Forecasting volatility with noisy jumps: an application to the Dow Jones Industrial Average stocks
AbstractEmpirical 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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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 27 (2008)
Issue (Month): 3 ()
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
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