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Consistent nonparametric specification tests for stochastic volatility models based on the return distribution

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  • Zu, Y.

Abstract

This paper develops nonparametric specification tests for stochastic volatility models by comparing the nonparametically estimated return density and distribution functions with their parametric counterparts. Asymptotic null distributions of the tests are derived and the tests are shown to be consistent. Extensive Monte Carlo experiments are performed to study the finite sample properties of the tests. The tests are applied to an empirical dataset and we find the estimated stochastic volatility model is misspecified.

Suggested Citation

  • Zu, Y., 2015. "Consistent nonparametric specification tests for stochastic volatility models based on the return distribution," Working Papers 15/02, Department of Economics, City University London.
    • Handle: RePEc:cty:dpaper:15/02
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    File URL: https://openaccess.city.ac.uk/id/eprint/12206/1/Consistent%20nonparametric%20specification%20tests_Yang%20Zu_1502.pdf
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    References listed on IDEAS

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    Cited by:

    1. Christensen, Kim & Thyrsgaard, Martin & Veliyev, Bezirgen, 2019. "The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing," Journal of Econometrics, Elsevier, vol. 212(2), pages 556-583.
    2. Yinfen Tang & Tao Su & Zhiyuan Zhang, 2022. "Distribution-free specification test for volatility function based on high-frequency data with microstructure noise," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(8), pages 977-1022, November.
    3. Zu, Yang & Boswijk, H. Peter, 2017. "Consistent nonparametric specification tests for stochastic volatility models based on the return distribution," Journal of Empirical Finance, Elsevier, vol. 41(C), pages 53-75.

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