### Abstract

Original language | English |
---|---|

Place of Publication | Tilburg |

Publisher | Econometrics |

Number of pages | 29 |

Volume | 1996-38 |

Publication status | Published - 1996 |

### Publication series

Name | CentER Discussion Paper |
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Volume | 1996-38 |

### Fingerprint

### Keywords

- garch models
- estimation

### Cite this

*Efficient Estimation in Semiparametric GARCH Models*. (CentER Discussion Paper; Vol. 1996-38). Tilburg: Econometrics.

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**Efficient Estimation in Semiparametric GARCH Models.** / Drost, F.C.; Klaassen, C.A.J.

Research output: Working paper › Discussion paper › Other research output

TY - UNPB

T1 - Efficient Estimation in Semiparametric GARCH Models

AU - Drost, F.C.

AU - Klaassen, C.A.J.

N1 - Pagination: 29

PY - 1996

Y1 - 1996

N2 - It is well-known that financial data sets exhibit conditional heteroskedasticity.GARCH type models are often used to model this phenomenon. Since the distribution of the rescaled innovations is generally far from a normal distribution, a semiparametric approach is advisable.Several publications observed that adaptive estimation of the Euclidean parameters is not possible in the usual parametrization when the distribution of the rescaled innovations is the unknown nuisance parameter.However, there exists a reparametrization such that the efficient score functions in the parametric model of the autoregression parameters are orthogonal to the tangent space generated by the nuisance parameter, thus suggesting that adaptive estimation of the autoregression parameters is possible.Indeed, we construct adaptive and hence efficient estimators in a general GARCH in mean type context including integrated GARCH models.Our analysis is based on a general LAN Theorem for time-series models, published elsewhere.In contrast to recent literature about ARCH models we do not need any moment condition.

AB - It is well-known that financial data sets exhibit conditional heteroskedasticity.GARCH type models are often used to model this phenomenon. Since the distribution of the rescaled innovations is generally far from a normal distribution, a semiparametric approach is advisable.Several publications observed that adaptive estimation of the Euclidean parameters is not possible in the usual parametrization when the distribution of the rescaled innovations is the unknown nuisance parameter.However, there exists a reparametrization such that the efficient score functions in the parametric model of the autoregression parameters are orthogonal to the tangent space generated by the nuisance parameter, thus suggesting that adaptive estimation of the autoregression parameters is possible.Indeed, we construct adaptive and hence efficient estimators in a general GARCH in mean type context including integrated GARCH models.Our analysis is based on a general LAN Theorem for time-series models, published elsewhere.In contrast to recent literature about ARCH models we do not need any moment condition.

KW - garch models

KW - estimation

M3 - Discussion paper

VL - 1996-38

T3 - CentER Discussion Paper

BT - Efficient Estimation in Semiparametric GARCH Models

PB - Econometrics

CY - Tilburg

ER -