Volatility Of Oil Futures Research Articles

Oil futures volatility predictability: New evidence based on machine learning models

This paper comprehensively examines the connection between oil futures volatility and the financial market based on a model-rich environment, which contains traditional predicting models, machine learning models, and combination models. The results highlight the efficiency of machine learning models for oil futures volatility forecasting, particularly the ensemble models and neural network models. Most interestingly, we consider the “forecast combination puzzle” in machine learning models, and find that combination models continue to have more satisfactory performances in all types of situations. We also discuss the model interpretability and each indicator's contribution to the prediction. Our paper provides new insights for machine learning methods' applications in futures market volatility prediction, which is helpful for academics, policy-makers, and investors.

The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model

The fluctuations in global policy affect the volatility of international crude oil markets. It is quite meaningful to analyze the impact of policy uncertainty on crude oil market volatility from a global integration perspective in deep. This paper aims to examine whether the global uncertainty of economic policy (GEPU) and the uncertainty of uncertainty (GEPU change) have different impacts on crude oil futures volatility. We establish two types of models under the GARCH-MIDAS framework, single-factor model and two-factor model, for discussing their explanatory and predictive power when considering the historical realized volatility. The findings show the GEPU index and its changes are consistent effective predictive factors in volatility of crude oil futures market when only individual effect is considered. Specially, GEPU change has stronger predictive power than GEPU index. Furthermore, the two-factor model with GEPU change contains more information which holds stronger forecasting ability in crude oil futures market volatility. However, GEPU cannot be an effective forecast factor when taking the influence of realized volatility into account.

Forecasting China's crude oil futures volatility: How to dig out the information of other energy futures volatilities?

This paper explores the predictability of China's crude oil futures volatility by considering other energy futures volatilities. Empirical results show that other energy futures volatilities can provide useful information for forecasting crude oil futures volatility both in- and out-of-sample. To further dig out predictive information from other energy futures volatilities, we employ forecast combinations and shrinkage methods. Corresponding results suggest that both forecast combinations and shrinkage methods make full use of information from other energy futures volatilities and generate more accurate forecasts of crude oil futures volatility. Furthermore, shrinkage methods have better forecasting performance than forecast combinations. Finally, the superior performance of shrinkage methods stems from their ability to accurately select other energy futures volatilities with strong predictive power.

The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic

The purpose of this article is to investigate whether various uncertainty measures provide incremental information for the prediction the volatility of crude oil futures under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, by considering the information overlap among various uncertainty measures and fully using of the information in various uncertainty measures, this paper uses two prevailing shrinkage methods, the least absolute shrinkage and selection operator (lasso) and elastic nets, to select uncertainty variables during the entire sampling period, before the COVID-19 pandemic and during the COVID-19 pandemic and then uses the HAR model to predict crude oil volatility. The results show that (i) uncertainty measures can be utilized to predict crude oil volatility under the high-frequency framework in both in-sample and out-of-sample analyses. (ii) Because of the information overlap between various uncertainty measures, adding a large number of uncertain variables to the HAR model may not significantly improve the volatility prediction. (iii) Before and during the COVID-19 pandemic, Chicago Board Options Exchange (CBOE) crude oil volatility (OVX) has the greatest impact on crude oil volatility, infectious disease equity market volatility (EMV) exerts a significant influence on crude oil futures volatility forecasts during the COVID-19 period, and CBOE implied volatility (VIX) and the financial stress index (FSI) have substantial impacts on crude oil futures volatility forecasts before COVID-19.

Oil futures volatility smiles in 2020: Why the bachelier smile is flatter

Oil futures volatility smiles in 2020: Why the bachelier smile is flatter

Newspaper-based equity uncertainty or implied volatility index: new evidence from oil market volatility predictability

ABSTRACT This article investigates two equity-oriented market volatility measures, newspaper-based equity market volatility (EMV) index and COBE-implied volatility index (VIX), which contain more predictive contents for the oil futures market based on a Markov regime-switching method. We find that both EMV and VIX contain valuable content for the oil market. Interestingly, the predictive performance of the model containing both EMV and regime switching is better than that of the model containing VIX and regime switching, further showing that EMV can be more powerful than VIX for oil futures volatility when considering regime switching. Our study tries to provide new insight into the oil futures market.

Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic

In this study, we focus on the role of jumps and leverage in predicting the realized volatility (RV) of China's crude oil futures. We employ a standard mixed data sampling (MIDAS) modeling framework. First, the in-sample results indicate that the jump and leverage effects are useful in predicting the RV of Chinese crude oil futures. Second, the out-of-sample results suggest that jump has very significant predictive power at the one-day-ahead horizon while the leverage effect contains more useful information for long-term predictions. Moreover, our results are supported by a number of robustness checks. Finally, we find new evidence that the prediction model that considers the leverage effect has the best predictive power during the COVID-19 pandemic.

Oil futures volatility predictability: Evidence based on Twitter-based uncertainty

This paper explores the influence of the Twitter-based uncertainty index on oil futures market volatility. The Twitter-based Market Uncertainty (TMU) index, based on the novel Markov-regime GARCH-MIDAS model, may significantly improve prediction accuracy for oil futures volatility. Moreover, the TMU was still useful in predicting oil volatility during the COVID-19 pandemic. Furthermore, when the alternative Twitter-based uncertainty index, Twitter-based Economic Uncertainty (TEU), is adopted, these results are also robust. This paper highlights the importance of the Twitter-based uncertainty index for oil futures market.

Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework

The purpose of this article is to investigate whether oil investor attention (OA), measured by Google search volume, contains incremental information content to predict crude oil futures volatility under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, to account for possible structural breaks and nonlinearity in the relation between OA and crude oil volatility, this article extends HAR-type models with regime switching considerations. The results of parameter estimation and out-of-sample prediction show that the in-sample and out-of-sample performance of HAR-type and Markov switching (MS)-HAR-type models with OA is significantly better than that of their corresponding HAR-type and MS-HAR-type models without OA. Furthermore, our findings suggest that (i) HAR-type-OA models tend to produce better forecasts for the volatility of the crude oil market at short horizons (1-day) compared to HAR-type, MS-HAR-type and MS-HAR-type-OA models. (ii) MS-HAR-type-OA models have the best forecasting performance at relatively long prediction horizons (1-week and 1-month). Therefore, the result suggests that the OA and regime switching specifications have a significant positive impact on volatility predictions and can be useful for improving the performance of HAR-type models.

Macroeconomic Uncertainty and Crude Oil Futures Volatility–Evidence from China Crude Oil Futures Market

This paper investigates whether the macroeconomic uncertainty factors can explain and forecast China’s INE crude oil futures market volatility. We use the GARCH-MIDAS model to investigate the explaining and predicting power of the macroeconomic uncertainties. We considered various geopolitical risk (GPR) indices, economic policy uncertainty (EPU) indices, and infectious disease pandemic (IDEMV) indices in our model. The empirical results suggest that the geopolitical risk, the geopolitical act risk, the global economic policy uncertainty, the economic policy uncertainty from the United Kingdom, and the economic policy uncertainty from Japan comprehensively integrate the information contained in the rest factors, and have superior predictive powers for INE crude oil future volatility. These findings highlight the importance of the impact of macroeconomic uncertainty factors has on the crude oil futures market, and indicate that the macroeconomic uncertainties need to be considered when explaining and forecasting crude oil futures market volatility.

Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach

AbstractVolatility is an important element for various financial instruments owing to its ability to measure the risk and reward value of a given financial asset. Owing to its importance, forecasting volatility has become a critical task in financial forecasting. In this paper, we propose a suite of hybrid models for forecasting volatility of crude oil under different forecasting horizons. Specifically, we combine the parameters of generalized autoregressive conditional heteroscedasticity (GARCH) and Glosten–Jagannathan–Runkle (GJR)‐GARCH with long short‐term memory (LSTM) to create three new forecasting models named GARCH–LSTM, GJR‐LSTM, and GARCH‐GJRGARCH LSTM in order to forecast crude oil volatility of West Texas Intermediate on different forecasting horizons and compare their performance with the classical volatility forecasting models. Specifically, we compare the performances against existing methodologies of forecasting volatility such as GARCH and found that the proposed hybrid models improve upon the forecasting accuracy of Crude Oil: West Texas Intermediate under various forecasting horizons and perform better than GARCH and GJR‐GARCH, with GG–LSTM performing the best of the three proposed models at 7‐, 14‐, and 21‐day‐ahead forecasts in terms of heteroscedasticity‐adjusted mean square error and heteroscedasticity‐adjusted mean absolute error, with significance testing conducted through the model confidence set showing that GG–LSTM is a strong contender for forecasting crude oil volatility under different forecasting regimes and rolling‐window schemes. The contribution of the paper is that it enhances the forecasting ability of crude oil futures volatility, which is essential for trading, hedging, and purposes of arbitrage, and that the proposed model dwells upon existing literature and enhances the forecasting accuracy of crude oil volatility by fusing a neural network model with multiple econometric models.

Global financial uncertainties and China’s crude oil futures market: Evidence from interday and intraday price dynamics

This paper investigates the impacts of five global financial market uncertainties on the interday and intraday price dynamics of newly launched China’s crude oil futures, using both conventional causality tests and a novel nonparametric causality-in-quantiles test. The empirical results show that, first, the international stock, oil and gold market uncertainties Granger cause China’s crude oil futures returns. However, the international silver and exchange rate market uncertainties have no such effect. Second, China’s crude oil futures volatility, including interday volatility, intraday volatility and its components, moves tightly with all the global financial uncertainties. Third, concerning the asymmetries of Granger causal relationships, the impacts of financial uncertainties on the different conditional distributions of China’s crude oil futures series appear as saddle shapes in general. Then, the financial uncertainties exert significant effects on oil intraday volatility mainly through jump components instead of continuous volatility. Finally, the bad volatility of China’s crude oil futures is more susceptible to global financial uncertainties than good volatility. All the conclusions are robust after further controlling the effects of economic policy uncertainty and adopting alternative uncertainty measures based on the unpredictable components of asset returns or based on public sentiment. • The responses of the newly emerged China’s crude oil futures market to global financial uncertainties are analyzed. • The global financial uncertainties are measured by implied volatility, unpredictable components of asset returns or public sentiment. • We discuss the predictability of three oil interday price dynamics, as well as that of eight intraday price dynamics. • The linear, nonlinear and quantile causality tests and the novel nonparametric causality-in-quantiles test are simultaneously used.

The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach

In this paper, we extract the qualitative information from crude oil news headlines, and develop a novel VMD-BiLSTM model with investor sentiment indicator for crude oil forecasting. First, we construct a sentiment score considering cumulative effect from contextual data of oil news texts. Then, we adopt an event-based method and GARCH model to investigate the impact of news sentiment on returns and volatility. A non-recursive signal decomposition method, namely variational mode decomposition (VMD), is applied to decompose the historical crude oil return and volatility data into various intrinsic modes. After that, a bidirectional long short-term memory neural networks (BiLSTM) is introduced as the deep learning prediction model that integrates both the qualitative and quantitative model inputs. Our empirical results indicate that the shock of news sentiment significantly causes the fluctuation of oil futures prices, and news sentiment has an asymmetric impact on the volatility of oil futures. The incorporation of sentiment score is always helpful for improving the forecasting performances in all benchmark scenarios. Specifically, our proposed data-decomposition based deep learning model is more effective than several econometric and machine learning models.

Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model

AbstractIn this study, we investigate the impacts of common information between oil futures and the United States stock markets on forecasting oil volatility using the multivariate heterogeneous autoregressive realized volatility model. We have several noteworthy findings. First, the in‐sample estimation results show that the negative returns and the ‘bad’ jumps have larger influence on oil futures volatility than positive returns and ‘good’ jumps, respectively. Second, the out‐of‐sample results further indicate that our multivariate heterogeneous autoregressive (HAR) model can generate higher forecast accuracy than individual HAR model for the realized volatility‐type models, which highlights the importance of incorporating volatility information from the stock Market. Third, our findings are robust across the direction‐of‐change test and different windows. Furthermore, from the perspective of the investors' allocation portfolio in the future, the use of common information can also bring greater gains.

Jumps and oil futures volatility forecasting: a new insight

This study designs the Markov-switching (MS) mixed data sampling (MIDAS) models and then explores the effects of intraday and interday jumps on oil futures price realized volatility. The in-sample estimates show that the current realized volatility and jumps have significantly positive impacts on oil futures price volatility using MIDAS models. However, the effects of the jumps are mixed when using MS-MIDAS models. Out-of-sample evaluations show that our proposed model, which includes continuous volatility and jumps based on the Time-of-Day volatility pattern (TOD) test combined with the Markov-switching MIDAS model (MS-MIDAS-TOD), exhibits a higher predictive ability for forecasting one-day-ahead oil futures volatility. Moreover, we further find that the improved results of this model remain significant over medium-term and long-term horizons and can also outperform a simple combination forecast.

Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models

This study evaluates whether CBOE crude oil volatility index (OVX) owns forecasting ability for China’s oil futures volatility using Markov-regime mixed data sampling (MS-MIDAS) models. In-sample empirical result shows that, OVX can significantly lead to high future short-term, middle-term and long-term volatilities with regard to Chinese oil futures market. Moreover, our proposed model, the Markov-regime MIDAS with including the OVX (MS-MIDAS-RV-OVX), significantly outperforms the MIDAS and other competing models. Unsurprising results further confirm that OVX indeed contain predictive information for oil realized volatility (especially significant and robust in middle-term and long-term horizons) and regime switching is useful to deal with the structural break within the energy market. We carry out economic value analysis and discuss OVX’s asymmetric effects concerning different trading hours and good (bad) OVX, and find OVX performs better in day-time trading hours and the good OVX is more predictive for the oil futures RV than the bad OVX. The further discussion also confirms our previous conclusions are robust during the highly volatile period of the COVID-19 pandemic.

Economic determinants of oil futures volatility: A term structure perspective

To assess the economic determinants of oil futures volatility, we firstly develop and estimate a multi-factor oil futures pricing model with stochastic volatility that is able to disentangle long-term, medium-term and short-term variations in commodity markets volatility. The volatility estimates reveal that in line with theory, the volatility factors are unspanned, persistent and carry negative market price of risk, while crude oil markets are becoming more integrated with financial markets. After 2004, short-term volatility is driven by industrial production, term and credit spreads, the S&P 500 and the US dollar index, along with the traditional drivers including hedging pressure and VIX. Medium-term volatility is consistently related to open interest and credit spreads, while after 2004 oil sector variables such as inventory and consumption also impact this part of the term structure. Interest rates mostly matter for long-term futures price volatility.

Impact of international energy prices on China's industries

AbstractThis 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.

Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models

Using a textual analysis based geopolitical risk (GPR) index, this paper exploits the effects of geopolitical risk uncertainty on oil futures price volatility within a mixed data sampling (MIDAS) modeling framework. With a variety of MIDAS specifications, our in-sample estimation results suggest that the short-term (e.g. one-day-ahead) oil realized volatility is positively associated with GPR uncertainty, and our out-of-sample forecasting exercise indicates that the GPR index is useful for improving short-term oil futures volatility prediction. In addition, we find that the categorical GPR index: GPR action related index (GPA), contributes more to the long-term oil volatility forecasting, compared with GPR threat related index (GPT).

The global influence of oil futures-prices on Dow Jones Islamic stock indexes

PurposeThe purpose of this paper is to investigate the global influence of crude and refined oil futures prices on Dow Jones Islamic equity indices (DJIMI) during the recent global financial crisis under structural breaks in the conditional volatility of oil futures prices.Design/methodology/approachIt aims at exploring the long-run and the short-run elasticity and causal relationships using an ARDL bound testing approach and a vector error correction model.FindingsThe main findings confirm the presence of long-run relationship for DJIM emerging markets index compared to other global and sub-regional developed indexes. Speed of adjustment to the long-run equilibrium is moderate and the effect of structural breaks, produced from nonlinear volatility model with long memory (LM), is overall not pronounced for that relationship. Short-run causality is bi-directional but long-run Granger causality does not run from refined oil to the DJIMI and crude oil.Research limitations/implicationsThe paper demonstrates the implicit extent of international financial integration of Islamic stock markets in light of the global influence of oil prices.Practical implicationsThe findings offer some highlights to researchers, portfolio managers and policymakers.Originality/valueThe paper gives an answer to an identified need to test the position of Islamic equity markets as booming Islamic investment and socially responsible investment areas to the global influence of the new soaring path of oil markets. It uses as well bounds testing approach and tests weak and strong causalities under structural breaks. It considers as well LM behavior in oil prices along with the asymmetry property in oil prices.