Qml inference for volatility models with covariates

Christian Francq and Le Quyen Thieu

MPRA Paper from University Library of Munich, Germany

Abstract: The asymptotic distribution of the Gaussian quasi-maximum likelihood estimator (QMLE) is obtained for a wide class of asymmetric GARCH models with exogenous covariates. The true value of the parameter is not restricted to belong to the interior of the parameter space, which allows us to derive tests for the significance of the parameters. In particular, the relevance of the exogenous variables can be assessed. The results are obtained without assuming that the innovations are independent, which allows conditioning on different information sets. Monte Carlo experiments and applications to financial series illustrate the asymptotic results. In particular, an empirical study demonstrates that the realized volatility is an helpful covariate for predicting squared returns, but does not constitute an ideal proxy of the volatility.

Keywords: APARCH model augmented with explanatory variables; Boundary of the parameter space; Consistency and asymptotic distribution of the Gaussian quasi-maximum likelihood estimator; GARCH-X models; Power-transformed and Threshold GARCH with exogenous covariates (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
Date: 2015-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Related works:
Journal Article: QML INFERENCE FOR VOLATILITY MODELS WITH COVARIATES (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:63198

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