Arbeitspapier

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

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.

Sprache
Englisch

Erschienen in
Series: CFS Working Paper ; No. 2013/22

Klassifikation
Wirtschaft
Forecasting Models; Simulation Methods
Information and Market Efficiency; Event Studies; Insider Trading
Energy and the Macroeconomy
Thema
Mixed frequency
Real-time data
Oil price
Forecasts

Ereignis
Geistige Schöpfung
(wer)
Baumeister, Christiane
Guérin, Pierre
Kilian, Lutz
Ereignis
Veröffentlichung
(wer)
Goethe University Frankfurt, Center for Financial Studies (CFS)
(wo)
Frankfurt a. M.
(wann)
2013

Handle
URN
urn:nbn:de:hebis:30:3-324998
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Baumeister, Christiane
  • Guérin, Pierre
  • Kilian, Lutz
  • Goethe University Frankfurt, Center for Financial Studies (CFS)

Entstanden

  • 2013

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