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.
Semi-parametric estimation of the autoregressive parameter in non-Gaussian Ornstein-Uhlenbeck processes / Rao Jammalamadaka, Sreenivas; Taufer, Emanuele. - In: COMMUNICATIONS IN STATISTICS. SIMULATION AND COMPUTATION. - ISSN 0361-0918. - STAMPA. - 2019, 48:9(2019), pp. 2791-2811. [10.1080/03610918.2018.1468456]
Semi-parametric estimation of the autoregressive parameter in non-Gaussian Ornstein-Uhlenbeck processes
Emanuele Taufer
2019-01-01
Abstract
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.File | Dimensione | Formato | |
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2019 - CSSC - Semi parametric estimation of the autoregressive parameter in non Gaussian Ornstein Uhlenbeck processes.pdf
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