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