Oil Market Volatility Research Articles

It's not that important: The negligible effect of oil market uncertainty

Extensive literature has shown the predictability of stock returns based on various factors originating in the oil markets. We investigate the international stock return predictability at both daily and monthly frequencies from a new perspective of oil market uncertainties, which are measured by the oil volatility risk premium. In addition to traditional in-sample and out-of-sample analyses, a time-varying approach is also employed to identify the predictive characteristics of the oil market uncertainty. However, the empirical results suggest that there is a weak statistically significant effect of the oil market volatility on global stock returns. The results are essentially unchanged during cyclical expansions and recessions as well as during market periods with relatively high and low volatility. Even in the time-varying analysis, the stock return predictability based on the oil market uncertainty is still inferior. Generally, we shed some doubts on the stock return predictability based on the oil market uncertainty, which contrasts with the existing perspectives.

Analyzing volatility transmission using group transfer entropy

We analyze the transmission of volatility between the oil, stock, gold, and currency markets using transfer entropy. Our approach, which we denote by group transfer entropy, measures the overall sensitivity of a process to lagged realizations of a group of other processes. We supplement our measure by a suitable block bootstrap approach which allows to conduct inference. Our empirical analysis uses daily data of OVX, VIX, GVZ, and EVZ from 2008 to 2017. The results show that oil and stock market volatility are the most affected by past volatility changes in the other markets, which highlights the importance for investors to hedge against this predictable risk transmission. Furthermore, we show that group transfer entropy can unveil nonlinear dynamics beyond the standard vector autoregressive methodology and provides a useful basis for forecast averaging.

Does OPEC news sentiment influence stock returns of energy firms in the United States?

This paper utilises commodity-specific news sentiment data on the Organisation of Petroleum Exporting Countries (OPEC) collected from Thomson Reuters News Analytics in the period 2003–2014 and examines the impact of foreign news sentiment on the financial performance of domestic firms in the energy sector. We find that in the US market, negative news on OPEC positively influences the stock returns of the US-listed firms. The effects of negative news sentiment on OPEC dominate our results. These results are robust in different subsectors of the oil and gas industry and in alternative specifications of oil prices and market volatility. We show that the effect is conditional on market risks and the types of news announcements made. The effect from negative news is stronger in periods of higher reserves, exports and production. However, at times of low consumer confidence and high imports, a positive OPEC news announcement negatively influences a US-listed energy firm's stock return. In the context of hedging and risk mitigation, our findings have important policy implications for investors and traders in the energy sector.

Cross herding between American industries and the oil market

This paper contributes to the debate on the cross-markets correlation by extending tests of herding behavior between the stock market and the crude oil market. We study herding behavior at the sector level during extreme oil market movements. Furthermore, we investigate the cross herding behavior between the two markets while including the volatility of the oil prices and investors’ sentiment. We use a modified version of the cross section absolute deviation model to infer the impact of the oil market on industry herding. Our database covers a sample of all U.S. stocks from January 2000 to December 2017 on a daily basis. The results indicate that industry herding is absent in all sectors. However, when considering the extreme oil market movements, the results suggest that sector herding is more pronounced during oil market downturns than upturns. Moreover, we find that the oil market volatility and investors’ sentiment reduce the industry herding. Our findings suggest that the herding is strongly related to the information in the oil market. In fact, investors use this information as trading signals, which affect their collective behavior. When the fear sentiment is high, investors do not follow the market consensus, but rather their own information making independent decisions.

Oil Market Volatility and Stock Market Volatility

This paper studies the comovement between volatility of the equity market and the oil market, both for implied and realized volatilities. The wavelet methodology enables us to study this relationship on various time scales. We find that there is a strong comovement between the volatilities of the two markets. However, this comovement is time-varying and depends on the time scale. It is strong at yearly horizon, but much weaker at horizons of a few days. Moreover, implied volatility of the stock market leads the implied volatility of the oil market, whereas no such relationship is observed for realized volatilities.

Oil market volatility and stock market volatility

This paper studies the comovement between volatility of the equity market and the oil market, both for implied and realized volatilities. The wavelet methodology enables us to study this relationship on various time scales. We find that there is a strong comovement between the volatilities of the two markets. However, this comovement is time-varying and depends on the time scale. It is strong at yearly horizon, but much weaker at horizons of a few days. Moreover, implied volatility of the stock market leads the implied volatility of the oil market, whereas no such relationship is observed for realized volatilities.

Does Oil Market Volatility Earn OPEC a Risk Premium?

The question is examined whether OPEC is in a position to collect a risk premium to effectively insure others against a macroeconomic downturn, which may, in turn, result from a rise in the price of crude oil. Contingent exchange is modeled such that parties whose consumption is elastic with respect to aggregate consumption hedge; they pay risk premia to smooth consumption. Parties whose consumption is inelastic or negatively related to aggregate consumption collect risk premia. OPEC’s revenues are estimated, insofar as they are explained by OPEC’s cartel pricing, to be strongly negatively related to aggregate consumption. Insofar as they are explained by exogenous macroeconomic changes, they are highly inelastic with respect to the aggregate. Either way, OPEC is in a strong position to collect a risk premium from volatility in the market for crude oil. This may incent it to destabilize the market, which would lower macroeconomic output because of the asymmetric effects of oil prices on the macroeconomy.

A Multiple and Partial Wavelet Analysis of the Oil Price, Inflation, Exchange Rate, and Economic Growth Nexus in Saudi Arabia

ABSTRACTThis article provides a fresh insight into the dynamic nexus between oil prices, the Saudi/US dollar exchange rate, inflation, and output growth rate in Saudi Arabia’ economy, using novel Morlet’ wavelet methods. Specifically, it implements various tools of methodology: the continuous wavelet power spectrum, the cross-wavelet power spectrum, the wavelet coherency, the multiple and the partial wavelet coherence to the annual sample period 1969–2014. Our results unveil that the relationships among the variables evolve through time and frequency. From the time-domain view, we show strong but non-homogenous linkages between the four variables. From the frequency-domain view, we uncover significant wavelet coherences and strong lead-lag relationships. From an economic view, the wavelet analysis shows that Saudi economy is still exposed to several global risk factors, which are mainly related to the oil market volatility, and the pegging of the local currency to the US dollar. Such risk factors strongly and negatively affect the real economic growth, exert more pressure on inflation, and substantially limit the freedom to pursue an independent monetary policy.

Modeling and forecasting oil price risk: the role of implied volatility index

PurposeWhile numerous empirical studies have tried to model and forecast the oil price volatility over the years, such attempts using the crude oil volatility index (OVX) rarely exist. In order to conceal this void, the purpose of this paper is to investigate whether including OVX in the realized volatility (RV) models improve the accuracy of predictions.Design/methodology/approachAt the empirical stage, the authors employ several measures to frame the RV of crude oil futures returns. In particular, the authors use three different range-based RV estimators recommended by Parkinson (1980), Rogers and Satchell (1991) and Alizadeh et al. (2002), respectively.FindingsThe findings reveal that the information content of crude OVX helps to provide more accurate volatility predictions in comparison to the base-line RV model which contains only historical oil volatilities. Besides, the forecast encompassing test further suggests that the modified RV model (when OVX is introduced in the base-line RV model) forecast encompasses the conventional RV forecast in majority of the cases.Practical implicationsSince forecasting oil price volatility plays a vital role in portfolio optimization, derivatives pricing, optimum asset allocation decisions and risk management, the findings of this study thus carry important implications for energy economists, investors and policymakers.Originality/valueThis paper adds to the existing literature, since it is one of the initial studies to explore whether OVX is informative about the realized variance of the US oil market returns. The findings recommend that the information content of oil implied volatilities should be taken into account when modeling the US oil market volatility. In addition, range-based measures should be utilized while estimating the RV.

“De-financialization” of commodities? Evidence from stock, crude oil and natural gas markets

In order to investigate whether the crude oil and natural gas market volatility is influenced by the volatility in the stock market or whether these different variables move all together, we introduce the Volatility Threshold Dynamic Conditional Correlations (VT-DCC) approach to investigate the spillover effect of stock market volatility index (VIX, VSTOXX) on crude oil and natural gas markets during 1999–2015, and make the correlation dynamics dependent on conditional variance values through a threshold grid search algorithm. By detecting one endogenous break point in the raw series, we identify two clusters: one in 2008 and another in 2014, due to the financial crisis and the structural low oil prices linked to changing fundamentals, respectively. Also, the U.S. Henry Hub gas seems to be associated with the stock market volatility indexes, contrary to the European NBP gas, which is linked to the Brent. Besides, regarding the volatility behaviors of our series, the four energy variables violate their thresholds at similar moments, and the stock market VIX and VSTOXX exhibit logically similarities. Co-movements are detectable as well between the VIX and crude oil series, when investigating the volatility extracted from the GARCH model. In addition, the Block-DCC estimates provide ample evidence of similarities in the correlation dynamics between the crude oil and stock volatility series. It should be noted that the modeling framework proposed in this paper represents a useful tool for the study of cross-market contagion.

Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?

This paper aims to identify the most informative determinant in forecasting crude oil market volatility. We use a new GARCH-class model based on mixed data sampling regression and the dynamic model averaging combination method to examine the predicting power of the determinants. We integrate both the global economic policy uncertainty (GEPU) indices and several national economic policy uncertainty (EPU) indices with traditional determinants, such as global oil demand, supply, and speculation. Our analysis suggests that the EPU indices comprehensively integrate the information contained in other determinants. Specifically, GEPU indices and the U.S.’s EPU index have superior predictive powers for West Texas Intermediate spot oil volatility. This finding highlights the importance of EPU indices, implying that they are key factors to consider when determining crude oil market volatility.

Does speculation in the oil market drive investor herding in emerging stock markets?

This paper examines whether the time variation in the level of investor herding in the stock markets of major oil exporting nations relates to speculation and volatility in the global oil market. We find that speculative activities in the oil market, rather than oil price movements, are positively correlated with anti-herding in the stock markets of major exporters. We argue that traders take the speculative signals from the oil market as a sign of positive expectations and try to generate superior profits by going against the crowd in their local market. While this pattern largely holds during calm (low volatility) market periods, we also find that significant herd behavior takes place during high volatility (or crisis) periods. The findings suggest that policy makers who are concerned about stability in their stock markets should monitor measures of speculative activities in the energy market in order to model and monitor volatility and/or risk transmissions into their markets.

The impact of oil price volatility on net-oil exporter and importer countries’ stock markets

This paper examines the impact of oil price fluctuations on a large set of stock market returns in net-oil importer countries and net-oil exporter countries. It applies multivariate cDCC-GARCH model, which has greater flexibilities, allowing the conditional variance covariance matrix of stock market returns to vary over time. Daily data spanning from January 2005 to February 2016 is used to obtain dynamic correlations between crude oil and stock market returns. Moreover, it employs the commonly recognized vector auto regression (VAR) specification and the corresponding Granger causality test in order to examine the linear relationship between crude oil and stock market volatility within each country, revealing whether there is a causal relationship between the variables in terms of time precedence. The influence of bullish and bearish market conditions is also measured by dividing the sample period into two sub-periods: Global Financial Crisis Period (2007–2010) and Post-Crisis Period (2010–2016). Main findings of this research indicate time-varying correlation of oil and stock prices for oil-importing countries is more pronounced than that for oil-exporting countries. This result shows that the correlation between the volatilities of stock market and oil price returns varies depending on the net position of the country in global oil market.

Oil and Stock Market Returns: Direction, Volatility, or Liquidity?

We examine how oil market variables affect short-term stock market returns in the U.S. and in six other major oil-importing countries. Apart from oil price direction, we also consider oil market volatility and liquidity. Analysis of daily returns during the 2007 to 2017 period reveals that oil price direction has a significant positive effect on returns for all markets, which can be interpreted as evidence of oil as a business climate indicator. Furthermore, for the U.S. market, shocks to implied oil market volatility negatively affect stocks, this effect is significantly asymmetric, and declining oil market liquidity predicts declining stock prices.

Improved Forecast Ability of Oil Market Volatility Based on combined Markov Switching and GARCH-class Model

This paper estimates and forecasts West Texas Intermediate (WTI) crude oil market volatility with GARCH, EGARCH models. We find that, in terms of Root Mean Squared Error (RMSE) criteria, EGARCH model outperformances GARCH model in both short term and long term forecast length. Then, we apply Markov Switching model on oil volatility term and find that we can improve forecast accuracy of oil market volatility further with MSGARCH model, when compared with normal GARCH and normal EGARCH model. Besides, empirical results confirm that two regimes consist of regime 1 with low volatility and regime 2 with high volatility exist in WTI crude oil market. Also, the smoothed probabilities indicate that before 2004, WTI crude oil market stays in a low volatility regime most of time, while after 2004, WTI crude oil market switches to high volatility regime and stays in it until present most of time.

Time series analysis of volatility in the petroleum pricing markets: the persistence, asymmetry and jumps in the returns series

AbstractThe petroleum energy market is becoming more volatile owing to recent fluctuations in oil price, which in the long run affects the pricing and volatility persistence levels of other petroleum products. Apart from the symmetry and asymmetry that are known with volatility series, jumps have recently been identified, while the symmetric and asymmetric models failed in predicting the jump components in the financial series. The historical prices of crude oil and its distilled constituents possess occasional jumps as a result of global political or economic constraints. We applied both fractional persistence and volatility modelling frameworks in studying the volatility persistence in crude oil and petroleum products prices. We chose among symmetric, asymmetric and jumps volatility models. Results indicated that prices of crude oil and gasoline were less persistent when compared with volatility series of other petroleum products. The newly proposed jump volatility model variants outperformed other existing volatility models in predicting the volatility in the prices of crude oil, heating oil and diesel. The exception was the Asymmetric PowerARCH(APARCH) model, which emerged best in predicting the prices of gasoline, kerosene and propane prices; butGASvariants were still ranked second and third competing models in predicting the volatility in gasoline and kerosene prices. Using wrongly specified model for predicting the volatility in petroleum pricing can misinform oil markets, thereby generating intense conditional oil market volatility that is capable of distorting the price of oil and macroeconomic stability of the entire globe.

What Drives Long-term Oil Market Volatility? Fundamentals versus Speculation

Abstract This paper explores the role of speculation and economy fundamentals in the oil market using a two-component GARCH-MIDAS model. Specifically, the authors highlight the different roles played by the changing oil shocks with respect to the short-term and long-term components regarding oil market volatility. The results indicate that a global demand shock is the only factor found not only to be positive but to also significantly increase long- and short-term oil volatility in the full sample. This is consistent with a classic host of research that advocates that global demand dominates the oil market. However, since 2004, impacts of other oil shocks have been significantly weakened or even reversed. For example, the speculative demand shock has helped to stabilize long-term oil volatility during the post-2004 period. The results also suggest the existence of asymmetric impacts on short-term oil volatility, particularly for shocks from oil supply, oil-specific demand and oil speculative demand.

On the volatility transmission between oil and stock markets: a comparison of emerging importers and exporters

This paper examines the volatility transmissions between the oil and stock markets by performing causality-in-variance tests across a number of emerging (oil) importing and exporting nations. The volatility spillover causality tests generally rule out country-to-oil effects, suggesting unidirectional volatility transmissions from oil prices to emerging stock markets, particularly in the case of the net exporting nations of Kuwait, Saudi Arabia and UAE. We also observe significant volatility spillovers from oil prices to emerging importers during the post-global financial crisis period. The findings suggest that the information on oil market volatility combined with the magnitude of volatility spillovers can be utilized towards modeling and monitoring volatility shocks in emerging stock markets.

Forecasting crude oil market volatility: A Markov switching multifractal volatility approach

We use a Markov switching multifractal (MSM) volatility model to forecast crude oil return volatility. Not only can the model capture stylized facts of multiscaling, long memory, and structural breaks in volatility, it is also more parsimonious in parameterization, after allowing for hundreds of regimes in the volatility. Our in-sample results suggest that MSM models fit oil return data better than the traditional GARCH-class models. The out-of-sample results show that MSM models generate more accurate volatility forecasts than either popular GARCH-class models or the historical volatility model.

The spillover effects across natural gas and oil markets: Based on the VEC–MGARCH framework

Abstract This paper empirically investigates both price and volatility spillover effects in a comprehensive VEC–MGARCH framework. The hedging strategy is further discussed using the spillover effects. Crude oil and natural gas markets of US, Europe and Japan are examined for regional segmentation and different pricing mechanisms of natural gas. Our results show that the European and Japanese gas prices are cointegrated with Brent oil prices, but US gas price is decoupled from oil due to natural gas market liberalization and shale gas expansion. In all cases, the results support the presence of price spillover from crude oil markets to natural gas markets, but a reverse relationship does not exist. The asymmetric price spillover effects might be explained by the relative size of each market. It was also found that the volatility in oil market seems to spillover to the natural gas market, and vice versa, in both US and Europe. On the contrary, volatility seems to be independent in natural gas and oil markets in Japan. The difference in the results of the volatility spillover effects could be explained by the pricing mechanism of natural gas, especially the risk avoidance mechanism in gas pricing in Japan. The risk management performance of hedging strategy is remarkable considering volatility spillover.