Statistical inference for Ornstein–Uhlenbeck processes based on low-frequency observations

Abstract

Low-frequency observations are a common occurrence in real-world applications, making statistical inference for stochastic processes driven by stochastic differential equations (SDEs) based on such observations an important issue. In this paper, we investigate the statistical inference for the Ornstein–Uhlenbeck (OU) process using low-frequency observations. We propose modified least squares estimators (MLSEs) for the drift parameters and a modified quadratic variation estimator for the diffusion parameter based on the solution of the OU process. The MLSEs are derived heuristically using the nonlinear least squares method, despite the OU process satisfying a linear SDE. Unlike previous approaches, these modified estimators are asymptotically unbiased. Leveraging the ergodic properties of the OU process, we also propose ergodic estimators for the three parameters. The asymptotic behavior of these estimators is established using the ergodic properties and central limit theorem for the OU process, achieved through linear model techniques and multivariate Markov chain central limit theorem. Monte Carlo simulation results are presented to illustrate and support our theoretical findings.