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

Awartani Basel M. A. | Zendy

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

Author(s) -

Awartani Basel M. A.

Publication year - 2008

Publication title -

journal of forecasting

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.543

H-Index - 59

eISSN - 1099-131X

pISSN - 0277-6693

DOI - 10.1002/for.1057

Subject(s) - volatility (finance) , jump , econometrics , realized variance , economics , forward volatility , stochastic volatility , mathematics , statistics , physics , quantum mechanics

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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