Crude oil price analysis and forecasting: A perspective of “new triangle”

In this paper, the new structural characteristics and core influencing factors of the crude oil prices are summarized based on previous representative research results. Firstly, a newly dynamic Bayesian structural time series model (DBSTS) is developed to investigate the oil prices. In particular, Google trend is introduced as an indicator to reflect the impact of search data on the oil price. Secondly, the spike and slab method is employed to select core influence factors. Finally, the Bayesian model average (BMA) is utilized to predict the oil price. Experimental results confirm that the supply and demand of global crude oil and the financial market are still the main factors affecting the oil price. Furthermore, Google trend can reflect the changes in the crude oil price to a certain extent. Moreover, the impact of shale oil production on the oil price is gradually increasing, yet remains relatively small. In addition, the DBSTS model can identify turning points in historical data (such as the 2008 financial crisis). Finally, the findings suggest the DBSTS model has good predictive capabilities in short-term prediction, making it suitable for analyzing the crude oil prices.

[1]  A. Mollick,et al.  Oil price fluctuations and U.S. dollar exchange rates , 2010 .

[2]  Jian Chai,et al.  Exploring the core factors and its dynamic effects on oil price: An application on path analysis and BVAR-TVP model , 2011 .

[3]  D. Revel.  Understanding the Decline in the Price of Oil since June 2014 , 2015 .

[4]  M. Clyde,et al.  Mixtures of g Priors for Bayesian Variable Selection , 2008 .

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

[6]  Ling Tang,et al.  A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting , 2015 .

[7]  Serena Ng,et al.  Are more data always better for factor analysis , 2006 .

[8]  Sjur Westgaard,et al.  Forecasting Volatility of the U.S. Oil Market , 2014 .

[9]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[10]  Lixin Tian,et al.  A novel approach for oil price forecasting based on data fluctuation network , 2018 .

[11]  K. Lai,et al.  A new approach for crude oil price analysis based on Empirical Mode Decomposition , 2008 .

[12]  Tianyang Wang,et al.  Influential factors in crude oil price forecasting , 2017 .

[13]  Adrian E. Raftery,et al.  Bayesian Model Averaging: A Tutorial , 2016 .

[14]  S. L. Scott,et al.  Bayesian Variable Selection for Nowcasting Economic Time Series , 2013 .

[15]  Olivier Darné,et al.  Forecasting crude-oil market volatility: Further evidence with jumps , 2017 .

[16]  Gang Li,et al.  Forecasting tourist arrivals using time-varying parameter structural time series models , 2011 .

[17]  Perry Sadorsky.  Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat , 2014 .

[18]  Xin Li,et al.  How does Google search affect trader positions and crude oil prices , 2015 .

[19]  L. Kilian,et al.  Lower Oil Prices and the U.S. Economy: Is This Time Different? , 2017, SSRN Electronic Journal.

[20]  Edward I. George,et al.  The Practical Implementation of Bayesian Model Selection , 2001 .

[21]  Yudong Wang,et al.  Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models? , 2012 .

[22]  Fenghua Wen,et al.  Asymmetric impacts of oil price uncertainty on Chinese stock returns under different market conditions: Evidence from oil volatility index , 2018, Energy Economics.

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

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

[25]  Ronald A. Ratti,et al.  Oil prices and global factor macroeconomic variables , 2016 .

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

[27]  D. Madigan,et al.  Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window , 1994 .

[28]  James D. Hamilton,et al.  Understanding Crude Oil Prices , 2008 .

[29]  Yudong Wang,et al.  Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models , 2017 .

[30]  Jianping Li,et al.  A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting , 2012 .

[31]  P. Narayan,et al.  Do momentum-based trading strategies work in the commodity futures markets? , 2015 .

[32]  Jinliang Zhang,et al.  A novel hybrid method for crude oil price forecasting , 2015 .

[33]  Michael P. Clements,et al.  Evaluating The Forecast of Densities of Linear and Non-Linear Models: Applications to Output Growth and Unemployment , 2000 .

[34]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[35]  Li Yang,et al.  Forecasting crude oil market volatility: A Markov switching multifractal volatility approach , 2016 .

[36]  R. Smyth,et al.  How do daily changes in oil prices affect US monthly industrial output , 2017 .

[37]  Zhongyi Hu,et al.  Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices , 2013, ArXiv.

[38]  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 .

[39]  O. Blanchard.  [Do We Really Know That Oil Caused the Great Stagflation? A Monetary Alternative]: Comment , 2001, NBER Macroeconomics Annual.

[40]  Ronald A. Ratti,et al.  Crude oil prices and liquidity, the BRIC and G3 countries , 2013 .

[41]  Chun-Ping Chang,et al.  Do oil spot and futures prices move together , 2015 .

[42]  Anthony S. Tay,et al.  Evaluating Density Forecasts with Applications to Financial Risk Management , 1998 .

[43]  Ying Fan,et al.  How Does Oil Price Volatility Affect Non-Energy Commodity Markets? , 2012 .

[44]  Zebin Yang,et al.  Online big data-driven oil consumption forecasting with Google trends , 2019, International Journal of Forecasting.

[45]  Robert J. Vigfusson,et al.  The Role of Oil Price Shocks in Causing U.S. Recessions , 2014, SSRN Electronic Journal.

[46]  Jennifer L. Castle,et al.  How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms , 2009 .

[47]  E. George,et al.  APPROACHES FOR BAYESIAN VARIABLE SELECTION , 1997 .

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

[49]  Jian Chai,et al.  Forecasting the WTI crude oil price by a hybrid-refined method , 2018 .

[50]  Luiz Fernando Loureiro Legey,et al.  Forecasting oil price trends using wavelets and hidden Markov models , 2010 .

[51]  Jianping Li,et al.  A deep learning ensemble approach for crude oil price forecasting , 2017 .

[52]  Hui Bu,et al.  Effects of Inventory Announcement on Crude Oil Price Volatility , 2014 .

[53]  Lu Zhang,et al.  Interpreting the crude oil price movements: Evidence from the Markov regime switching model , 2015 .

[54]  Steven L. Scott,et al.  Predicting the Present with Bayesian Structural Time Series , 2013 .