City Research Online

Abstract

This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous components using estimators which are not only consistent, but also scarcely plagued by small sample bias. With the aim of achieving this, we introduce the concept of threshold bipower variation, which is based on the joint use of bipower variation and threshold estimation. We show that its generalization (threshold multipower variation) admits a feasible central limit theorem in the presence of jumps and provides less biased estimates, with respect to the standard multipower variation, of the continuous quadratic variation in finite samples. We further provide a new test for jump detection which has substantially more power than tests based on multipower variation. Empirical analysis (on the S&P500 index, individual stocks and US bond yields) shows that the proposed techniques improve significantly the accuracy of volatility forecasts especially in periods following the occurrence of a jump.

Publication Type:	Article
Additional Information:	NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Econometrics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Econometrics, Volume 159, Issue 2, December 2010, Pages 276–288, http://dx.doi.org/10.1016/j.jeconom.2010.07.008
Publisher Keywords:	Volatility estimation, Jump detection, Volatility forecasting, Threshold estimation, Financial markets
Subjects:	H Social Sciences > HB Economic Theory
Departments:	School of Policy & Global Affairs > Economics
Related URLs:	Author
SWORD Depositor:	Symplectic Administrator

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