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When stock prices are observed at high frequencies, more information can beutilized in estimation of parameters of the price process. However,high-frequency data are contaminated by the market microstructure noise whichcauses significant bias in parameter estimation when not taken into account. Wepropose an estimator of the Ornstein-Uhlenbeck process based on the maximumlikelihood which is robust to the noise and utilizes irregularly spaced data.We also show that the Ornstein-Uhlenbeck process contaminated by theindependent Gaussian white noise and observed at discrete equidistant timesfollows an ARMA(1,1) process. To illustrate benefits of the proposednoise-robust approach, we introduce a novel intraday pairs trading strategybased on the mean-variance optimization. In an empirical study of 7 Big Oilcompanies, we show that the use of the proposed estimator of theOrnstein-Uhlenbeck process leads to an increase in profitability of the pairstrading strategy.

Estimation of Ornstein-Uhlenbeck Process Using Ultra-High-Frequency Data with Application to Intraday Pairs Trading Strategy