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Adaptive Testing in ARCH Models Author info | Abstract | Publisher info | Download info | Related research | Statistics Oliver Linton (Cowles Foundation, Yale University )
Douglas G. Steigerwald (UCLA, Santa Barbara)
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Existing specification tests for conditional heteroskedasticity are derived under the assumption that the density of the innovation, or standardized error, is Gaussian, despite the fact that many recent empirical studies provide evidence that this density is not Gaussian. We obtain specification tests for conditional heteroskedasticity under the assumption that the innovation density is a member of a general family of densities. Our test statistics maximize asymptotic local power and weighted average power criteria for the general family of densities. We establish both first order and second order theory for our procedures. Monte Carlo simulations indicate that asymptotic power gains are achievable in finite samples. We apply the tests to shock futures data sampled at high frequency and find evidence of conditional heteroskedasticity in the residuals from a GARCH(1,1) model, indicating that the standard (1,1) specification is not adequate.
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Paper provided by Cowles Foundation, Yale University in its series Cowles Foundation Discussion Papers with number
1105.
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Length: 43 pages
Date of creation: Jun 1995Date of revision:
Handle: RePEc:cwl:cwldpp:1105Contact details of provider: Postal: Yale University, Box 208281, New Haven, CT 06520-8281 USA Phone: (203) 432-3702 Fax: (203) 432-6167 Web page: http://cowles.econ.yale.edu/ More information through EDIRC
Order Information: Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA
For technical questions regarding this item, or to correct its listing, contact: (Glena Ames).
Keywords: Adaptive estimation ; ARCH ; efficient ; semiparametric model ; testing ; Other versions of this item:
Find related papers by JEL classification: C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
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