A Note on Adaptation in Garch Models
AbstractIn 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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Bibliographic InfoPaper provided by The A. Gary Anderson Graduate School of Management. University of California Riverside in its series The A. Gary Anderson Graduate School of Management with number 95-1.
Length: 15 pages
Date of creation: 1995
Date of revision:
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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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REGRESSION ANALYSIS; MAXIMUM LIKELIHOOD;
Other versions of this item:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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- HAFNER, Christian & ROMBOUTS, Jeroen, 2003. "Semiparametric multivariate GARCH models," CORE Discussion Papers 2003003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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