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Forecasting volatility with noisy jumps: an application to the Dow Jones Industrial Average stocks


  • Basel M. A. Awartani

    (School of Management, New York Institute of Technology, CERT Technology Park, Abu Dhabi, United Arab Emirates)


Empirical high-frequency data can be used to separate the continuous and the jump components of realized volatility. This may improve on the accuracy of out-of-sample realized volatility forecasts. A further improvement may be realized by disentangling the two components using a sampling frequency at which the market microstructure effect is negligible, and this is the objective of the paper. In particular, a significant improvement in the accuracy of volatility forecasts is obtained by deriving the jump information from time intervals at which the noise effect is weak. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Basel M. A. Awartani, 2008. "Forecasting volatility with noisy jumps: an application to the Dow Jones Industrial Average stocks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 267-278.
  • Handle: RePEc:jof:jforec:v:27:y:2008:i:3:p:267-278 DOI: 10.1002/for.1057

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    References listed on IDEAS

    1. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 1-30.
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    7. Bates, David S, 1996. "Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options," Review of Financial Studies, Society for Financial Studies, vol. 9(1), pages 69-107.
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