Asymptotic equivalence for inference on the volatility from noisy observations

Abstract

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.

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

References in corpus (3)

• Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps
• Asymptotic Equivalence for Nonparametric Regression
• Adaptive wavelet estimation of the diffusion coefficient under additive error measurements

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