Semi-parametric estimation of the autoregressive parameter in non-Gaussian Ornstein-Uhlenbeck processes

This paper considers the problem of estimating the autoregressive parameter in discretely observed Ornstein-Uhlenbeck processes. Two consistent estimators are proposed: one obtained by maximizing a kernel-based likelihood function, and another by minimizing a Kolmogorov-type distance from independence. After establishing the consistency of these estimators, their finite-sample performance and possible normality in large samples, is investigated by means of extensive simulations. An illustrative example to credit rating is discussed.