Realized Volatility Forcasting in the Presence of Time-varying Noise

Federico Bandi*, Jeff Russell, Chen Yang (University of Chicago)


Observed high-frequency financial prices can be considered as having two components, a true price and a market microstructure noise perturbation. It is an empirical fact that the second moment of market microstructure noise is time-varying. We study the optimal design of nonparametric variance estimators in linear forecasting models with time-varying market microstructure noise. Specifically, we discuss optimal frequency selection in the case of the classical realized variance estimator and optimal bandwidth selection in the case of kernel-type integrated variance estimators. In this setting, we show that the sampling frequencies are generally considerably lower (the bandwidths are generally considerably larger) than those that would be optimally chosen in linear forecasting models when time-variation in the second moment of the noise is unaccounted for. Conditional and unconditional frequency/bandwidth choices are discussed.