Spot volatility estimation for high-frequency data

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 highfrequency return data. We establish both pointwise and global asymptotic distributions for the estimators. Jianqing Fan is Frederick Moore’18 Professor of Finance, Department of Operation Research and Financial Engineering, Princeton University, Princeton, NJ 08544 and Director, Center for Statistical Research, Chinese Academy of Science. Yazhen Wang is professor, Department of Statistics, University of Connecticut, Storrs, CT 06269. Fan’s research was partially supported by the NSF grant DMS-0532370 and Chinese NSF grant 10628104, and Wang’s research was partially supported by the NSF grant DMS-0504323. The authors thank Per Mykland for helpful comments and suggestions.