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Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean

  • Andrew P. Blake

    (Bank of England)

  • George Kapetanios

    ()

    (Queen Mary, University of London)

Tests of ARCH are a routine diagnostic in empirical econometric and financial analysis. However, it is well known that misspecification of the conditional mean may lead to spurious rejections of the null hypothesis of no ARCH. Nonlinearity is a prime example of this phenomenon. There is little work on the extent of the effect of neglected nonlinearity on the properties of ARCH tests. This paper provides some such evidence and also new ARCH testing procedures that are robust to the presence of neglected nonlinearity. Monte Carlo evidence shows that the problem is serious and that the new methods alleviate this problem to a very large extent.

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File URL: http://www.econ.qmul.ac.uk/papers/doc/wp496.pdf
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Paper provided by Queen Mary University of London, School of Economics and Finance in its series Working Papers with number 496.

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Date of creation: Jul 2003
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Handle: RePEc:qmw:qmwecw:wp496
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  1. Blake, Andrew P., 2002. "A 'Timeless Perspective' on Optimality in Forward-Looking Rational Expectations Models," Royal Economic Society Annual Conference 2002 30, Royal Economic Society.
  2. Peguin-Feissolle, Anne, 1999. "A comparison of the power of some tests for conditional heteroscedasticity," Economics Letters, Elsevier, vol. 63(1), pages 5-17, April.
  3. Bera, Anil K & Higgins, Matthew L & Lee, Sangkyu, 1992. "Interaction between Autocorrelation and Conditional Heteroscedasticity: A Random-Coefficient Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 133-42, April.
  4. Bera, Anil K & Higgins, Matthew L, 1997. "ARCH and Bilinearity as Competing Models for Nonlinear Dependence," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 43-50, January.
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