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Jumps in Oil Prices: The Role of Economic News

Author

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  • John Elder
  • Hong Miao
  • Sanjay Ramchander

Abstract

Previous research has been unable to identify a strong link between crude oil prices and economic news. We reexamine this relationship using high frequency intraday data and relatively new methodology to estimate jumps in oil prices. We find a surprisingly strong correspondence between high frequency jumps in oil prices and the arrival of new economic information, with the largest jumps tending to be preceded identifiable economic news. These results indicate that oil prices respond very rapidly to new economic data in ways that appear consistent with economic theory, and also suggest that economic news, rather than speculation unrelated to the economic environment, drives jumps in oil prices.

Suggested Citation

  • John Elder & Hong Miao & Sanjay Ramchander, 2013. "Jumps in Oil Prices: The Role of Economic News," The Energy Journal, , vol. 34(3), pages 217-237, July.
    • Handle: RePEc:sae:enejou:v:34:y:2013:i:3:p:217-237
    DOI: 10.5547/01956574.34.3.10
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    References listed on IDEAS

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    1. repec:bla:jfinan:v:59:y:2004:i:2:p:755-793 is not listed on IDEAS
    2. Torben G. Andersen & Tim Bollerslev & Per Frederiksen & Morten Ørregaard Nielsen, 2010. "Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 233-261.
    3. repec:taf:jnlbes:v:30:y:2012:i:2:p:242-255 is not listed on IDEAS
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