Nonparametric specification tests for stochastic volatility models based on volatility density

Zu, Yang

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: https://nottingham-repository.worktribe.com/output/747032
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0304407615001190
• 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.