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Nonparametric specification tests for stochastic volatility models based on volatility density

Zu, Yang

Authors

YANG ZU yang.zu@nottingham.ac.uk
Assistant Professor



Abstract

© 2015 Elsevier B.V. This paper develops a specification test for stochastic volatility models by comparing the nonparametric kernel deconvolution density estimator of an integrated volatility density with its parametric counterpart. L2 distance is used to measure the discrepancy. The asymptotic null distributions of the test statistics are established and the asymptotic power functions are computed. Through Monte Carlo simulations, the size and power properties of the test statistics are studied. The tests are applied to an empirical example.

Citation

Zu, Y. (2015). Nonparametric specification tests for stochastic volatility models based on volatility density. Journal of Econometrics, 187(1), 323-344. https://doi.org/10.1016/j.jeconom.2015.02.045

Journal Article Type Article
Acceptance Date Feb 18, 2015
Online Publication Date Mar 24, 2015
Publication Date Jul 1, 2015
Deposit Date Sep 13, 2017
Publicly Available Date Sep 13, 2017
Journal Journal of Econometrics
Print ISSN 0304-4076
Electronic ISSN 1872-6895
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 187
Issue 1
Pages 323-344
DOI https://doi.org/10.1016/j.jeconom.2015.02.045
Keywords Nonparametric tests, Kernel deconvolution estimator, Stochastic volatility model
Public URL http://eprints.nottingham.ac.uk/id/eprint/45843
Publisher URL http://www.sciencedirect.com/science/article/pii/S0304407615001190
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
Additional Information This article is maintained by: Elsevier; Article Title: Nonparametric specification tests for stochastic volatility models based on volatility density; Journal Title: Journal of Econometrics; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jeconom.2015.02.045; Content Type: article; Copyright: Copyright © 2015 Elsevier B.V. All rights reserved.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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