Variance reduction approach for the volatility over a finite-time horizon,Communications in Statistics - Theory and Methods

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Variance reduction approach for the volatility over a finite-time horizon
Communications in Statistics - Theory and Methods ( IF 0.6 ) Pub Date : 2020-07-25 , DOI: 10.1080/03610926.2020.1797803
Yuping Song ₁ , Zheng Sun ₁ , Qicheng Zhao ₁ , Youyou Chen ₂

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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.

更新日期：2020-07-25

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