The Distribution of the Mean Reversion Estimator in the Ornstein-Uhlenbeck Process

We derive the exact distribution of the maximum likelihood estimator of the mean reversion parameter (k) in the Ornstein-Uhlenbeck process by employing numerical integration via analytical evaluation of a joint characteristic function. Different scenarios are considered: known or unknown drift term, fixed or random start-up value, and zero or positive k. Monte Carlo results demonstrate the remarkably reliable performance of our exact approach across all the scenarios. In comparison, misleading results may arise under the asymptotic distributions, including the advocated infill asymptotic distribution, which performs poorly in the tails when there is no intercept in the regression and the starting value of the process is non-zero