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.

[1]  Krzysztof Drachal,et al.  Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time? , 2016 .

[2]  Rodney W. Strachan,et al.  On the evolution of the monetary policy transmission mechanism , 2009 .

[3]  Haichun Ye,et al.  Understanding the Crude Oil Price: How Important Is the China Factor? , 2010 .

[4]  Libo Yin.  Does oil price respond to macroeconomic uncertainty? New evidence , 2016 .

[5]  H. Mohammadi,et al.  International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models , 2010 .

[6]  Eric Ghysels,et al.  Forecasting Real Estate Prices , 2012 .

[7]  L. Kilian.  Not All Oil Price Shocks are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market , 2006 .

[8]  Rangan Gupta,et al.  The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method , 2017 .

[9]  Giorgio E. Primiceri.  Time Varying Structural Vector Autoregressions and Monetary Policy , 2002 .

[10]  J. Stock,et al.  Combination forecasts of output growth in a seven-country data set , 2004 .

[11]  G. Koop,et al.  UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So? , 2009 .

[12]  Seong-Min Yoon,et al.  Forecasting volatility of crude oil markets , 2009 .

[13]  Feng Ma,et al.  Forecasting the realized volatility in the Chinese stock market: further evidence , 2016 .

[14]  Feng Ma,et al.  Forecasting the realized range-based volatility using dynamic model averaging approach , 2017 .

[15]  Feng Ma,et al.  Forecasting realized volatility in a changing world: A dynamic model averaging approach , 2016 .

[16]  Chin Wen Cheong,et al.  Modeling and forecasting crude oil markets using ARCH-type models , 2009 .

[17]  Adrian E. Raftery,et al.  Prediction under Model Uncertainty Via Dynamic Model Averaging : Application to a Cold Rolling Mill 1 , 2008 .

[18]  Eric Ghysels,et al.  Stock Market Volatility and Macroeconomic Fundamentals , 2013, Review of Economics and Statistics.

[19]  Olivier Darné,et al.  Volatility persistence in crude oil markets , 2014 .

[20]  P. Hansen,et al.  A Forecast Comparison of Volatility Models: Does Anything Beat a Garch(1,1)? , 2004 .

[21]  Luciana Juvenal,et al.  Speculation in the Oil Market: SPECULATION IN THE OIL MARKET , 2015 .

[22]  L. Kilian,et al.  The Impact of Oil Price Shocks on the U.S. Stock Market , 2007 .

[23]  James D. Hamilton.  Causes and Consequences of the Oil Shock of 2007–08 , 2009 .

[24]  Rangan Gupta,et al.  Uncertainty and Crude Oil Returns , 2015 .

[25]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[26]  T. Bollerslev,et al.  A CONDITIONALLY HETEROSKEDASTIC TIME SERIES MODEL FOR SPECULATIVE PRICES AND RATES OF RETURN , 1987 .

[27]  George Filis,et al.  Dynamic Co-Movements of Stock Market Returns, Implied Volatility and Policy Uncertainty , 2013 .

[28]  Robert K. Kaufmann,et al.  Modelling the world oil market: Assessment of a quarterly econometric model , 2007 .

[29]  C. Liu,et al.  Are There Structural Breaks in Realized Volatility , 2008 .

[30]  E. Ghysels,et al.  Série Scientifique Scientific Series Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies , 2022 .

[31]  Yue-Jun Zhang,et al.  Speculative trading and WTI crude oil futures price movement: An empirical analysis , 2013 .

[32]  Farrukh Javed,et al.  Importance of the macroeconomic variables for variance prediction: A GARCH-MIDAS approach , 2012 .

[33]  Fabian Gogolin,et al.  Does Speculation Impact What Factors Determine Oil Futures Prices? , 2016 .

[34]  Kostas Andriosopoulos,et al.  Modelling energy spot prices: Empirical evidence from NYMEX , 2012 .

[35]  Christian Conrad,et al.  On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets , 2014 .

[36]  I-Chun Tsai,et al.  The source of global stock market risk: A viewpoint of economic policy uncertainty , 2017 .

[37]  Peter Reinhard Hansen,et al.  The Model Confidence Set , 2010 .

[38]  S. Davis,et al.  Measuring Economic Policy Uncertainty , 2013 .

[39]  P. Hansen.  A Test for Superior Predictive Ability , 2005 .

[40]  Eric Ghysels,et al.  Série Scientifique Scientific Series the Midas Touch: Mixed Data Sampling Regression Models the Midas Touch: Mixed Data Sampling Regression Models* , 2022 .

[41]  Flávio Augusto Ziegelmann,et al.  Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA , 2014 .

[42]  Stelios D. Bekiros,et al.  Oil Price Forecastability and Economic Uncertainty , 2015, SSRN Electronic Journal.

[43]  G. Koop,et al.  Forecasting In ation Using Dynamic Model Averaging , 2009 .

[44]  Ivan Petrella,et al.  Speculation in the Oil Market , 2011 .

[45]  Libo Yin,et al.  What Drives Long-term Oil Market Volatility? Fundamentals versus Speculation , 2016 .

[46]  R. Engle,et al.  The Spline-Garch Model for Low Frequency Volatility and its Global Macroeconomic Causes , 2006 .

[47]  Uncertainty about Government Policy and Stock Prices , 2010 .

[48]  James D. Hamilton,et al.  Risk Premia in Crude Oil Futures Prices * , 2011 .

[49]  Perry Sadorsky,et al.  Modeling and forecasting petroleum futures volatility , 2006 .

[50]  E. Ghysels,et al.  MIDAS Regressions: Further Results and New Directions , 2006 .

[51]  Wensheng Kang,et al.  Oil shocks, policy uncertainty and stock market return , 2013 .

[52]  N. Antonakakis,et al.  Dynamic spillovers of oil price shocks and economic policy uncertainty , 2014 .

[53]  Yudong Wang,et al.  Disentangling the determinants of real oil prices , 2016 .

[54]  K. West,et al.  Asymptotic Inference about Predictive Ability , 1996 .

[55]  Seema Narayan,et al.  Modelling oil price volatility , 2007 .

[56]  Jiahai Yuan,et al.  Energy consumption and economic growth : Evidence from China at both aggregated and disaggregated levels , 2008 .

[57]  Hanan Naser,et al.  Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach , 2016 .

[58]  Yu Wei,et al.  Forecasting crude oil market volatility: Further evidence using GARCH-class models , 2010 .

[59]  Apostolos Serletis,et al.  Energy markets volatility modelling using GARCH , 2014 .

[60]  H. White,et al.  A Reality Check for Data Snooping , 2000 .

[61]  Lutz Kilian,et al.  The Role of Inventories and Speculative Trading in the Global Market for Crude Oil , 2010 .

[62]  Shigeyuki Hamori,et al.  Modeling interest rate volatility: A Realized GARCH approach , 2015 .

[63]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[64]  Valentina Colombo.  Economic policy uncertainty in the US: Does it matter for the Euro area? , 2013 .