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

Basel M. A. Awartani
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Basel M. A. Awartani: School of Management, New York Institute of Technology, CERT Technology Park, Abu Dhabi, United Arab Emirates, Postal: School of Management, New York Institute of Technology, CERT Technology Park, Abu Dhabi, United Arab Emirates

Journal of Forecasting, 2008, vol. 27, issue 3, pages 267-278

Abstract: 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.

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