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

Basel Awartani ()

Journal of Forecasting, 2008, vol. 27, issue 3, 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.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:27:y:2008:i:3:p:267-278

DOI: 10.1002/for.1057

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