Finite-Sample Properties of the Maximum Likelihood Estimator in GARCH(1,1) and IGARCH(1,1) Models
A Monte Carlo Investigation
Datos Bibliográficos
| ID | 19417682 |
|---|---|
| Autores | Robin L Lumsdaine (Princeton University, autor de correspondencia) |
| Año | 1995 |
| Volumen | 13 |
| Número | 1 |
| Páginas | 1-10 |
| Fecha de publicación | 1995-01-01 |
| Peer Reviewed | Sí |
| Open Access | No |
| Tipo | ARTICLE |
| Revista | Journal of Business and Economic Statistics (JOURNAL) |
| Identificadores de la revista | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Editorial | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.1995.10524574 |
| OpenAlex | W2011343908 |
| Idioma | EN |
| Citas recibidas | 7 |
| Referencias citadas | 27 |
This article compares GARCH(1,1) and IGARCH(1,1) models via a Monte Carlo study of the finite-sample properties of the maximum likelihood estimator and related test statistics. Although the asymptotic distribution is well approximated by the estimated t statistics, other commonly used statistics do not behave as well. In addition, the estimators themselves are skewed in small samples. For the null hypothesis of IGARCH(1,1), Wald tests typically have the best size, but the standard Lagrange multiplier statistic is badly oversized; versions that are robust to possible nonnormality of the data perform marginally better. An empirical example demonstrates these results
Estimator · Lagrange multiplier · M-estimator · Mathematical optimization · Monte Carlo method · Sample size determination · Score test · Statistic · Statistical hypothesis testing · Statistics · Test statistic · Wald test · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact · Applied Mathematics
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| Obras citantes distintas | 7 |
|---|---|
| Citas por año | 0,23 |
| Intervalo de citas | 1996 - 2003 (8) |
| Velocidad de citación | historical |
| Altamente citado | No |
| Tipos de cita | Neutras: 6 |