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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 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.

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.

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.

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.

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.

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.

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.

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.

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.

This paper examines the effect of US monetary policy, oil price and gold price on stock indices of BRICS countries. Vector Auto Regression model is applied to study the stock indices of all BRICS countries as a group over the period 1996–2018. We find that the Bombay Sensex responds positively to the Federal Funds Rate. The stock index of South Africa – FTSE JSE of Johannesburg – responds negatively to shocks in oil price while stock indices of Russia and Brazil – RTSI of Moscow and BVSP of Sao Paulo respectively – respond positively to gold price changes. We provide managerial and policy implications of these results.

The relationship between oil spot and futures prices has long been a focus of attention, but there is a lack of understanding about how these prices interact in times of crisis. Using cointegration and causality analysis, Brent crude spot and futures prices are examined before, during and after two different types of crises: (1) a supply-led event (the 1990–91 Gulf War) and (2) a demand-led event (the global financial crisis). Structural break analysis was employed to identify the subsamples to be studied. The core findings show evidence of a long-run bidirectional relationship between spot and futures prices. This study provides evidence that different types of crises engender different strengths of causal relationships between Brent crude spot and futures prices.

This paper investigates how the six major petroleum futures Oman crude, NYMEX RBOB gasoline, ICE low sulphur gasoil, ICE Brent crude, NYMEX light sweet crude and NYMEX NY Harbor ULSD (the new standard for highway diesel)) traded at three global key commodity exchanges are connected, using the novel Quantile VAR spillover approach. The study finds high degree of return connectedness between these markets, which increases as the size of the return shock increases at the 5th and 95th quantiles relative to the median 50th quantile. The global benchmark Brent crude futures traded at the Intercontinental Exchange (ICE) emerges as the lead petroleum futures irrespective of market conditions. NYMEX_RBOB_gasoline_futures emerges as a major receiver of return shocks from other oil futures market. The findings are validated in a time-varying framework, and robustness is also cross validated using the LASSO VAR spillover analysis. Policy implications of the findings are also discussed.

The current study emphasizes on the importance of developing an effective price risk management strategy for investment portfolios containing energy assets, given the high volatility of this market. A thorough investigation of energy price volatility is provided through the use of GARCH type model variations and the Markov-Switching GARCH methodology, as these are used in the most representative academic research. First, a large number of GARCH type models are exhibited together with all the econometric procedures and tests that are necessary for developing a robust and precise forecasting model for measuring energy price volatility. Next, follows a comprehensive examination of the probability of potential shifts in the unconditional variance of the models due to the effect of economic crises and several unexpected geopolitical events. Finally, taking into consideration the most relevant and up-to-date of the academic literature, a detailed comparison of the various volatility models and methodologies is presented. This is based on a variety of data samples containing the price returns of the main commercial energy commodities.

The present paper analyses the association among the fluctuations in global crude oil prices and the Dow Jones Islamic Stock Index by employing daily data from 1st January 1996 to 13th April 2018. The paper uses a novel approach of Wavelet-based Quantile-on-Quantile Regression Model to examine the influence of different quantiles of the decomposed time series of WTI Brent Crude Oil Prices on quantiles of Islamic stock index. The outcome of the study indicates the heterogeneity in the influence of global crude oil prices on Islamic Stock Index. The original time series data exerts the positive influence across all the quantiles. When we decompose the series, we find the positive influence starts decreasing and with the advent of stability in the time series of global crude oil prices, the negative effect becomes stronger. The analysis of the present study indicates that oil prices fluctuations may have a positive effect on Islamic stock index in short run, but on attaining stability, the oil prices exert a negative influence on the Islamic stock index.

This paper examines the predictive power of crypto-currencies and real time commodity futures for each other. The sample for crypto-currencies consists of Bitcoin and Ethereum, while that for real time commodity futures of Gold, Silver, Copper, Crude Oil, Brent Oil, Natural Gas and Wheat on daily basis, spanning the period February 2, 2012 to December 31, 2017. Given that the past evidence of a non-linear structure of cryptocurrencies and real time commodity futures ( Shahbaz et al., 2017 , Shahzad et al., 2017 ), the analysis uses the novel methodology of causality on quantiles, proposed by Balcilar et al. (2016) , for the case of a non-linear framework. The results highlight that significant causality runs from cryptocurrencies to commodity futures both in terms of mean and in volatility in the majority of the quantiles. These results carry substantial implications for investors, including both cryptocurrencies and commodity futures alone or along with traditional equities in a portfolio.

Previous studies have focused on the co-movements between the prices of different types of energy and, to some extent, the co-movements between the energy and financial assets prices, falling short of analysing the co-movements between the different types of energy and emission price. In this study, using the daily data from November 2007–31st October 2017 on quotes of Brent Crude oil and Natural Gas spot returns and quotes of the EU-ETS spots, we employed a time-varying copulas connection function to assess the risk dependency relationship between ETS and energy prices. The results show that there is an asymmetry dependence change rule between ETS, oil and gas spot index, with the correlation of the lower tail significantly higher than that of the upper tail. These findings indicate that, with the use of time-varying SJC Copulas model, economic agents can control investment risk and forecast abnormal fluctuations in oil prices

In this paper, we investigate causality and connectedness between economic policy uncertainty and oil price shocks across time scales. By incorporating the wavelet approach into the structural vector autoregression (VAR) framework proposed by Diebold and Yilmaz (2009, 2012, 2014), we find that crude oil prices behave like receivers of information from economic policy uncertainty, regardless of time scale. However, the causality relationship between economic policy uncertainty and oil price shocks intensifies as time scales increase. In addition, the connectedness relationship is robust to time scale changes, whereas the causal relationship intensifies as time scales increase. Notably, the weight of US economic policy uncertainty increases in the VAR system as time scales increase. In particular, we employ the West Texas Intermediate (WTI) crude oil price as an alternative measure to increase the robustness of our results and identify differences in the VAR system. Overall, the total connectedness of the WTI crude oil price is lower than that of the Brent crude oil price, regardless of time scale. Our results provide meaningful information for both investors and policymakers.

Can we accurately predict the Brent oil price? If so, which forecasting method can provide the most accurate forecasts? To unravel these questions, we aim at predicting the weekly Brent oil price growth rate by using several forecasting methods that are based on different approaches. Basically, we assess and compare the out-of-sample performances of linear parametric models (the ARIMA, the ARFIMA and the autoregressive model), a nonlinear parametric model (the GARCH-in-Mean model) and different nonparametric data-driven methods (a nonlinear autoregressive artificial neural network, genetic programming and the nearest-neighbor method). The results obtained show that (1) all methods are capable of predicting accurately both the value and the directional change in the Brent oil price, (2) there are no significant forecasting differences among the methods and (3) the volatility of the series could be an important factor to enhance our predictive ability.

Crude oil markets have been quite volatile and risky in the past few decades due to the large fluctuations of oil prices. We contribute to the current debate by testing for the existence of the leverage effect when considering daily spot returns in the WTI and Brent crude oil markets and by studying the direct impact of the leverage effect on measures of risk such as VaR and CVaR. More specifically, we model spot crude oil returns using Stochastic Volatility (SV) models with various distributions of the errors. We find that the introduction of the leverage effect in the traditional SV model with Normally distributed errors is capable of adequately estimating risk for speculative oil suppliers in both the WTI and Brent markets. Our results also show that financial regulators' model choice, both on the supply and on the demand side, would not be affected by the introduction of leverage. Focusing instead on firm's internal risk management, our results show that the introduction of leverage would be useful for firms who are on the demand side for oil, who use VaR for risk management and who are particularly worried about the magnitude of the losses exceeding VaR while wanting to minimize the opportunity cost of capital. Using the same logic, firms who are on the supply side would be better off not considering the leverage effect.

In the paper, we study the long-run relationship in the WTI-Brent oil time series, taking into account the occurrence of two relevant events: the rise of shale oil production, in early 2011, and the widening and closing of the WTI-Brent price spread, from 2011 to 2014. Monthly data of WTI and Brent crude oil prices, as well as US shale oil quantities from January 2000 to December 2017 is used for the analyses. The empirical results of the cointegration tests with structural breaks show that two structural break occurs, in February 2011 and in October 2014. We then estimate a Vector Error Correction Model (VECM), considering the structural break suggested by the cointegration test results, the timing of the rise in shale oil production and the dynamics of the WTI-Brent price spread. Our analysis reveals that WTI and Brent crude oil prices have had a long-run relationship up to 2011; no cointegration existed during the period of widening of the spread; again, a new long-run relationship arises after the closing of the gap, which includes the shale oil production. In the last period, the cross price elasticity of Brent on WTI slightly reduces compared to the pre-2011 era, whilst the shale oil production increases its importance in explaining the long-run relationship between WTI and Brent fivefold. Using the Generalized Impulse Response Functions (GIRFs) we finally study the impact of exogenous shocks on the variables, showing that in the first period, with limited shale oil production, oil prices reacted to shale oil and not vice versa. After October 2014, the opposite becomes true and shale oil production follows changes in both WTI and Brent prices.

The prediction of future crude oil prices is highly challenging due to three characteristics of crude oil prices, namely, their lag, nonlinearity, and interrelationship among different oil markets, which cannot be handled simultaneously by most traditional crude oil price forecasting models. This paper proposes a new hybrid vector error correction and nonlinear autoregressive neural network (VEC-NAR) model to deal with these characteristics simultaneously. Firstly, a VEC model is used to optimize the lag of crude oil prices and determine the interrelationship which distinguishes the endogenous and exogenous variables. Then, the optimal results obtained by the VEC model are combined with a NAR model which effectively depicts nonlinear component, to forecast crude oil prices. The data of Brent oil prices from January 1, 2003 to December 31, 2014 were used as the empirical sample to test the effectiveness of our proposed model which is compared with those well-recognized methods for crude oil price forecasting. The results of Diebold-Mariano test demonstrated that the VEC-NAR model provided superior forecasting accuracy to traditional models such as GARCH class models, VAR, VEC and NAR model in multi-step ahead short-term forecast.

We develop a jump-diffusion model for pricing and hedging with margined options on futures. Unlike a standard equity option, margined options require no up-front payment. An attractive feature of margined options is that there is no early exercise premiums under general assumptions. Model parameter estimates and out-of-sample pricing errors are calculated using data on Brent crude contracts. Using the same pricing technology, we also hedge equity style options with margined options. Hedging coefficients are derived by matching an extended set of Greeks. We find that a target equity option can be effectively hedged using a portfolio of two margined options and the underlying. As has been reported elsewhere, a delta hedge is inappropriate when the underlying is a jump-diffusion.

We investigate oil market price dynamics in the context of the Mixture of Distributions Hypothesis (MDH). Our econometric model addresses autoregressive properties in returns, the impact of surprise volume and conditional oil market return volatility as well as oil market liquidity in the conditional return equation. Surprise volume as a proxy of private information flow is shown to be unrelated to a set of standard market liquidity proxies. Oil return heteroscedasticity is found to be partly explained by surprise volume, a finding that is consistent with the MDH. Our findings further show that both oil market liquidity as well as surprise volume shocks are priced in the oil market. As such, lower levels of lagged market liquidity relate to above average conditional returns. Surprise volume shocks are associated with lower conditional oil market returns jointly with higher contemporaneous conditional return volatility. Lagged market liquidity dominates conditional volatility in predicting conditional oil price returns.

This paper examines the frequency dynamics of volatility spillovers between Brent crude oil and stock markets in the US (S&P500 index), Europe (STOXX600 index), Asia (Dow Jones Asia index) and stock markets of five vulnerable European Union (EU) countries known as the GIPSI (Greece, Ireland, Portugal, Spain, and Italy). We use the methodologies developed by Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) and the in-sample VaR performance to achieve our objectives. The results show that the spillover effect between the oil and the considered stock markets is time-varying, crisis-sensitive, and frequency-dependent. In addition, this effect had intensified during the onset of the 2008–2009 Global Financial Crisis, the plunge of oil prices that started in the mid-2014, and the occurrence of COVID-19. Furthermore, the results also demonstrate that oil is a net receiver of risk in the system, irrespective of the time horizon. Among the GIPSI markets, the larger and highly indebted EU economies of Italy and Spain are net contributors of risk, while the smaller EU economies of Greece, Ireland, and Portugal as well as Asia are net receivers of risk, also regardless of the time horizons. The US and European stock markets are net contributors of the spillovers, whereas the Asia equity index is a net receiver, irrespective of the frequencies. Finally, the AR(1)-FIGARCH model with the skewed Student-t distribution provides more accurate in-sample estimates in all markets. The hedging is more expensive in the long term than in the short term. The hedging is also more expensive during the ESDC period than the other turbulent periods considered in the study. The hedging effectiveness is also higher in the long term than in the short term, is crisis-sensitive, and depends on the time-investment horizon factor. Refereed/Peer-reviewed

Volatilities in the stock market are due to fluctuations in essential energy commodities. This in effect underpins the impact of short- and long-run prices on producers, consumers, portfolio managers, and policymakers. To understand the past, present, and future dynamics of energy commodities and stock market uncertainty — this paper investigated the nexus between real stock index in the US. We investigated energy commodities namely oil price, coal price, and natural gas price employing over decadal monthly data from 1991:01 to 2019:12. The study applied autoregressive distributed lag (ARDL) and dynamic simulations of ARDL (DYNARDL) techniques to investigate long-term shocks in oil price, coal price, natural gas price, short-term interest rate, and industrial production index. The study found negative long-run relationship between real oil price, real coal price, real natural gas price, short-term interest rate, and real stock index, with only industrial production index reporting a positive relationship with real stock index in both ARDL and dynamic simulated ARDL models. While we found positive relationship between energy commodities and real stock index in the short run, negative relationship was reported between short-term interest rate, industrial production index, and real stock index. Incorporating real Western Texas Intermediate oil in S&P500 stock price index function corrects historical fluctuations by 64% compared to 54% speed of adjustment with real brent oil. The dynamic ARDL simulation further provides key insight into how energy commodity prices and economic activity shocks are transmitted to stock market prices in the U.S.

Prior studies argue economic policy uncertainty index (EPU) can successfully predict oil price volatility. This paper mainly sheds light on the forecasting ability of categorical EPU indexes for both WTI and Brent oil futures. To illustrate this problem, we extend the LASSO model with Markov two-stage regimes (MS-LASSO model) to better fit the nonlinear feature of oil volatility dynamic. Our empirical results provide empirical evidence that EPU index not only contains predictive information content for WTI and Brent oil volatility, but achieves higher realized utility. Notably, we find that the predictive ability of categorical EPU indexes is asymmetric through global financial crisis, business cycle and different market conditions. Additionally, the uncertainty index of sovereign debt and currency crises is the most frequent predictor selected by shrinkage method for both WTI and Brent oil volatility.

Modeling the volatility of energy commodity returns has become a topic of increased interest in recent years, because of the important role it plays in today's economy. In this paper we propose a novel copula-based stochas- tic volatility model for energy commodity returns that allows for asymmetric volatility persistence. We employ Approximate Bayesian Computation (ABC), a powerful tool to make inferences and predictions for such highly-nonlinear model. We carry out two simulation studies to illustrate that ABC is an appropriate alternative to standard MCMC-based methods when the state transition process is challenging to implement. Finally, we model the volatility of WTI and Brent oil futures' returns with the proposed copula-based stochastic volatility model and show that such model outperforms symmetric alternatives in terms of in- and out-of-sample volatility prediction accurac The first author acknowledges financial support from Spanish Ministry of Science, Innovation and Universities, grant number ECO2017–83255-C3–2-P. The second and third authors acknowledge financial support from Agencia Estatal de Investigación (PID2019–108311GB-I00/AEI/10.13039/501100011033)

We study the equilibrium relationship between the WTI and the Brent crude oil indexes in prices and in option-implied moments using fractional cointegration models from 2008-2016. This period has been subject to changing constraints in terms of rising US inventories and falling demand. Our results suggest there exists a cointegrating relationship in prices as well as between risk-neutral moments. While a long-lasting spread in prices is not supported by the data, our results support a significant volatility differential between the two oil indexes. The Cushing bottleneck is linked to slower speeds of adjustment to disequilibrium for both indexes as well as a fragmentation of the international equilibrium for tail and crash risk, especially for longer horizons. Crash and tail risk are more locally driven and less affected by the international equilibrium than are price and volatility.

This paper examines the impacts of COVID-19 on the multifractality of gold and oil prices based on upward and downward trends. We apply the Asymmetric Multifractal Detrended Fluctuation Analysis (A-MF-DFA) approach to 15-min interval intraday data. The results show strong evidence of asymmetric multifractality that increases as the fractality scale increases. Moreover, multifractality is especially higher in the downside (upside) trend for Brent oil (gold), and this excess asymmetry has been more accentuated during the COVID-19 outbreak. Before the outbreak, the gold (oil) market was more inefficient during downward (upward) trends. During the COVID-19 outbreak period, we see that the results have changed. More precisely, we find that gold (oil) is more inefficient during upward (downward) trends. Gold and oil markets have been inefficient, particularly during the outbreak. The efficiency of gold and oil markets is sensitive to scales, market trends, and to the pandemic outbreak, highlighting the investor sentiment effect., Highlights • We examine the impacts of COVID-19 on the asymmetric multifractality of gold and oil. • Multifractality is especially higher in the downside (upside) trend for Brent oil (gold). • Before the COVID-19 outbreak, the gold (oil) market was more inefficient during downward (upward) trends. • During the COVID-19 outbreak, gold (oil) is more inefficient during upward (downward) trends. • Gold and oil have become more inefficient during the pandemic outbreak compared to the pre-COVID-19 period.

Balancing the oil pass-through to consumer and producer prices is crucial for policymakers. This study aimed to advance associated thinking by examining how consumer and producer prices in China related to changes in global oil prices from 2006 to 2018. First we investigated the pass-through of oil spot prices to consumer prices as indicated by the consumer price index (CPI) and means of consumption price index (MCPI), and to producer prices as indicated by the producer price index (PPI) and means of production price index (MPPI), with a monetary policy in China. Furthermore, this study explored the non-linear and mediating effects of financial markets and government debt on linkages between oil prices and consumer/producer prices based on non-linear framework and causal steps approach, respectively. Our findings indicated some key points; for example, the pass-through of oil prices with a monetary policy in China shed light on a benchmark role in global oil markets. Additionally, the non-linear effect of oil prices on consumer/producer prices varied across the Brent and West Texas Intermediate (WTI) crude oil markets. Additionally, the mediating effect of government debt reflected the effectiveness in balancing the relationship between oil prices and producer prices. Government debt explained the -0.091 transition between the Brent oil price and the PPI and could explain the -0.095 transition between the Brent oil price and the MPPI, whereas for the transition due to financial markets were -0.064 and -0.080, respectively. These outcomes have important implications for stabilizing price levels in countries.

We study the changing relationship between Brent oil prices and geopolitical risk, conditional on physical oil market conditions. We conduct the analysis at three frequencies, medium (1-3 years), high (2-3 months), and very high (daily), using three complementary techniques at the different levels (respectively, continuous wavelet partial coherence, VAR and GARCH-MIDAS) over the period April 1993 to the end of 2018. At the annual frequency, we find evidence of a sustained positive relationship between oil prices and geopolitical risk over the past decade -- with geopolitical risk leading during the Arab Spring, resulting in a substantial geopolitical risk premium, and lagging thereafter by about two months, as oil markets first reacted to and then anticipated geopolitical events. At the monthly frequency, we find the same positive correlation with oil prices anticipating geopolitical risk in both parts of the sample and find that realized geopolitical strife has not led to higher prices in either subsample. At the daily frequency, we find that geopolitical risk has had a positive effect on oil price volatility in later days during the second half of the sample (2005 to 2018). Our findings suggest that some financial market speculators, such as macro hedge funds and algorithmic traders, may amass long positions in Brent in anticipation of geopolitical threats that might potentially lead to oil disruptions.

This paper tries to determine the strength of the interdependence between Brent oil market and the stock markets of oil importing Visegrad group countries and oil exporting Russia in different time-horizons. The paper uses several novel and elaborate methodologies – bivariate DCC-EGARCH model, wavelet correlations and phase difference. The results of DCC model show that all dynamic correlations between Brent oil and the selected stock indices are low at daily-frequency level. The magnitude of mutual correlations does not exceed 20% for Visegrad countries, while for Russia it goes little bit over 30%. Wavelet correlations in short-term confirms DCC results, whereby this relatively weak connection is found up to 32 days. However, in midterm and long-term, wavelet correlations strengthen, and go above 50% in midterm and even beyond 80% in long-term for majority of the indices. Slovakian SAX index has stronger wavelet correlation in 32 days than in 64 days, and it goes around 23%. This means that SAX can be coupled with Brent oil for diversification purposes in both short-term and midterm portfolios. Besides, phase-difference methodology provides an evidence that SAX was in anti-phase position in two separate occasions, meaning that SAX can also serve well for hedging purposes.

This paper investigates the interrelationship between Brent oil price and exchange rate in 10 emerging markets of East Europe, Asia, Africa, and South America. For computational purpose, we apply two innovative methodologies—wavelet coherence and phase difference that are capable of observing different frequency scales. Wavelet coherence results suggest that strong coherence is present during world financial crisis (WFC) in the oil‐exporting countries and in majority of the oil‐importing countries. Phase arrows as well as phase difference suggest negative coherence between oil and exchange rates in the oil‐importing countries during WFC. Negative coherence is found in these countries because currency depreciation was accompanied by immense oil price drop in WFC period. In addition, phase difference has relatively stable in‐phase dynamics in long term in the oil‐importing countries during tranquil periods, which confirms theoretical stance that higher oil prices cause currency depreciation and vice versa. As for the oil‐exporting countries, we find constant and relatively long‐lasting anti‐phase pattern in Russian and Nigerian cases for long‐term horizons but not for Brazilian one.

This paper concerns the risk analysis of six Chinese banks which are involved in carbon financing. Factor copula is introduced to simulate the corresponding carbon finance credit risk and market risk by latent variables in an indirect method. In short, the four common factors in carbon financing – exchange rates, interest rates, CER price, and Brent oil prices – are analyzed and explored in factor copula approach that incorporates KMV, GARCH models in two steps. The KMV and GARCH models are used to generate data that reflects the overall credit and market risk associated with each bank. Both normal copula and t-copula functions are used to simulate the parameter estimations for comparison in a new way. The value at risk for each of the six banks is calculated through a Monte Carlo simulation and compared. Additionally, we calculate shock estimates for each factor to explore the changes in both credit risk and market risk given economic shocks, also make a hypothetical analysis of the impact of the financial crisis on common factors. Overall, our findings reveal that exchange rates and oil prices are the key factors to consider in carbon financing. Ping An Bank is facing the most risk of all the banks in the sample, while the Industrial and Commercial Bank of China is carrying the least risk. The results of this analysis gave provide some insight into domestic carbon trading and carbon markets connectivity.

This paper sheds light on the questions whether it is possible to generate an accurate forecast of the real price of oil and how it can be improved using forecast combinations. For this reason, the following paper will investigate the out-of-sample performance of seven individual forecasting models. The results show that it is possible to construct better forecasts compared to a no-change benchmark for horizons up to 24 months with gains in the MSPE ratio as high as 25%. In addition, some of the existing models will be extended, e.g. the U.S. inventories model by introducing more suitable real-time measures for the Brent crude oil price and the VAR model of the global oil market by using different measures for the economic activity. Furthermore, the time performance investigated by constructing recursively estimated MSPE ratios discovers potential weaknesses of the used models. Hence, several different combination approaches are tested with the goal of demonstrating that a combination of individual models is beneficial for the forecasting performance. Thereby, a combination consisting of four models has proven to have a lower MSPE ratio than the best individual models over the medium run and, in addition, to be remarkably stable over time.

We examine the response of ICE Brent Crude futures to the spot Dated Brent benchmark published by Platts. Trading activity in the futures market intensifies during the benchmark assessment. We also find trading in the direction of the published benchmark during the price assessment window. Aligned positions and a substantially increased arrival rate of informed traders suggest that sophisticated traders, taking advantage of a rise in uninformed trading activity, induce the price run-up in Brent futures, ahead of the Dated Brent assessment end. The general increase in the arrival rate of both informed and uninformed traders during the assessment window underlines the benchmark's relevance and its potential for attracting liquidity. Our results are robust to alternative specifications and underscore the significance of physical commodity benchmarks as critical elements of the financial market infrastructure.

This article examines the interconnectedness of WTI and Brent prices on different resolutions of price movements. Firstly, within a multivariate BEKK framework we identify high but volatile correlations with recurring highs around 0.8 and multiple periods of decoupling. OPEC meetings increase the correlation in the short run. Secondly, linear l1-trends reveal that long-term movements of WTI and Brent are driven by the same dynamics, confirming the ‘one great pool’ hypothesis. OPEC meetings have only little impact on long-term price trends. Thirdly, we find leading effects of WTI over Brent by short-term trends of several days, especially in a negative direction. These trends have an asymmetrical effect on volatility; negative trends cause a stronger increase than positive trends. These findings are of interest to policy makers as well as hedging strategies of crude oil portfolios and provide insight into long-term movements of crude prices.

We use a semiparametric Markov switching AR-ARCH model to forecast the prices of OPEC, WTI, and Brent crude oils. We investigate the applicability of this model based on the proper selection of the core function in the prediction of the crude oil prices. Also, the forecasting ability of this model is compared with different ARIMA and GARCH models for both in-sample and out-of-sample horizons. The period 01-04-2010 to 31-12-2015 is used as the in-sample horizon for the estimation purposes, while the out-of-sample period for the forecasting evaluation is from 04-01-2016 to 15-12-2016. The estimation results show that the semiparametric Markov switching models forecast the crude oil prices more accurately than the ARIMA and GARCH models in both in-sample and out-of-sample horizons (1, 5, 10 and 22 steps ahead) for the studied crude oil prices.

The paper firstly studies the static tail dependence structure between the economic policy uncertainty (EPU) index and several financial markets (Brent Oil, CDS, VIX, SP500 and UK EPU) with Copula models. The results show significant negative upper tail dependence between the EPU index and the SP500 index in most years, but also show positive upper tail dependence during the 2016 period. Further, the negative tail dependence area between EPU and CDS also demonstrates that higher economic policy uncertainty may reduce real risk premium. Finally, based on Markov time-varying Copula functions, the dynamic time-varying structures between the EPU index and indexes including SP500, CDS, VIX and UK EPU indexes are studied. The significant regime switching characteristic is shown between the EPU index and the UK EPU index.