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Asymptotic Normality of Convoluted Smoothed Kernel Estimation for Scalar Diffusion Model

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  • Yuping Song

    (Shanghai Normal University)

  • Weijie Hou

    (Shanghai Normal University)

  • Guang Yang

    (Shanghai Normal University)

Abstract

In this paper, we consider a convoluted smoothed nonparametric approach for the unknown coefficients of diffusion model based on high frequency data. Under regular conditions, we obtain the asymptotic normality for the proposed estimators as the time span T →∞ and sample interval Δn → 0. The procedure and asymptotic behavior can be applied for both Harris recurrent and positive Harris recurrent processes. The finite-sample benefits of the underlying estimators are verified through Monte Carlo simulation and 15-min high-frequency stock index in Shanghai Stock Exchange for an empirical application.

Suggested Citation

  • Yuping Song & Weijie Hou & Guang Yang, 2020. "Asymptotic Normality of Convoluted Smoothed Kernel Estimation for Scalar Diffusion Model," Methodology and Computing in Applied Probability, Springer, vol. 22(1), pages 191-221, March.
    • Handle: RePEc:spr:metcap:v:22:y:2020:i:1:d:10.1007_s11009-019-09696-7
    DOI: 10.1007/s11009-019-09696-7
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    References listed on IDEAS

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