Statistics and Its Interface

Volume 1 (2008)

Number 2

Spot volatility estimation for high-frequency data

Pages: 279 – 288



Jianqing Fan (Princeton University, Princeton, New Jersey, U.S.A.)

Yazhen Wang (University of Connecticut, Storrs, Conn., U.S.A.)


The availability of high-frequency intraday data allows us to accurately estimate stock volatility. This paper employs a bivariate diffusion to model the price and volatility of an asset and investigates kernel type estimators of spot volatility based on high-frequency return data. We establish both pointwise and global asymptotic distributions for the estimators.


asymptotic normality, CIR model, constant elasticity of diffusion, extreme distribution, kernel estimator, long memory, stock price

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