Nonparametric specification tests for stochastic volatility models based on volatility density
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 th...
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| Format: | Article |
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Elsevier
2015
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| Online Access: | https://eprints.nottingham.ac.uk/45843/ |
| _version_ | 1848797204851982336 |
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| author | Zu, Yang |
| author_facet | Zu, Yang |
| author_sort | Zu, Yang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:00:10Z |
| format | Article |
| id | nottingham-45843 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:00:10Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-458432020-05-04T17:04:17Z https://eprints.nottingham.ac.uk/45843/ Nonparametric specification tests for stochastic volatility models based on volatility density Zu, Yang 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. Elsevier 2015-03-24 Article PeerReviewed Zu, Yang (2015) Nonparametric specification tests for stochastic volatility models based on volatility density. Journal of Econometrics, 187 . pp. 323-344. ISSN 0304-4076 Nonparametric tests Kernel deconvolution estimator Stochastic volatility model http://www.sciencedirect.com/science/article/pii/S0304407615001190 doi:10.1016/j.jeconom.2015.02.045 doi:10.1016/j.jeconom.2015.02.045 |
| spellingShingle | Nonparametric tests Kernel deconvolution estimator Stochastic volatility model Zu, Yang Nonparametric specification tests for stochastic volatility models based on volatility density |
| title | Nonparametric specification tests for stochastic volatility models based on volatility density |
| title_full | Nonparametric specification tests for stochastic volatility models based on volatility density |
| title_fullStr | Nonparametric specification tests for stochastic volatility models based on volatility density |
| title_full_unstemmed | Nonparametric specification tests for stochastic volatility models based on volatility density |
| title_short | Nonparametric specification tests for stochastic volatility models based on volatility density |
| title_sort | nonparametric specification tests for stochastic volatility models based on volatility density |
| topic | Nonparametric tests Kernel deconvolution estimator Stochastic volatility model |
| url | https://eprints.nottingham.ac.uk/45843/ https://eprints.nottingham.ac.uk/45843/ https://eprints.nottingham.ac.uk/45843/ |