil a cheaper alternative to renewable energy (RE). Given this, the present paper examines the effect of fluctuation of oil prices on the use of RE in the United States (US) during the period 1970 to 2018. We constructed two nonlinear autoregressive distributed lag (NARDL) models to examine the effect of the positive and negative oil price shocks on the use of RE in the US. The RE consumption is taken as the dependent variable and the gross domestic product (GDP), Brent crude prices, population density, trade openness, and price index as independent variables. The result revealed that the rise in crude oil price, GDP, and population density will increase RE use in the short run and in the long run as well. Moreover, the study finds that any decrease in oil prices will decrease RE use in the short run and its effect will eventually diminish in the long run. On the policy front, it is suggested that US should raise its energy security by reducing its dependency on imported crude oil and increase the role of RE through the imposition of taxes on oil and increase the base of production and consumption through a series of measures.

There is an ongoing scientific debate on how gold and crude oil affect each other prices. It is of high importance as both of them are strategic assets. The aim of the study is to check whether prices of these two assets influence each other. If so, if this is a short-term or a long-term relation and what the causality between these assets prices is. Daily data from January 2005 to December 2020 are used. The author applies Johansen cointegration test, Granger causality test and VAR model, denies a long-term and confirms a short term relation between gold and crude oil prices. However, it goes only in one direction that is from gold to crude oil. Such an interaction has significant consequences for investors, traders, producers, authorities, policymakers.Keywords: VAR, gold, crude oil price, Granger causalityJEL Classifications: G15, C51, F37DOI: https://doi.org/10.32479/ijeep.11229

Investigating the co-movements between crude oil futures helps to understand the integration of the global markets. This paper focuses on Shanghai crude oil futures (INE) and study its co-movements with the international benchmarks of WTI and Brent crude oil futures in intra-day day and night trading sessions. A complex network model framework is proposed to analyse the intra-day co-movement patterns labelled by a functional data clustering approach on intra-day return curves. Our findings indicate INE is more integrated with the global market during the night session, but it shows a regional fractional effect during the day session. Based on the revealed dynamics of co-movement patterns, we further design a pairs trading strategy between INE crude oil futures and the international benchmarks. The simulation results show that the pairs trading strategy can be promisingly profitable, even during market turmoil phases.

The human mind is not as good at processing large amounts of information as we might like. Psychologists have shown that human beings are only able to juggle small numbers of related and often conflicting pieces of information without making judgment errors. As a result, individuals faced with the vast amounts of information available to support investment decisions often find themselves swamped by the enormity of the task; unable to see the wood from the trees. Technical analysis is a field of financial markets research that works to address the above problem by focusing on a single, commonly available, data source that reflects all known information and activity relating to all monetary securities- Price history. Technical analysts argue that as markets are efficient, prices reflect all known information and that they move over time as participants react to new information and changing needs. As a result, the technical analysis of these price changes can provide real insight into the market dynamics and be used to develop trade strategies that exhibit superior risk/reward characteristics. While technical analysis approaches have developed significantly over the past few decades, some techniques are far more ancient. While their real origins are anonymous, Japanese candlestick charts have been recorded as being employed in the rice markets as far back as the 1600s. What is particularly interesting is that various of these ancient approaches continue to provide highly effective trading signals when applied to modern markets and securities. Crude oil price volatility is in the midst of the largest business risk that oil and gas companies face. This is followed by unstable policy regime, managing costs and risks emerging from technological advancements. The high levels and rapid fluctuations of petroleum prices have become a great concern to individual consumers, firms, policy makers and society. Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is "likely" to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. Hence, to mitigate the negative impacts of price volatility and to predict about the future price movement of crude oil and natural gas we can use technical analysis. Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting price trends. The term “market action” includes the three principal source of action available to the technician-price, volume and open interest. This research paper highlights fundamental factor which affects the Brent price and analysed the factor which are highly correlated with Brent price and on the basis of the results forecasted the Brent price for next five years. Fundamental analysis of Brent oil, price pattern & movement of crude oil has also been carried out using candlestick technical tool.

The present article deals with intra-horizon risk in models with jumps. Our general understanding of intra-horizon risk is along the lines of the approach taken in Boudoukh et al. (2004); Rossello (2008); Bhattacharyya et al. (2009); Bakshi and Panayotov (2010); and Leippold and Vasiljevic (2020). In particular, we believe that quantifying market risk by strictly relying on point-in-time measures cannot be deemed a satisfactory approach in general. Instead, we argue that complementing this approach by studying measures of risk that capture the magnitude of losses potentially incurred at any time of a trading horizon is necessary when dealing with (m)any financial position(s). To address this issue, we propose an intra-horizon analogue of the expected shortfall for general profit and loss processes and discuss its key properties. Our intra-horizon expected shortfall is well-defined for (m)any popular class(es) of Levy processes encountered when modeling market dynamics and constitutes a coherent measure of risk, as introduced in Cheridito et al. (2004). On the computational side, we provide a simple method to derive the intra-horizon risk inherent to popular Levy dynamics. Our general technique relies on results for maturity-randomized first-passage probabilities and allows for a derivation of diffusion and single jump risk contributions. These theoretical results are complemented with an empirical analysis, where popular Levy dynamics are calibrated to the S&P 500 index and Brent crude oil data, and an analysis of the resulting intra-horizon risk is presented.

We investigate whether crude oil markets are weak-form efficient during different economic cycles over multi-scales using multifractal detrended fluctuation analysis. Using crude oil prices from 1996 to 2018, our results for different sub-samples provide evidence that in the shorter horizon, the efficiency of prices tends to improve across each post-recession period of expansion. However, in the longer horizon, we do not observe such improvements in efficiency during recovery after recessions, especially during the global recovery after 2010. Overall, we find that the benchmark Brent crude oil prices are weak-form efficient, which implies that prices are largely unpredictable. We reevaluate our results using weekly data and rolling time windows. The findings remain robust to both tests and the use of eight oil price benchmarks.

The Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to study the volatility of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which is the primary objective of this study. The other goal of this paper is to expand on the researchers’ previous work by examining long memory and volatilities simultaneously, by using the ARFIMA-sGARCH hybrid model and comparing it against the ARFIMA-fGARCH hybrid model. Consequently, the hybrid models were configured with the monthly Brent crude oil price series for the period from January 1979 to July 2019. These datasets were considered as the global economy is currently facing significant challenges resulting from noticeable volatilities, especially in terms of the Brent crude prices, due to the outbreak of COVID-19. To achieve these goals, an R/S analysis was performed and the aggregated variance and the Higuchi methods were applied to test for the presence of long memory in the dataset. Furthermore, four breaks have been detected: in 1986, 1999, 2005, and 2013 using the Bayes information criterion. In the further section of the paper, the Hurst Exponent and Geweke-Porter-Hudak (GPH) methods were used to estimate the values of fractional differences. Thus, some ARFIMA models were identified using AIC (Akaike Information Criterion), BIC (Schwartz Bayesian Information Criterion), AICc (corrected AIC), and the RMSE (Root Mean Squared Error). In result, the following conclusions were reached: the ARFIMA(2,0.3589648,2)-sGARCH(1,1) model and the ARFIMA(2,0.3589648,2)-fGARCH(1,1) model under normal distribution proved to be the best models, demonstrating the smallest values for these criteria. The calculations conducted herein show that the two models are of the same accuracy level in terms of the RMSE value, which equals 0.08808882, and it is this result that distinguishes our study. In conclusion, these models can be used to predict oil prices more accurately than others.

We develop an index of uncertainty, the COVID-19 induced uncertainty (CIU) index, and employ it to empirically examine the vulnerability of energy prices amidst the COVID-19 pandemic using a distributed lag model that jointly accounts for conditional heteroscedasticity, autocorrelation, persistence, and structural breaks, as well as day-of-the-week effect. The nexus between energy returns and uncertainty index is analyzed, using daily price returns of eight energy sources (Brent oil, diesel, gasoline, heating oil, kerosene, natural gas, propane, and WTI oil) and four news/information-based uncertainty proxies [CIU, EPU, Global Fear Index (GFI) and VIX]. The CIU and alternative indexes are used, respectively for the main estimation and sensitivity analysis. We show the outperformance of CIU over alternative news uncertainty proxies in the prediction of energy prices. News (aggregate) and bad news are found to negatively and significantly impact energy returns, while good news has a significantly positive impact. Imperatively, energy variables lack hedging potentials against the uncertainty occasioned by the COVID-19 pandemic, while we find no strong evidence of asymmetry. Our results are robust to the choice of news variables, forecast horizons employed, with likely sensitivity to energy prices.

Oil still contributes around 30 % to Russia's commodity export earnings, therefore the impact of oil prices on Ruble's exchange rate is of current interest to Russian economists. Instruments of time series analysis were used to test a proposition that the Russian ruble’s exchange rate has become less dependent on Brent crude oil price in recent years. We obtained 1,095 weekly observations for years 2000 to 2020 were obtained from FINAM company website. Our empirical model is a linear regression of the ruble’s exchange rate on Brent crude oil price. The Bai-Perron test has identified three structural breaks in the data corresponding to four chronological regimes. The Engle-Granger cointegration test has found both the rate and the price to be non-stationary in all regimes while cointegration was found only in the third regime (September 12, 2011 – October 23, 2017). The main reasons for collapse of cointegration in the fourth regime (October 30, 2017 – December 28, 2020) are 1) successful efforts by oil-producing countries to curb oil production, 2) decline in Russian import of goods and services, 3) Bank of Russia’s contractionary monetary policy, 4) built-in exchange rate stabilizer activated by the budget rule, and 5) anti-Russian sanctions. Cointegration, as it turns out, comes and goes.

We apply autoregressive distributed lag regression (ARDL) and several methods of structural break analysis on a daily data set between 1995 and 2014 to explore various supply and demand factors as drivers of the price differential between WTI and Brent crude oil. In line with previous literature, we identify a major break in the WTI-Brent spread in December 2010. The ARDL regression reveals that the convenience yield, as a proxy for crude oil inventories, is the most important spread determinant. Moreover, also the trading activity in crude oil paper markets, shipping costs, as well as the stock market development in the US and Europe affect the size of the spread. Unlike other papers, we find that the impact of the spread determinants changed after the break in 2010. Especially, the impact of local WTI inventories as well as the influence of paper markets activity on physical trading in crude oil spot markets have gained in importance. In summary, the rising variability in the spread time series after 2010, which reflects a decoupling process of WTI and Brent, can be explained by an absolute increase in several economic determinants.

In this study, we examine the nexus between crude oil prices, clean energy investments, technology companies, and energy democracy. Our dataset incorporates four variables which are S & P Global Clean Energy Index (SPClean), Brent crude oil futures (Brent), CBOE Volatility Index (VIX), and NASDAQ 100 Technology Sector (DXNT) daily prices between 2009 and 2021. The novelty of our study is that we included technology development and market fear as important factors and assess their impact on clean energy investments. DCC-GARCH models are utilized to analyze the spillover impact of market fear, oil prices, and technology company stock returns to clean energy investments. According to our findings when oil prices decrease, the volatility index usually responds by increasing which means that the market is afraid of oil price surges. Renewable investments also tend to decrease in that period following the oil price trend. Moreover, a positive relationship between technology stocks and renewable energy stock returns also exists.

Purpose This study aims to examine the effect of the COVID-19 pandemic in comparison to the global financial crisis (GFC) on the gross domestic product (GDP) growth rate of China. Design/methodology/approach Empirical analyses are conducted through alternative methods such as ordinary least squares, Markov regime switching (MRS) and mixed data sampling (MIDAS) regressions. The flexibility of MIDAS regression enables us to use different variables with quarterly (GDP), monthly (export sales and foreign-exchange reserves) and daily frequencies (foreign exchange rates and Brent oil price). Findings The results indicate that the COVID-19 pandemic has had a considerable negative effect on China’s GDP growth, while the dummy variables used for the GFC are found to be insignificant. Further, the forecast accuracy test statistics exhibited a superior performance from MIDAS regression compared to the alternative models, such as MRS regression analysis. According to the forecast results, the authors expect a recovery in China’s economic growth in the second quarter of 2020. Originality/value This is one of the earliest studies to examine the effect of the COVID-19 pandemic on the Chinese economy, and to compare the impact of COVID-19 with the GFC. The authors provide further evidence regarding the performance of MIDAS regression analysis vs alternative methods. Findings obtained shed light on policymakers, corporations and households to update their consumption, saving and investment decisions in the chaotic environment of this pandemic.

This paper analyses the performance of commodity cross-hedging of aviation turbine fuel (ATF) price exposures with crude oil and Brent oil futures for the Indian aviation industry. Models that were estimated using three alternative techniques of ordinary least squares (OLS), error correction models (ECMs), and autoregressive conditional heteroskedastic (ARCH) showed that Brent crude oil futures had the highest cross-hedging efficiency. Further, the variances of the profit and loss (P&L) series and value at risk (VAR) associated with alternative hedging strategies – including a composite hedge of crude oil and Brent oil futures – showed that although hedging is redundant for domestic operations, composite hedging for imported ATF prices could substantially lower the VAR compared to all other alternatives from imported and domestic operations.

Coronavirus (2019-nCoV) not only has an effect on human health but also on economic variables in countries around the world. Coronavirus has an effect on the price of black gold and on its volatility. The shock on all markets is already very strong. Volatility patterns in Brent crude oil simulation are examined during covid-19 crisis that significantly affected the oil market volatility. The selected crisis of coronavirus arose due to different triggers having diverse implications for oil returns volatility. Our findings indicate that model choice with data modeling is the same appropriate model EGARCH(0,2) with different parameters between pre-coronavirus and post-coronavirus. We find that oil prices are the most strongly and negatively influenced by the Coronavirus crisis. The downward movement post-covid-19 crisis is very noticeable in energy volatility. The return series, on the other hand, do not appear smooth, they rather appear volatile. We conduct a Monte Carlo simulation exercise during coronavirus crisis to investigate whether this decline is real or an artefact of the oil market. Our findings support the fact that the decline in oil prices volatility is an artefact of the covid-19 crisis.Keywords: Oil Returns Conditional Volatility, Coronavirus Crisis, Univariate GARCH Models, Mean Equation, Variance Equation, Monte Carlo Simulation.JEL Classifications: Q43, E44, C1, I15, C15DOI: https://doi.org/10.32479/ijeep.10374

This paper investigates the volatility transmission effect between Brent oil futures and stock markets in the major global oil producing and consuming countries – the U.S., Russia, China and Saudi Arabia. In that process, we employ a mixture of novel and elaborate methodologies – wavelet signal decomposing procedure, GARCH model with complex distribution and recently developed robust quantile regression. Our results indicate that the effect is stronger in short-term horizon than in midterm and long-term in most cases. The magnitude is much stronger in turbulent times, whereas in tranquil times, this effect is very weak. We find that Russian RTS index endures the strongest volatility transmission effect from oil market. Surprisingly, Saudi stock market does not suffer heavy spillover effect even in the periods of increased market unrest. In the U.S. and China, the effect is much stronger from stocks to oil than viceversa, and this particularly applies for the U.S. case. info:eu-repo/semantics/publishedVersion

Applying recently developed Diebold and Yilmaz (2012) spillover index, this paper investigates the oil–stocks returns and volatility connectedness with weekly data between January 14, 2000 and March 31, 2017. For the purpose of robustness, we have both used WTI and Brent oil prices. Sensitivity of overall spilover index is also examined using different lag‐structures and different forecast horizons. The empirical results are qualitatively similar either when WTI or Brent oil prices are used to examine the spillover amongst series under consideration. Specifically, the spillover index value for WTI and Brent, respectively, is 71.60 and 72.32%. We show that total spillover connectedness, as measured by a rolling‐window approach, has dynamic and volatile characteristics in returns and volatility series across major markets. Last but not least, we find from the net spillover analysis that NYK, SPTSX, IBOV, MICEX, SENSEX, Shanghai, TOP40 and WTI stock markets are net receiver of return spillover, whereas SPX, DAX, UKX, FTSEMIB and CAC40 are the net contributors.

This paper investigates the idiosyncratic volatility spillover effect from the Brent oil futures market to the 11 stock markets of Central and Eastern European economies. As volatility proxies, we use regime‐switching conditional volatilities, obtained from two‐states MS‐GARCH model. In order to determine the level of this effect in different market conditions and in different time‐horizons, we combine wavelet methodology with the quantile regression approach. Our results indicate that the volatility spillover effect is not particularly strong across the countries and the wavelet scales, except in those conditions when stock market volatility is exceptionally high. Also, the wavelet‐based quantile parameters report that the volatility transmission effect gradually subsides with the flow of time, and it applies for the majority of the indices. Romanian BET index experiences the strongest volatility spillover effect from oil in conditions when Romanian stock market is under extreme stress. The reason for this finding probably lies in the facts that Romania is the largest oil and gas producer among all CEECs, and oil and gas markets tend to comove strongly. Based on findings of wavelet quantile parameters and wavelet correlations, we can conclude that hedgers and portfolio managers can build their portfolio strategies, combining Brent oil futures with the CEE indices.

We investigate the directional volatility and return network connectedness among stock, commodity, bond, currency and cryptocurrency markets. The period of study covers Feb 2006 until August 2018. We utilize and expand Diebold and Yilmaz (2014 2015) connectedness measurement; accordingly, in the variance decomposition structure, we use Hierarchical Vector Autoregression (HVAR) to estimate high dimensional networks more accurately. Our empirical results show that markets are highly connected, especially during 2008–2009. Asian stock markets are the net receiver of shocks, while European and American stock markets are the net transmitter of shocks to other markets. The pairwise connectedness results suggest that among stock markets, DAX-CAC 40, FTSE 100-CAC 40 and S&P 500-S&P_TSX index are more integrated through connectedness than the others. For other markets, WTI crude oil — Brent crude oil, 30-Year bond and 10-Year bond, Dollar Index futures-EUR/USD have notable connections. In terms of cryptocurrencies, they contribute insignificantly to other markets and are highly integrated with each other. Gold and cryptocurrencies seem to be good choices for investors to hedge during a crisis.

The shale gas developments over the last two decades have challenged the gas price linkage with crude oil. The decoupling of the US wholesale gas from oil markets is mainly attributed to the rapid development of unconventional production, which formed a regional natural gas market based on regional market fundamentals. Moreover, investments in exporting facilities in the US made more quantities available to the rest of the world making global integration more plausible. This paper provides empirical evidence on the price and volatility transmission among the main European (NBP and TTF) and the Japan-Korean Marker (JKM) gas markets with that of Brent crude oil market, a crude oil benchmark used in Europe and Asia. The paper provides evidence that there are no price spillovers among oil and gas in European gas hubs. The European markets, contrary to the JKM market, seem to be mature enough as in the case of the US gas market. Finally, the paper provides policy recommendations on key elements for establishing functional gas hubs.Keywords: natural gas and oil markets; price and volatility spillovers; Europe, JapanJEL Classifications: Q40, Q41, C5DOI: https://doi.org/10.32479/ijeep.9774

The sector performances of many economies especially the oil-producing ones, are consistently linked with the volatility of the international crude oil prices. Considering the significance of the international crude oil price to the economy of the Saudi Arabia, the current study investigates the dynamics of the crude oil price and the country's real estate market. This is achieved by investigating the evidence of both speculative bubbles and regime switching in the crude oil and real estate market from October 3, 2005, to March 29, 2018. As such, the Supremum Augmented Dickey-Fuller (SADF) and Markov switching regression model were employed. Although there is no significant evidence of speculative bubbles in the Saudi Arabia's real estate market, the result reveals statistical significant evidence of price bubbles in the Brent crude oil price especially between the period of 2007 to 2008, which coincides with the Global Financial Crisis period. However, the Markov switching investigation reveal that there is evidence of significant and positive impact of crude oil price return on the real estate market in the regimes. Importantly, empirical evidence shows that the impact is higher in Regime 2 even as the global factor (proxy volatility index) is evidently significant. Although the regimes are persistent, the expected regime duration of the stable regime is of the higher quarter. This investigation is statistically significant and robust, especially when Organization of the Petroleum Exporting Countries (OPEC) price of crude oil is used in lieu of the European Brent price. The estimation result encourages more strict energy policies that tackle the Saudi Arabia's energy and the real estate challenges.

The panel vector autoregression model is estimated using three main variables related to with profitability, financial liquidity, and financial leverage for 94 manufacturing companies from 2000 to 2017 in Indonesia. The aim is to examine the impact of oil price shocks on the ROA (profitability), CR (financial liquidity), and DER (financial leverage). The impulse reaction function of samples reveals some remarkable results. First, the response of ROA, DER, and CR appears to be consistent in many ways. Second, either Brent oil or WTI oil gives the same result for these variables. Third, financial liquidity for Indonesia manufacturing companies is not affected by the oil prices. The results obtained are robust following the GMM model in the estimation of the panel VAR.Keywords: Oil price shocks, Panel VAR, Impulse Reaction Function, GMM modelJEL Classifications: L6, Q4DOI: https://doi.org/10.32479/ijeep.9808

This study attempts to investigate how the spread of coronavirus (COVID-19) influence the UK economic policy uncertainty. Using daily data ranging from 11 March 2020 to 7 May 2020, an ARDL model has been applied in this study to capture both the short-run and long-run impact of COVID-19 on economic policy uncertainty. Additionally, the influence of Brent oil price on economic policy uncertainty is also examined. Based on the ARDL result, it is found that both COVID-19 new cases and new deaths reported in the UK have a strong and positive association with the UK economic policy uncertainty in the long-run. However, Brent oil price shows an inverse connection with economic policy uncertainty in the long run.

In order to test whether Chinese crude oil futures (INE) has already played the role of futures market and whether it has had a significant impact on international benchmark market, we construct the permanent temporary model and Information Share model based on 15 min of high‐frequency trading data from March 26, 2018 to October 30, 2018 to inspect the proportions of new information in INE and Brent markets, and use the Garbade‐Silber model to measure the risk transfer effect. Furthermore, the generalised spillover index is proposed to examine the effects of return and volatility spillovers among INE, WTI and Brent futures markets. The results reveal that: firstly, during the sample period, INE is not yet a promoter of international benchmark crude oil prices, but more obvious followers. Secondly, although INE has begun to display the price discovery function, it is weaker than that of Brent, and the risk transfer function between them does not appear strong. Finally, INE market mainly acts as a net transmitter of return spillover before August 2018, but it has almost always been the net transmitter of volatility spillover during the full sample period. These findings are of interest to policy makers as well as investors for risk hedging and asset allocation of crude oil portfolios.

Since the first case was reported at the end of 2019, COVID-19 has spread throughout the world resulting in more than 2 million confirmed cases. The World Health Organization (WHO) also declared the COVID-19 disease as pandemic on 11 March 2020. The COVID-19 pandemic has also affected the global financial market, which includes Malaysia. This study aims to investigate the impact of the COVID-19 outbreak on the Malaysian stock market. The dependent variables used in this study were the Kuala Lumpur Composite Index (KLCI) and 13 other sectorial indices. The independent variables were (i) the number of COVID-19 cases in Malaysia, China, and USA; (ii) the number of deaths due to COVID-19 in Malaysia, China, and USA; (iii) the volatility index, and (iv) the Brent oil price. The sample period of this study covered from 31st December 2019 to 18th April 2020. The findings showed that higher numbers of COVID-19 cases in Malaysia tended to adversely affect the performance of the KLCI index and all sectorial indices, except for the Real Estate Investment Fund (REIT) index. The results also showed that the Brent oil price and the volatility index tended to affect the Malaysian stock market performance. The results of this study can help investors understand the impact of COVID-19 on different sectors in Malaysia.

This paper stresses the bimodality of the widely used Student's t likelihood function applied in modelling Mixed causal-noncausal AutoRegressions (MAR). It first shows that a local maximum is very often to be found in addition to the global Maximum Likelihood Estimator (MLE), and that standard estimation algorithms could end up in this local maximum. It then shows that the issue becomes more salient as the causal root of the process approaches unity from below. The consequences are important as the local maximum estimated roots are typically interchanged, attributing the noncausal one to the causal component and vice-versa, which severely changes the interpretation of the results. The properties of unit root tests based on this Student's t MLE of the backward root are obviously affected as well. To circumvent this issues, this paper proposes an estimation strategy which i) increases noticeably the probability to end up in the global MLE and ii) retains the maximum relevant for the unit root test against a MAR stationary alternative. An application to Brent crude oil price illustrates the relevance of the proposed approach.

This study investigates the relationship between oil price fluctuations and renewable energy stock returns using daily data on Brent crude oil prices and global renewable energy stock market indices between 29 November 2010 and 18 February 2020. The investigation is based on the existing evidence on positive correlations between stock prices and oil prices, but it also considers the shift from non-renewable to renewable sources of energy. A two-stage GARCH(1,1) model and a Granger causality test were applied. Our results show that volatility clustering is present in the renewable energy companies‘ stock prices, but, oil price volatility does not seem to induce any significant effects on returns‘ volatility. This might suggest that oil markets and renewable energy markets are rather disconnected, which means that the development of renewable energy businesses is less affected by potential shocks in the oil prices and markets. As a result, the exposure of companies and entrepreneurs in the renewable sector to an important source of macroeconomic volatility is reduced.

Purpose – This paper aims to provide an important perspective to the predictive capacity of Organization of the Petroleum Exporting Countries (OPEC) meeting dates and production announcements for energy futures (crude oil West Texas Intermediate (WTI), gasoline reformulated gasoline blendstock for oxygen blending (RBOB), Brent oil, London gas oil, natural gas and heating oil) market returns and volatilities. Design/methodology/approach – To examine the impact of OPEC news on energy futures market returns and volatilities, the authors use a conditional quantile regression methodology during the period from April 01, 2013 to June 30, 2017. Findings – From the empirical findings, the authors show a conditional dependence between energy futures returns and OPEC-based predictors; hence, the authors can find clear the significance of relationship in the process of financialization of the OPEC announcements and energy futures in the case of this paper. From the quantile-causality test, the authors find that the effect of OPEC news is important to energy futures. Specifically, OPEC announcements dates predict the quantiles of the conditional distribution of energy futures market returns. Originality/value – The authors confirm the presence of unidirectional nexus between OPEC news and energy commodities futures in the long term. Proposito – Este documento tiene como objetivo proporcionar una perspectiva importante de la capacidad predictiva de las fechas de reunion de la Organizacion de Paises Exportadores de Petroleo (OPEP) y los anuncios de produccion de energia —petroleo crudo (WTI), mezcla de gasolina reformulada para mezcla de oxigeno (RBOB), Brent oil, London gas oil, gas natural y fuel oil— retornos y volatilidades del mercado. Diseno/metodologia/enfoque – Para examinar el impacto de los anuncios de la OPEP en los rendimientos y volatilidades del mercado de futuros de energia, los autores utilizan una metodologia de regresion condicional basada en cuantiles durante el periodo comprendido entre el 1 de abril de 2013 y el 30 de junio de 2017. Hallazgos – A partir de los hallazgos empiricos, los autores muestran una dependencia condicional entre los rendimientos de los futuros de energia y los predictores basados en la OPEP; por lo tanto, pueden encontrar clara la importancia de la relacion en el proceso de los anuncios de la OPEP y los futuros de energia. A partir de la prueba de causalidad de cuantiles, se observa que el efecto de los anuncios de la OPEP es importante para los futuros de la energia. Especificamente, las fechas de los anuncios predicen los cuantiles de la distribucion condicional de los rendimientos del mercado de futuros de energia. Originalidad/valor – Se confirma la presencia de un nexo unidireccional entre los anuncios de la OPEP y los futuros de las materias primas energeticas a largo plazo.

This study examines how returns and volatility of future contracts for Brent crude oil (Brent), West Texas Intermediate crude oil (WTI), Henry Hub natural gas, and Newcastle thermal coal impacts industries in China. Using the firm‐level data of 3,750 stock listings across both Shanghai and Shenzhen stock exchanges, segregated into 138 subindustries under the Global Industry Classification Standard, this study finds evidence that crude oil futures have the most significant influence. Further analysis suggests that stock returns of oil‐related companies are more closely align to Brent and WTI's futures returns following China's key oil pricing reform on March 27, 2013. Overall, Chinese industries are also more exposed to global crude oil futures volatility after this event.

Purpose The purpose of this paper is to examine whether oil price risk is a significant determinant of stock returns. Design/methodology/approach Using monthly data on a sample of Islamic stocks listed on the New York Stock Exchanges and National Association of Securities Dealers Automated Quotations System (NASDAQ) over the period from January 1990 to December 2017, the study examines whether oil price risk is a significant determinant of stock returns using Fama–French–Carhart’s four-factor asset pricing model amplified with Brent oil price factor. Findings The results from the cross-sectional regression analysis indicate that the extent of the exposure is significantly positive using a full sample period. Moreover, results from size and momentum factors are highly significant whereas book-to-market has no significant impact on Islamic stock returns. Research limitations/implications The results support the concept for diversification in equity investment and are thus important for investors, analysts and policymakers. Originality/value This study is the first of its kind to establish whether oil price risk is a factor that can determine returns of Islamic listed stocks using the most developed stock market in the world (New York Stock Exchanges and NASDAQ).

This article deals with an analysis of the relationship between the Brent oil price and the US dollar price. This paper analyses the development of the intensity and direction of dependence between the nominal effective exchange rate of the US dollar and the price of Brent oil and other commodities, such as industrial metals, agricultural commodities, gold (including jewellery and platinum) in the period from January 1994 to April 2018. The next section tests the hypothesis that there is a short-term relationship between the effective US dollar exchange rate and the oil price. The last part of the article deals with the question whether there is a long-term relationship between these variables: Brent oil price, effective exchange rate of the US dollar, industrial production index in OECD countries, three-month treasury bill, US ending stocks of crude oil, US percent utilization of refinery operable capacity and the price of ethanol.

Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers. This paper estimates a variety of multivariate GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of Coffee Arabica and compares the forecasting performance of these models based on high-frequency intraday data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance. The analysis points to the conclusion that the varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of MGARCH models to pay particular attention to the measurement of realized volatility and employ high-frequency data whenever feasible.

We examine return and volatility transmission between the newly established crude oil futures in China and international major crude oil futures markets using intraday data. For the first time, we document evidence for cointegration relationships among these oil futures markets. Both China's and Oman's oil futures markets react to deviations from their long‐run equilibrium with West Texas Intermediate and Brent oil futures. There is also new evidence for asymmetric volatilities and correlations across these oil futures markets. Furthermore, the Chinese oil futures have stronger linkages with the international major futures markets than Oman futures.

Since 2014, the Russian stock market has been under pressure due to both sanctions and a sharp drop in oil prices, which led to its increased volatility. This paper analyzes the impact of the price volatility of Brent oil and sanctions on the volatility of the Russian stock index RTS. Under volatility the paper understands both its parametric estimate obtained from the GARCH model estimation as well as non-parametric estimate — realized volatility. To estimate the effect of oil price volatility and sanctions, several cointegrated regressions were analyzed. The robustness of the results in relation to the choice of volatility assessment is demonstrated. The results show that RTS index volatility still depends on oil prices volatility in 2007—2018. This dependence is most pronounced in the periods of crisis. The paper also demonstrates the adjustment of the Russian stock market to the previous sanctions, which calls into question their long-term efficiency.

The study aims to examine the causality between industrial production index and crude oil price for Russia, Kazakhstan and Azerbaijan by using Frequency Domain Causality Analysis. For this purpose, the monthly data of the industrial production index and Brent oil price data over the period 1993-2019 are used. The Frequency Domain Causality Analysis suggests that the uni-directional causality relationship runs from oil prices to industrial production index is valid in the medium run for Russia and Azerbaijan and in the short run for Kazakhstan. However, there is no uni-directional causality linkage between oil prices and industrial production index in the long run for any of the countries. We hope to contribute to the literature by using frequency-domain causality test which examines the interrelation of crude oil prices on industrial production with the periodicity in these countries. The finding of this study is expected to serve as a tool for industrial production policy.

This paper tests the predictive power of crude oil price returns in forecasting Vietnamese stock index returns. We used the VN index and HNX index to calculate stock index returns and WTI and BRENT oil prices. Using a daily sample from 4th January 2006 to 31st December 2017, our analysis focused on both in-sample and out-of-sample predictability by applying the Westerlund and Narayan (2015) feasible generalized least square (FGLS) estimator which corrects persistency heteroskedasticity and endogeneity problems. We showed that the crude oil prices are reliable predictors of Vietnamese stock index returns. In terms of in-sample predictability, thirteen out of sixteen predictive regression were significant. We found that the BRENT crude oil index is slightly more powerful than the WTI crude oil price in predicting Vietnam stock index returns with seven out of eight regressions being significant compared to six out of eight from that of the WTI oil price. In terms of out-of-sample predictability, our results were also complemented by a robustness test, i.e. competing with a constant return model which used the historical average as the predictive value.

This paper investigates the relationship between prices of crude oil and its petroleum products such as Gasoline, Gas oil, Residual fuel oil (RFO) and premix fuel in Ghana. The monthly data of Brent crude oil and the prices of petroleum products for the period from January 2009 to June 2019 were used. The Autoregressive Distributed Lag (ARDL) Bounds cointegration test was employed to show the existence of a long run relationship between crude oil prices and the prices of petroleum products. An ARDL-based error correction model (ECM) was used to estimate the short and long run effect between the variables. Results from the cointegration test revealed the inexistence of a long run relationship between the prices of crude oil and premix fuel prices. It was established that while crude oil prices have both short and the long run effects on the prices of Gasoline, Gas oil and Residual fuel oil, inflation had significant positive effect on only the prices of residual fuel. Exchange rate had significant negative effects on the prices of Gasoline, Gasoil and Residual fuel oil in both the short and long run. Results from the Wald’s Granger Causality test indicated a uni-causal relationship running from Crude oil to Gasoline, Gas oil and RFO. There is no causal relationship between Inflation rate and Gasoline, Gas oil.

The banking industry is one of the main regulators of the economy; therefore, a possible decline in performance or risk to operations could trigger a chain of unexpected economic events. In consideration of this, this paper sought to evaluate the risk imposed by the oil and gas sector on the banking industry in the United Kingdom (UK) by evaluating the spillover effects and the exposure of the banking industry to shocks caused by changes in oil prices. In order to reach this objective, the present study evaluated the impact and effect of the volatility of bank stock prices and oil prices in four leading banks in the UK. These banks—HSBC, Royal Bank of Scotland, Lloyds Banking Group and Barclays PLC—were selected on the basis of their involvement in the oil and gas sector, and they were chosen to represent the volatility of the banking industry. The change in price of Brent crude oil was used as a representation of the volatility imposed by the oil industry. The vector autoregressive fractionally integrated moving average (VARFIMA) model was used to evaluate the impact of the volatility spillover and to evaluate the presence of co-volatility between certain parameters. The results showed volatility responses between the BSP and oil prices. The Granger causality analysis confirmed the presence of bidirectional causality between the volatility caused by oil prices and the stock prices of banks

Çalışmada, Borsa İstanbul Endeksi (BİST-100) ile JP Morgan Gelişmekte Olan Ülkeler Tahvil Endeksi - Index Global (EMBI), Dow Jones Borsası Endüstri Endeksi (DJI), Amerikan Dolar Endeksi (DXY), Chicago Opsiyon Borsası Oynaklık Endeksi-CBOE (VIX) ve ham petrol fiyatlarını temsilen Brent Petrol (BrP) volatilite etkileşimi incelenmiştir. Veriler, 30.09.2009-05.07.2018 dönemine ait günlük getiri serileri olup, ekonometrik model olarak çok değişkenli GARCH (Genelleştirilmiş Otoregresif Koşullu Değişen Varyans) modellerinden zamana bağlı değişen korelasyonu dikkate alan DCC-GARCH modeli kullanılmıştır. Bulgular, BİST-100 ve ele alınan değişkenler arasında volatilitenin sürekli etkilere sahip olduğunu ve bu piyasalarda yoğun şekilde volatilite kümelenmelerinin oluştuğunu göstermektedir. Ham Petrol ve EMBI volatilitesi BİST-100 endeks volatilitesini azaltırken diğer değişkenlerdeki volatiliteler, BİST-100 endeksindeki volatiliteyi arttırmaktadır. Ayrıca, DXY, BİST-100 endeksi volatilitesini en çok etkileyen değişken olduğu söylenebilir.