Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Power and Bipower Variation with Stochastic Volatility and Jumps

Power and Bipower Variation with Stochastic Volatility and Jumps This article shows that realized power variation and its extension, realized bipower variation, which we introduce here, are somewhat robust to rare jumps. We demonstrate that in special cases, realized bipower variation estimates integrated variance in stochastic volatility models, thus providing a model-free and consistent alternative to realized variance. Its robustness property means that if we have a stochastic volatility plus infrequent jumps process, then the difference between realized variance and realized bipower variation estimates the quadratic variation of the jump component. This seems to be the first method that can separate quadratic variation into its continuous and jump components. Various extensions are given, together with proofs of special cases of these results. Detailed mathematical results are reported in Barndorff-Nielsen and Shephard (2003a). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Financial Econometrics Oxford University Press

Power and Bipower Variation with Stochastic Volatility and Jumps

Loading next page...
 
/lp/oxford-university-press/power-and-bipower-variation-with-stochastic-volatility-and-jumps-HYTQIGOFPh

References (65)

Publisher
Oxford University Press
Copyright
© 2004 Oxford University Press; all rights reserved.
ISSN
1479-8409
eISSN
1479-8417
DOI
10.1093/jjfinec/nbh001
Publisher site
See Article on Publisher Site

Abstract

This article shows that realized power variation and its extension, realized bipower variation, which we introduce here, are somewhat robust to rare jumps. We demonstrate that in special cases, realized bipower variation estimates integrated variance in stochastic volatility models, thus providing a model-free and consistent alternative to realized variance. Its robustness property means that if we have a stochastic volatility plus infrequent jumps process, then the difference between realized variance and realized bipower variation estimates the quadratic variation of the jump component. This seems to be the first method that can separate quadratic variation into its continuous and jump components. Various extensions are given, together with proofs of special cases of these results. Detailed mathematical results are reported in Barndorff-Nielsen and Shephard (2003a).

Journal

Journal of Financial EconometricsOxford University Press

Published: Jan 1, 2004

There are no references for this article.