We propose an unbiased measure of ex-post price variation in the frequency domain. It is periodogram-based. When intraday prices are contaminated by market microstructure noise, the proposed estimator behaves like a filter: it removes the noise by filtering out high frequency periodograms. In other words, the proposed estimator converts the high frequency data into low frequency periodograms. We show, via a simulation study and an application to Microsoft transaction prices, that the proposed estimator is insensitive to the choice of sampling frequency and it is competitive with other existing noise-corrected volatility measures.