| タイトル | Non-Gaussian quasi-likelihood estimation of locally stable SDE |
| 本文（外部サイト） | https://catalog.lib.kyushu-u.ac.jp/opac_download_md/1655025/MI2016-4.pdf |
| 参考URL | http://hdl.handle.net/2324/1655025 |
| 著者（英） | Masuda, Hiroki |
| 発行日 | 2016-05-13 |
| 発行機関など | Faculty of Mathematics, Kyushu University |
| 刊行物名 | MI Preprint Series |
| 巻 | 2016-4 |
| 刊行年月日 | 2016-04-21 |
| 言語 | eng |
| 内容記述 | We address parametric estimation of both trend and scale coefficients of a pure-jump Levy driven univariate stochastic differential equation (SDE) model based on high-frequency data over a fixed time period. The conventional Gaussian quasi-maximum likelihood estimator is known to be inconsistent. In this paper, under the assumption that the driving Levy process is locally stable, we propose a novel quasi-likelihood function based on the small-time non-Gaussian stable approximation of the unknown transition density. The resulting estimator is shown to be asymptotically mixed-normally distributed and remarkably more efficient than the Gaussian quasi-maximum likelihood estimator. We need neither ergodicity nor existence of finite moments. Compared with the existing methods for estimating SDE models, the proposed quasi-likelihood enables us to achieve better performance in a unified manner for a wide range of the driving Levy processes. |
| キーワード | Asymptotic mixed-normality; high-frequency sampling; locally stable Lévy process; stable quasi-likelihood function; stochastic differential equations |
| 資料種別 | Preprint |
| 著者版フラグ | author |
