Munich Personal RePEc Archive

Inference in non stationary asymmetric garch models

Francq, Christian and Zakoian, Jean-Michel (2013): Inference in non stationary asymmetric garch models.

[img]
Preview
PDF
MPRA_paper_44901.pdf

Download (467kB) | Preview

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.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.