Asymptotic equivalence for inference on the volatility from noisy observations

Details Asymptotic equivalence for inference on the volatility from noisy observations Asymptotic equivalence for inference on the volatility from noisy observations

2011-05-11

arxiv.org/abs/1105.2128v1

Annals of Statistics 2011, Vol. 39, No. 2, 772-802

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/10-AOS855 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of

Scientific paper

10.1214/10-AOS855

We consider discrete-time observations of a continuous martingale under measurement error. This serves as a fundamental model for high-frequency data in finance, where an efficient price process is observed under microstructure noise. It is shown that this nonparametric model is in Le Cam's sense asymptotically equivalent to a Gaussian shift experiment in terms of the square root of the volatility function $\sigma$ and a nonstandard noise level. As an application, new rate-optimal estimators of the volatility function and simple efficient estimators of the integrated volatility are constructed.

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