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.

We study the finite sample properties of the Fourier estimator of integrated volatility under market microstructure noise. We derive an analytic expression for the bias and the mean squared error of the contaminated estimator. These estimates can be practically used to design optimal MSE-based estimators, which are very robust and efficient in the presence of noise. Moreover an empirical analysis based on a simulation study and on high-frequency logarithmic prices of the Italian stock index futures (FIB30) validates the theoretical results. JEL: C10,C13,C14,C15,C22

We assess the performance of various methodological frameworks in forecasting the volatility of the euro’s bilateral exchange rates at high frequencies. We construct four 15-minute series, including euro/CHF, euro/GBP, euro/JPY and euro/USD. We then compare the forecast performance of six models, including both the realized volatility model and traditional time series volatility models. In assessing out-of-sample forecasting performance of these models we use the regression and accuracy tests, an equal accuracy test, the HLN-DM test, and a superior predictive ability test. We find that the FIGARCH model emerges superior in almost all exchange rate series. The widely preferred ARFIMA model displays better performance than the traditional daily volatility models, but it cannot surpass the FIGARCH model and the intraday GARCH model. Furthermore, the SVX model cannot significantly outperform the SV model in the accuracy test, in contrast to what earlier research has found. Using high frequency data and modelling the long memory dimension of the data significantly enhances the volatility forecasting performance. Moreover, we find that the euro exchange rates display a number of features that distinguish them from other exchange rates.