Finite-Sample Properties of the Maximum Likelihood Estimator in GARCH(1,1) and IGARCH(1,1) Models
A Monte Carlo Investigation
Bibliographic Data
| ID | 19417682 |
|---|---|
| Authors | Robin L Lumsdaine (Princeton University, corresponding author) |
| Year | 1995 |
| Volume | 13 |
| Issue | 1 |
| Pages | 1-10 |
| Publication date | 1995-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.1995.10524574 |
| OpenAlex | W2011343908 |
| Language | EN |
| Citations received | 7 |
| References cited | 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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| Unique citing works | 7 |
|---|---|
| Citations per year | 0,23 |
| Citation span | 1996 - 2003 (8) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 6 |