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Seminar on Probability and Statistics
Seminar information archive ～05/02｜Next seminar｜Future seminars 05/03～
| Organizer(s) | Nakahiro Yoshida, Hiroki Masuda, Teppei Ogihara, Yuta Koike |
|---|
2009/05/12
16:20-17:30 Room #126 (Graduate School of Math. Sci. Bldg.)
塩濱 敬之 (東京理科大学, 工学部)
Asymptitically efficient estimation of multiple change points in GARCH types models
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/03.html
塩濱 敬之 (東京理科大学, 工学部)
Asymptitically efficient estimation of multiple change points in GARCH types models
[ Abstract ]
Instability of volatility parameters in GARCH models in an important issue for analyzing financial time series. In this paper we investigate the asymptotic theory for multiple change point estimators of GARCH$(p,q)$ models. When the parameters of a GARCH models have changed within an observed realization, two types estimators, Maximum likelihood estimator (MLE) and Bayesian estimator (BE), are proposed. Then we derive the asymptotic distributions for these estimators. The MLE and BE have different limit laws, and the BE is asymptotically efficient. Monte Carlo studies on the finite sample behaviors are conducted. Applications to Nikkei 225 index are discussed.
[ Reference URL ]Instability of volatility parameters in GARCH models in an important issue for analyzing financial time series. In this paper we investigate the asymptotic theory for multiple change point estimators of GARCH$(p,q)$ models. When the parameters of a GARCH models have changed within an observed realization, two types estimators, Maximum likelihood estimator (MLE) and Bayesian estimator (BE), are proposed. Then we derive the asymptotic distributions for these estimators. The MLE and BE have different limit laws, and the BE is asymptotically efficient. Monte Carlo studies on the finite sample behaviors are conducted. Applications to Nikkei 225 index are discussed.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/03.html
