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Hypothesis Testing For Arch Models: A Multiple Quantile Regressions Approach

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  • Seonjin Kim

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.

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  • Seonjin Kim, 2015. "Hypothesis Testing For Arch Models: A Multiple Quantile Regressions Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 26-38, January.
    • Handle: RePEc:bla:jtsera:v:36:y:2015:i:1:p:26-38
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    References listed on IDEAS

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    1. Dufour, Jean-Marie & Khalaf, Lynda & Bernard, Jean-Thomas & Genest, Ian, 2004. "Simulation-based finite-sample tests for heteroskedasticity and ARCH effects," Journal of Econometrics, Elsevier, vol. 122(2), pages 317-347, October.
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    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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