Statistical inference for nonparametric GARCH models
| Autor: | Jens-Peter Kreiß, Alexander Meister |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Statistics::Theory Applied Mathematics 05 social sciences Nonparametric statistics Bivariate analysis Minimax 01 natural sciences Nonparametric regression 010104 statistics & probability Autoregressive model Modeling and Simulation 0502 economics and business Statistical inference Econometrics Statistics::Methodology Semiparametric regression 0101 mathematics 050205 econometrics Parametric statistics Mathematics |
| Zdroj: | Stochastic Processes and their Applications. 126:3009-3040 |
| ISSN: | 0304-4149 |
| Popis: | We consider extensions of the famous GARCH ( 1 , 1 ) model where the recursive equation for the volatilities is not specified by a parametric link but by a smooth autoregression function. Our goal is to estimate this function under nonparametric constraints when the volatilities are observed with multiplicative innovation errors. We construct an estimation procedure whose risk attains nearly the usual convergence rates for bivariate nonparametric regression estimation. Furthermore, those rates are shown to be nearly optimal in the minimax sense. Numerical simulations are provided for a parametric submodel. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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