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

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Author Info
Basel M. A. Awartani (School of Management, New York Institute of Technology, CERT Technology Park, Abu Dhabi, United Arab Emirates)
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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File URL: http://hdl.handle.net/10.1002/for.1057
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 27 (2008)
Issue (Month): 3 ()
Pages: 267-278
Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Handle: RePEc:jof:jforec:v:27:y:2008:i:3:p:267-278

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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This page was last updated on 2008-8-6.

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