Inference in non stationary asymmetric garch models
AbstractThis paper considers the statistical inference of the class of asymmetric power-transformed GARCH(1,1) models in presence of possible explosiveness. We study the explosive behavior of volatility when the strict stationarity condition is not met. This allows us to establish the asymptotic normality of the quasi-maximum likelihood estimator (QMLE) of the parameter, including the power but without the intercept, when strict stationarity does not hold. Two important issues can be tested in this framework: asymmetry and stationarity. The tests exploit the existence of a universal estimator of the asymptotic covariance matrix of the QMLE. By establishing the local asymptotic normality (LAN) property in this nonstationary framework, we can also study optimality issues.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 44901.
Date of creation: 01 Mar 2013
Date of revision:
GARCH models; Inconsistency of estimators; Local power of tests; Nonstationarity; Quasi Maximum Likelihood Estimation;
Other versions of this item:
- Christian Francq & Jean-Michel Zakoian, 2013. "Inference in Non Stationary Asymmetric Garch Models," Working Papers 2013-11, Centre de Recherche en Economie et Statistique.
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-03-16 (All new papers)
- NEP-ECM-2013-03-16 (Econometrics)
- NEP-ETS-2013-03-16 (Econometric Time Series)
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