Asymptotics of Cholesky GARCH models and time-varying conditional betas

Serge Darolles, Christian Francq and Sébastien Laurent ()
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Serge Darolles: DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Sébastien Laurent: AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique

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Abstract: This paper proposes a new observation-driven model with time-varying slope coefficients. Ourmodel, called CHAR, is a Cholesky-GARCH model, based on the Cholesky decomposition ofthe conditional variance matrix introduced by Pourahmadi (1999) in the context of longitudinaldata. We derive stationarity and invertibility conditions and proof consistency and asymptoticnormality of the Full and equation-by-equation QML estimators of this model. We then showthat this class of models is useful to estimate conditional betas and compare it to the approachproposed by Engle (2016). Finally, we use real data in a portfolio and risk management exercise.We find that the CHAR model outperforms a model with constant betas as well as the dynamicconditional beta model of Engle (2016).

Keywords: Multivariate-GARCH; conditional betas; covariance (search for similar items in EconPapers)
Date: 2017-06
Note: View the original document on HAL open archive server: https://hal.science/hal-04590522v1
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Published in 34th International Conference of the French Finance Association (AFFI), Jun 2017, Valence, France

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04590522

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