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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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    File URL: http://hdl.handle.net/10.1111/jtsa.12089
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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.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Xiao, Zhijie & Koenker, Roger, 2009. "Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1696-1712.
    4. Koenker, Roger, 1984. "A note on L-estimates for linear models," Statistics & Probability Letters, Elsevier, vol. 2(6), pages 323-325, December.
    5. Hong, Yongmiao & Shehadeh, Ramsey D, 1999. "A New Test for ARCH Effects and Its Finite-Sample Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 91-108, January.
    6. Bo Kai & Runze Li & Hui Zou, 2010. "Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 49-69, January.
    7. Koenker, Roger & Zhao, Quanshui, 1996. "Conditional Quantile Estimation and Inference for Arch Models," Econometric Theory, Cambridge University Press, vol. 12(5), pages 793-813, December.
    8. Zhao, Zhibiao & Xiao, Zhijie, 2014. "Efficient Regressions Via Optimally Combining Quantile Information," Econometric Theory, Cambridge University Press, vol. 30(6), pages 1272-1314, December.
    9. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(2), pages 186-199, June.
    10. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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