 Robust Modelling of ARCH ModelsJiancheng Jiang, Quanshui Zhao and Yer Van Hui Journal of Forecasting, 2001, vol. 20, issue 2, 111-33 Abstract: The autoregressive conditional heteroscedastic (ARCH) model and its extensions have been widely used in modelling changing variances in financial time series. Since the asset return distributions frequently display tails heavier than normal distributions, it is worth while studying robust ARCH modelling without a specific distribution assumption. In this paper, rather than modelling the conditional variance, we study ARCH modelling for the conditional scale. We examine the L[subscript 1]-estimation of ARCH models and derive the limiting distributions of the estimators. A robust standardized absolute residual autocorrelation based on least absolute deviation estimation is proposed. Then a robust portmanteau statistic is constructed to test the adequacy of the model, especially the specification of the conditional scale. We obtain their asymptotic distributions under mild conditions. Examples show that the suggested L[subscript 1]-norm estimators and the goodness-of-fit test are robust against error distributions and are accurate for moderate sample sizes. This paper provides a useful tool in modelling conditional heteroscedastic time series data. Copyright © 2001 by John Wiley & Sons, Ltd. Date: 2001 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:20:y:2001:i:2:p:111-33 Access Statistics for this article Journal of Forecasting is currently edited by Derek W. Bunn More articles in Journal of Forecasting from John Wiley & Sons, Ltd. |
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