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Do high-frequency financial data help forecast oil prices? The MIDAS touch at work

Listed author(s):
  • Baumeister, Christiane
  • Guérin, Pierre
  • Kilian, Lutz

The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models may be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, especially changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 82 percent. This MIDAS forecast also is more accurate than a mixed-frequency realtime VAR forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil.

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Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2013/22.

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Date of creation: 2013
Handle: RePEc:zbw:cfswop:201322
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  1. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
  2. Hamilton, James D. & Wu, Jing Cynthia, 2014. "Risk premia in crude oil futures prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 9-37.
  3. Baumeister, Christiane & Kilian, Lutz & Zhou, Xiaoqing, 2013. "Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis," CEPR Discussion Papers 9572, C.E.P.R. Discussion Papers.
  4. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Working Paper Series 42_10, The Rimini Centre for Economic Analysis.
  5. Christopher R. Knittel & Robert S. Pindyck, 2013. "The Simple Economics of Commodity Price Speculation," NBER Working Papers 18951, National Bureau of Economic Research, Inc.
  6. Christiane Baumeister & Lutz Kilian, 2013. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Staff Working Papers 13-28, Bank of Canada.
  7. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
  8. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions," PIER Working Paper Archive 07-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  9. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank, Research Centre.
  10. Christiane Baumeister & Lutz Kilian, 2013. "What Central Bankers Need to Know about Forecasting Oil Prices," Staff Working Papers 13-15, Bank of Canada.
  11. Frank Schorfheide & Dongho Song, 2012. "Real-time forecasting with a mixed-frequency VAR," Working Papers 701, Federal Reserve Bank of Minneapolis.
  12. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
  13. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
  14. Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City.
  15. Ching Wai (Jeremy) Chiu & Bjørn Eraker & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2011. "Estimating VAR's sampled at mixed or irregular spaced frequencies : a Bayesian approach," Research Working Paper RWP 11-11, Federal Reserve Bank of Kansas City.
  16. Ye, Michael & Zyren, John & Shore, Joanne, 2005. "A monthly crude oil spot price forecasting model using relative inventories," International Journal of Forecasting, Elsevier, vol. 21(3), pages 491-501.
  17. Kilian, Lutz & Lee, Thomas K., 2014. "Quantifying the speculative component in the real price of oil: The role of global oil inventories," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 71-87.
  18. Fattouh, Bassam & Kilian, Lutz & Mahadeva, Lavan, 2012. "The Role of Speculation in Oil Markets: What Have We Learned So Far?," CEPR Discussion Papers 8916, C.E.P.R. Discussion Papers.
  19. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
  20. Jeffrey A. Frankel, 2008. "The Effect of Monetary Policy on Real Commodity Prices," NBER Chapters, in: Asset Prices and Monetary Policy, pages 291-333 National Bureau of Economic Research, Inc.
  21. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
  22. James D. Hamilton, 2010. "Causes and consequences of the oil shock of 2007–08," FRB Atlanta CQER Working Paper 2009-02, Federal Reserve Bank of Atlanta.
  23. Chen, Shiu-Sheng, 2013. "Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks," MPRA Paper 49240, University Library of Munich, Germany.
  24. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
  25. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
  26. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
  27. Kilian, Lutz & Murphy, Daniel P, 2010. "The Role of Inventories and Speculative Trading in the Global Market for Crude Oil," CEPR Discussion Papers 7753, C.E.P.R. Discussion Papers.
  28. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
  29. repec:taf:jnlbes:v:30:y:2012:i:2:p:326-336 is not listed on IDEAS
  30. James D. Hamilton, 2008. "Daily Monetary Policy Shocks and the Delayed Response of New Home Sales," NBER Working Papers 14223, National Bureau of Economic Research, Inc.
  31. Hamilton, James D., 2008. "Daily monetary policy shocks and new home sales," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1171-1190, October.
  32. Baumeister, Christiane & Kilian, Lutz, 2011. "Real-Time Forecasts of the Real Price of Oil," CEPR Discussion Papers 8414, C.E.P.R. Discussion Papers.
  33. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 521-536.
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