Crude oil hedging strategies using dynamic multivariate GARCH

The paper examines the performance of several multivariate volatility models, namely CCC, VARMA-GARCH, DCC, BEKK and diagonal BEKK, for the crude oil spot and futures returns of two major benchmark international crude oil markets, Brent and WTI, to calculate optimal portfolio weights and optimal hedge ratios, and to suggest a crude oil hedge strategy. The empirical results show that the optimal portfolio weights of all multivariate volatility models for Brent suggest holding futures in larger proportions than spot. For WTI, however, DCC, BEKK and diagonal BEKK suggest holding crude oil futures to spot, but CCC and VARMA-GARCH suggest holding crude oil spot to futures. In addition, the calculated optimal hedge ratios (OHRs) from each multivariate conditional volatility model give the time-varying hedge ratios, and recommend to short in crude oil futures with a high proportion of one dollar long in crude oil spot. Finally, the hedging effectiveness indicates that diagonal BEKK (BEKK) is the best (worst) model for OHR calculation in terms of reducing the variance of the portfolio.     

Time-varying long range dependence in energy futures markets

This study aims to investigate the presence of long-range dependence in energy futures markets. Using a daily dataset covering from 1990 to 2013 (which includes crucial events for energy markets such as invasion of Iraq and global financial crisis of 2008), we estimate time-varying generalized Hurst exponents of several energy futures contracts with different times to maturity using a rolling window approach. Results reveal that efficiency of energy futures markets is clearly time-varying and changes drastically over the sample period. For futures contracts with 1–4 months to maturities, crude oil and gasoline are found to be more efficient compared to others. On the other hand, for contracts with 5–9 months to maturities, crude oil and natural gas futures are more efficient. For almost every different month to maturity, heating oil and gas oil futures are found to be the least efficient markets. Moreover in general, the efficiency of energy futures markets is found to be decreasing dramatically when time to maturity is increasing. Several implications are discussed.     

Oil price volatility forecast with mixture memory GARCH

We expand the literature of volatility and Value-at-Risk forecasting of oil price returns by comparing the recently proposed Mixture Memory GARCH (MMGARCH) model to other discrete volatility models (GARCH, RiskMetrics, EGARCH, APARCH, FIGARCH, HYGARCH, and FIAPARCH). We incorporate an Expectation-Maximization algorithm for parameter estimation of the MMGARCH and find different structures in volatility level as well as shock persistence. MMGARCH is also able to cover asymmetric and long memory effects. Furthermore, a dissimilar memory structure in variance of WTI and Brent crude oil prices is observed which is supported by additional tests. Parameter estimation and comparison of the models reveal significant long memory and asymmetry in oil price returns. In regard of variance forecasting and Value-at-Risk prediction, it is shown that MMGARCH outperforms the aforementioned models due to its dynamic approach in varying the volatility level and memory of the process. We find MMGARCH superior for application in risk management as a result of its flexibility in adjusting to variance shifts and shocks.     

How do OPEC news and structural breaks impact returns and volatility in crude oil markets? Further evidence from a long memory process

Since its formation, OPEC through its conference decisions has been a major player in the world oil markets. The purpose of this paper is to examine the impacts of OPEC's different news announcements on the conditional expectations and volatility of crude oil markets in the presence of long memory and structural changes. To do so, we first discern OPEC's oil production behavior in response to its “cut”, “maintain”, and “increase” decisions. Then by applying the ARMA–GARCH class models to the two global benchmarks WTI and Brent over the period May 1987 through December 2012, we find strong evidence of long memory. The empirical evidence also shows that OPEC's announcements especially the “cut” and the “maintain” decisions have a significant effect on both returns and volatility of the crude oil markets, particularly that of the WTI. Moreover, we explore the possibility of structural breaks in the crude oil prices and detect five (six) breakpoints for the WTI (Brent) oil markets. The presence of structural breaks reduces the persistence of volatility. Accounting for OPEC's scheduled news announcements in the presence of structural changes reduces the degree of volatility persistence and enhances the understanding of this volatility in the oil markets. These results have several implications for policy makers, oil traders and other participants in the crude oil markets.     

A multifactor stochastic volatility model of commodity prices

We propose a novel representation of commodity spot prices in which the cost-of-carry and the spot price volatility are both driven by an arbitrary number of risk factors, nesting many existing specifications. The model exhibits unspanned stochastic volatility, provides simple closed-form expressions of commodity futures, and yields analytic formulas of European options on futures. We estimate the model using oil futures and options data, and find that the pricing of traded contracts is accurate for a wide range of maturities and strike prices. The results suggest that at least three risk factors in the spot price volatility are needed to accurately fit the volatility surface of options on oil futures, highlighting the importance of using general multifactor models in pricing commodity contingent claims.     

Testing persistence of WTI and Brent long-run relationship after the shale oil supply shock

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.     

Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis

This paper examines the flow of information and its direction between the oil volatility index (OVX) and the spot variance of WTI and Brent returns. Since OVX is an indicator of the investor sentiment about oil market performance, we aim first at evidencing whether there is an exchange of information between OVX and the spot variance of the two crudes. Moreover, since OVX is linked to WTI crude oil, it is useful to investigate whether it shares an information content with Brent. To this purpose, we propose an entropy-based approach which exploits two non-parametric methods: the mutual information and the transfer entropy. The results show an increase in the information flow between OVX and the spot variance of Brent returns and a corresponding decrease in the information flow with WTI. Furthermore, the direction of the information flow comes from OVX to both oil spot variances, thus investor sentiment about oil market performance drives uncertainty in the corresponding spot market. However, the information flow from the oil spot variances to OVX is more statistically significant for WTI than for Brent and, since transfer entropy is a measure of resolution of uncertainty, we demonstrate that the spot variance of WTI returns helps more in reducing uncertainty about OVX (than Brent).     

Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model

Forecasting the volatility and correlation among different kinds of assets has important applications in areas such as risk management, options pricing, and asset allocation. This paper mainly uses a novel multivariate Generalized Autoregressive Score (GAS) model to analyze and forecast volatilities and correlations between Brent, WTI and gold prices. The time-varying parameters of multivariate GAS model for a given distribution of crude oil and gold prices is observed which is supported by Doornik-Hansen test. The testing results of time-varying parameters based on LRT statistics reveal that the dependent structure between Brent and gold prices is more complex than those of WTI and gold. The estimation results show that the multivariate GAS method well captures the volatility persistence and nonlinear interaction effects between the crude oil and gold markets. In addition, we compare the forecasting performance of the GAS with the classical Dynamic Conditional Correlation Generalized Auto-Regressive Conditional Heteroskedasticity (DCC-GARCH) model, and find that the forecasting power of volatility and correlation in multivariate GAS model is better than the DCC-GARCH model.     

Oil stocks,risk factors,and tail behavior

Early studies of oil stocks focus exclusively on the average relationship between oil price changes and individual stock returns. In this paper, we examine how the tail behavior of risk factors affects the tail behavior of individual oil stock returns. We consider a total of 25 widely-used risk factors from the asset pricing literature. These risk factors include 14 stock market factors, three bond market factors, and eight commodity market factors. We find that the excess stock market return, the change in CBOE aggregate market volatility index, the commodity market index return, the change in the prices of oil futures contracts, and the change in CBOE oil ETF volatility index have the largest impact in moving oil stocks tail returns.     

Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests

Using the Google search volume index (GSVI) to measure investor attention, this paper investigates the relationships between investor attention and crude oil prices for the main crude oil markets worldwide. To account for possible structural breaks and nonlinearity in the relation between investor attention and oil returns, Fourier unit root test and nonlinear Granger causality tests are employed. The empirical results suggest that the bidirectional nonlinear Granger causality exists only between investor attention and WTI future crude oil return. However, WTI crude oil return Granger-causes investor attention weakly. For Dubai spot, Daqing spot, WTI spot and Brent future oil markets, unidirectional nonlinear Granger causality runs from investor attention to oil returns, which is relatively weak.     

Do OPEC announcements influence oil prices?

This paper investigates the effect of OPEC production decisions (increase, cut, maintain) on both WTI and Brent crude oil prices between Q1 1991 and Q1 2015 by employing the event study methodology and by using two indices as benchmarks (BCI and S&P GSCI). We employ an EGARCH model to take into account the high volatility of oil prices and some stylized facts characterizing this volatility. We find that the impact of OPEC’s announcements on oil prices (i)evolves over time and among decisions, (ii) is more significant for production cut and maintain, (iii) is different for WTI and Brent prices, and (iv) is sensitive to the benchmark index. Moreover, OPEC’s decisions depend on the exploration and extraction cost of more expensive/unconventional oil resources.     

Are green bonds environmentally friendly and good performing assets?

The first objective of this paper is to study the existence of greenness in green bonds. For this objective, we propose a new model of price correlations between green bonds and energy commodities. The second objective is to examine the performance of green bonds over conventional bonds. We propose a new model of the expected return, the risk, and the performance ratio of green bond premiums defined by the log price differences between green and conventional bonds so as to address the second objective. Empirical studies using the data of green and conventional bond indices and crude oil prices show that the Bloomberg Barclays MSCI and the S&P green bond indices tend to have positive correlations with and increase in line with both WTI and Brent crude oil prices while the Solactive green bond index tends to have negative correlations with and decrease in line with both WTI and Brent crude oil prices. From the empirical evidence of the positive relationship between energy and environmental value, it is suggested that the greenness is incorporated in the Bloomberg Barclays MSCI and the S&P green bond. In contrast taking it into account that the conventional S&P bond index has negative correlations with WTI and Brent crude oil prices which are the same as the results of the Solactive green bond index, the Solactive green bond index may not fully represent the characteristics of green bonds in the sense of environmental value. We also demonstrate that the expected returns of green bond premiums are positive while decreasing and that the risks of green bond premiums are slightly decreasing but almost flat over time in the recent years, resulting in positive but decreasing information ratios. It implies that green bond investment performance is superior to conventional bond investment performance but the superiority is decaying over time.     

Oil sensitivity and its asymmetric impact on the stock market

We develop a two-step methodology to facilitate an examination of the impact of oil shocks on stock returns. Oil price volatility is monitored in this study through the use of a regime-switching model, with the presence of jumps subsequently being taken into consideration to examine the asymmetric effects of oil prices on stock returns. Our analysis provides quite conclusive results based upon the use of a regime-switching model with consideration of jumps; that is, when there are significant fluctuations in oil prices (West Texas Intermediate; WTI), the resultant unexpected asymmetric price changes lead to negative impacts on S&P 500 returns. However, the same result does not hold in a regime of lower oil price fluctuations. We therefore suggest that the achievement of a well diversified portfolio should involve the consideration of oil price shocks, which, as a consequence, should also help to improve the accuracy of hedging against oil price risks.     

Cointegration between oil spot and future prices of the same and different grades in the presence of structural change

The purpose of this study is to examine whether crude oil spot and futures prices of the same and different grades are cointegrated using a residual-based cointegration test that allows for one structural break in the cointegrating vector and high-frequency data. We choose the US WTI and the UK Brent as the representative crudes for this analysis since these two crudes have well-established spot and futures markets. We find that spot and future prices of the same grade as well as spot and futures prices of different grades are cointegrated. We examine potential causes of structural change as revealed by the cointegration test in terms of events that have impacted on world oil markets as well as discuss the implications of the results for hedge managers, investors and regulators.     

Are the crude oil markets becoming more efficient over time? New evidence from a generalized spectral test

This paper utilizes the newly developed method of a generalized spectral test to examine the weak-form efficiency of the main worldwide crude oil markets. The generalized spectral test, unlike other methods, can detect both linear and nonlinear serial dependence in the conditional mean and allows for different forms of unknown conditional heteroscedasticity. By using a “rolling sample” approach instead of an analysis of different time periods, we find that the efficiency of oil markets may depend on time periods. The main global crude oil markets reach weak-form efficiency in the long-term and the degree of efficiency of global oil markets changes over time. Among the oil markets examined in this study, the Brent and the WTI oil markets possess the highest efficiency levels, whereas the Daqing oil market has the lowest efficiency level. Apparent anti-synchronization is detected between the efficiency of Brent and WTI markets in recent years, whereas synchronization is found between the efficiency of Daqing and Dubai oil markets during the last decade.     

Analyzing time-varying volatility spillovers between the crude oil markets using a new method

The spillover effect is an important factor affecting the volatility of crude oil price. Basing on the study of Diebold and Yilmaz (2009, 2012, 2014), we propose a new method that calculates the time-varying volatility spillover indexes by the generalized forecast error variance decomposition of TVP-VAR-SV model. Then, using the new method, we study the time-varying volatility spillovers between four major crude oil markets (WTI, Brent, Oman, Tapis) from November 29, 2002 to July 13, 2018. By comparing the results of our new method and traditional rolling window method, we verify the superiority of our new method. The results show that the volatility spillovers calculated by the new method are clearer, more stable and not outlier sensitive. From the estimated results of time-varying volatility spillovers, we find that the volatility spillover between crude oil markets is slowly increasing, but there are obvious cyclical changes. And from the correlation analysis and the Granger causality test, we find that the volatility and volatility spillovers are positively correlated and are two-way Granger causality, which supported for the market infection hypothesis of King and Wadhwani (1990).     

Modeling and forecasting crude oil markets using ARCH-type models

This study investigates the time-varying volatility of two major crude oil markets, the West Texas Intermediate (WTI) and Europe Brent. A flexible autoregressive conditional heteroskedasticity (ARCH) model is used to take into account the stylized volatility facts such as clustering volatility, asymmetric news impact and long memory volatility among others. The empirical results indicate that the intensity of long-persistence volatility in the WTI is greater than in the Brent. It is also found that for the WTI, the appreciation and depreciation shocks of the WTI have similar impact on the resulting volatility. However, a leverage effect is found in Brent. Although both the estimation and diagnostic evaluations are in favor of an asymmetric long memory ARCH model, only the WTI models provide superior in the out-of-sample forecasts. On the other hand, from the empirical out-of-sample forecasts, it appears that the simplest parsimonious generalized ARCH provides the best forecasted evaluations for the Brent crude oil data.     

Forecasting volatility of crude oil markets

This article investigates the efficacy of a volatility model for three crude oil markets — Brent, Dubai, and West Texas Intermediate (WTI) — with regard to its ability to forecast and identify volatility stylized facts, in particular volatility persistence or long memory. In this context, we assess persistence in the volatility of the three crude oil prices using conditional volatility models. The CGARCH and FIGARCH models are better equipped to capture persistence than are the GARCH and IGARCH models. The CGARCH and FIGARCH models also provide superior performance in out-of-sample volatility forecasts. We conclude that the CGARCH and FIGARCH models are useful for modeling and forecasting persistence in the volatility of crude oil prices.     

Volatility behavior of oil,industrial commodity and stock markets in a regime-switching environment

This study supplements previous regime-switching studies on WTI crude oil and finds two possible volatility regimes for the strategic commodity prices of Brent oil, WTI oil, copper, gold and silver, and the S&P 500 index, but with varying high-to-low volatility ratios. The dynamic conditional correlations (DCCs) indicate increasing correlations among all the commodities since the 2003 Iraq war but decreasing correlations with the S&P 500 index. The commodities also show different volatility persistence responses to financial and geopolitical crises, while the S&P 500 index responds to both financial and geopolitical crises. Implications are discussed.     

Co-integration of ICE Gas oil and Crude oil futures

In this paper, the relationship between Gas oil and Brent Crude oil futures prices is investigated. The analysis is based on daily price series for five different contract lengths traded on ICE Futures Europe. The price series and their first differences are tested for stationarity. Linear relationships between the pair-wise Gas oil and Crude oil contracts are then tested for co-integration. A co-integrated relationship is found for the 1 and 2 month contracts covering data from 1994 to 2009, and Error Correction Models are established to estimate the relationships. No co-integrated relationships are found for the 3, 6 and 12 month contracts covering the period 2002-2009, nor for the 1 and 2 month contracts for this period. The futures prices for this period are collected from a volatile market, including hurricane Katrina, the economic boom and the following financial crises which might explain these results. Thus, in such volatile periods the spread between Gas oil and Crude oil is likely to deviate, and it might take several years until it reverts to its equilibrium value. For energy traders and hedgers, this will imply that exposures to the crack spread should be treated with great care in such market environments.