Diagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approach

• 成果类型: Article
• 署名作者: Li, GD; Li, WK
• 署名单位: University of Hong Kong
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/92.3.691
• 发表日期: 2005
• 页码: 691701
• 关键词: garch models; errors; ARCH

摘要

The recent paper by Peng & Yao (2003) gave an interesting extension of least absolute deviation estimation to generalised autoregressive conditional heteroscedasticity, GARCH, time series models. The asymptotic distributions of absolute residual autocorrelations and squared residual autocorrelations from the GARCH model estimated by the least absolute deviation method are derived in this paper. These results lead to two useful diagnostic tools which can be used to check whether or not a GARCH model fitted by using the least absolute deviation method is adequate. Some simulation experiments give further support to the asymptotic theory and a real data example is also reported.