Inference in Non Stationary Asymmetric Garch Models

Christian Francq and Jean-Michel Zakoian

No 2013-11, Working Papers from Center for Research in Economics and Statistics

Abstract: This 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

Keywords: GARCH models; Inconsistency of estimators; Local power of tests; Non stationarity; Quasi Maximum Likelihood estimation (search for similar items in EconPapers)
Pages: 45
Date: 2013-08
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (21)

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Working Paper: Inference in non stationary asymmetric garch models (2013)
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