• Media type: Text; E-Article
  • Title: Drift estimation for a Lévy-driven Ornstein–Uhlenbeck process with heavy tails
  • Contributor: Gushchin, Alexander [Author]; Pavlyukevich, Ilya [Author]; Ritsch, Marian [Author]
  • Published: Digital Library Thüringen, 2020-10
  • Language: English
  • DOI: https://doi.org/10.1007/s11203-020-09210-8
  • Keywords: Local asymptotic mixed normality ; Asymptotic observed information ; 60J75 ; Maximum likelihood estimator ; 60F05 ; Lévy process ; ScholarlyArticle ; Ornstein–Uhlenbeck type process ; Heavy tails ; Regular variation ; 62M05 ; article
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  • Description: We consider the problem of estimation of the drift parameter of an ergodic Ornstein–Uhlenbeck type process driven by a Lévy process with heavy tails. The process is observed continuously on a long time interval [0, T ], T → ∞ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T\rightarrow \infty $$\end{document} . We prove that the statistical model is locally asymptotic mixed normal and the maximum likelihood estimator is asymptotically efficient.
  • Access State: Open Access
  • Rights information: Attribution (CC BY)