A Note on Adaptation in Garch Models
In the framework of the Engle-type (G)ARCH models, I demonstrate that there is a family of symmetric and asymmetric density functions for which the asymptotic efficiency of the semiparametric estimator is equal to the asymptotic efficiency of the maximum likelihood estimator. This family of densities is bimodal (except for the normal). I also chracterize the solution to the problem of minimizing the mean squared distance between the parametric score and the semiparametric score in order to search for unimodal densities for which the semiparametric estimator is likely to perform well. The LaPlace density function emerges as one of these cases.
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|Date of creation:||1995|
|Contact details of provider:|| Postal: The A. Gary Anderson Graduate School of Management. University of California, Riverside. Riverside CA 92521|
Web page: http://www.agsm.ucr.edu/
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