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Variance reduction approach for the volatility over a finite-time horizon

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Listed:
  • Yuping Song
  • Zheng Sun
  • Qicheng Zhao
  • Youyou Chen

Abstract

The volatility is a measure for the uncertainty of an asset’s return and is used to reflect the risk level of a financial asset. In this article, we consider the double kernel nonparametric estimator for the volatility function in a diffusion model over a finite-time span based on high frequency sampling data. Under the minimum conditions, the asymptotic mixed normality for the underlying estimator is derived. Moreover, the better finite-sample performance as variance reduction and even mean squared error reduction of the proposed estimator is verified through a Monte Carlo simulation study and an empirical analysis on overnight Shibor in China.

Suggested Citation

  • Yuping Song & Zheng Sun & Qicheng Zhao & Youyou Chen, 2022. "Variance reduction approach for the volatility over a finite-time horizon," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(11), pages 3521-3541, June.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:11:p:3521-3541
    DOI: 10.1080/03610926.2020.1797803
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