Separating microstructure noise from volatility∗

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. We apply our methodology to a sample of S&P100 stocks and show its economic significance in an asset-allocation framework. Specifically, 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.