There are two variance components embedded in the returns constructed using high frequency asset prices: the time-varying variance of the unobservable efficient returns that would prevail in a frictionless economy and the variance of the equally unobservable microstructure noise. Using sample moments of high frequency return data recorded at different frequencies, we provide a simple and robust technique to identify both variance components. In the context of a volatility-timing trading strategy, we show that careful (optimal) separation of the two volatility components of the observed stock returns yields substantial utility gains.

Observed asset prices are known to deviate from their efficient values due to market microstructure frictions. This paper studies the effects of market microstructure noise on nonparametric estimates of the efficient price integrated variance. Specifically, we consider both asymptotic and finite sample effects of general market microstructure noise on realized variance estimates. The finite sample results culminate in a variance/bias trade-off that serves as a basis for an optimal sampling theory. Our theory also considers the effects of pre-filtering the data and proposes a novel bias-correction. We show that this theory is easily implementable in practise requiring only the calculation of sample moments of the observable high-frequency return data.

The notion of realized volatility as a model-free measurement of the quadratic variation of the underlying log price process loses its asymptotic validity in the presence of market microstructure noise. Should microstructure contaminations be present, the summing of an increasing number of squared return data (as in the definition of the realized volatility estimator) simply entails increasing accumulation of noise. Using asymptotic arguments as in the extant theoretical literature on the subject, we show that the realized volatility estimator diverges to infinity almost surely when noise plays a role as in a realistic price formation mechanism. We also show that, while the quadratic variation of the log price process cannot be estimated consistently, an appropriately standardized version of the realized volatility estimator can be employed to uncover a specific feature of the noise distribution, namely the second moment of the noise process. 1 1