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Conditional Quantile Estimation and Inference for Arch Models

Published online by Cambridge University Press:  11 February 2009

Roger Koenker  and
Quanshui Zhao
Roger Koenker
Affiliation:
University of Illinois
Quanshui Zhao
Affiliation:
The City University of Hong Kong
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Abstract

Quantile regression methods are suggested for a class of ARCH models. Because conditional quantiles are readily interpretable in semiparametric ARCH models and are inherendy easier to estimate robustly than population moments, they offer some advantages over more familiar methods based on Gaussian likelihoods. Related inference methods, including the construction of prediction intervals, are also briefly discussed.

Information

Type
Research Article
Information
Econometric Theory , Volume 12 , Issue 5 , December 1996 , pp. 793 - 813
Copyright
Copyright © Cambridge University Press 1996

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