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

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  • Baumeister, Christiane
  • Guérin, Pierre
  • Kilian, Lutz

Abstract

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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Bibliographic Info

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
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Handle: RePEc:zbw:cfswop:201322

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Keywords: Mixed frequency; Real-time data; Oil price; Forecasts;

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  1. Frank Schorfheide & Dongho Song, 2012. "Real-time forecasting with a mixed-frequency VAR," Working Papers 701, Federal Reserve Bank of Minneapolis.
  2. 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.
  3. 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.
  4. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers 8828, C.E.P.R. Discussion Papers.
  5. Kilian, Lutz, 2006. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," CEPR Discussion Papers 5994, C.E.P.R. Discussion Papers.
  6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  7. 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.
  8. 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.
  9. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2007. "Regression Models with Mixed Sampling Frequencies," University of Cyprus Working Papers in Economics 8-2007, University of Cyprus Department of Economics.
  10. Pesaran, M.H. & Timmermann, A., 2006. "Testing Dependence Among Serially Correlated Multi-category Variables," Cambridge Working Papers in Economics 0648, Faculty of Economics, University of Cambridge.
  11. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
  12. 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.
  13. Hamilton, James D., 2008. "Daily monetary policy shocks and new home sales," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1171-1190, October.
  14. repec:taf:jnlbes:v:30:y:2012:i:2:p:326-336 is not listed on IDEAS
  15. 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.
  16. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
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