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Conference Papers Year : 2017

Asymptotics of Cholesky GARCH models and time-varying conditional betas

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).
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Dates and versions

hal-04590522 , version 1 (28-05-2024)

Identifiers

  • HAL Id : hal-04590522 , version 1

Cite

Serge Darolles, Christian Francq, Sébastien Laurent. Asymptotics of Cholesky GARCH models and time-varying conditional betas. 34th International Conference of the French Finance Association (AFFI), Jun 2017, Valence, France. ⟨hal-04590522⟩
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