HYPOTHESIS TESTING FOR ARCH MODELS: A MULTIPLE QUANTILE REGRESSIONS APPROACH

Seonjin Kim

Journal of Time Series Analysis, 2015, vol. 36, issue 1, 26-38

Abstract: type="main" xml:id="jtsa12089-abs-0001"> We propose a quantile regression-based test to detect the presence of autoregressive conditional heteroscedasticity by combining distributional information across multiple quantiles. A chi-square-type test statistic based on the weighted average of distinct regression quantile estimators is formed. Unlike the widely used likelihood-based tests, the proposed test does not make any distributional assumptions on the underlying errors. Monte Carlo simulation studies show that the proposed test outperforms the likelihood-based tests in several aspects.

Date: 2015
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