A component GARCH model with time varying weights
Luc Bauwens () and
Giuseppe Storti ()
No 2007019, CORE Discussion Papers from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)
We present a novel GARCH model that accounts for time varying, state dependent, persistence in the volatility dynamics. The proposed model generalizes the component GARCH model of Ding and Granger (1996). The volatility is modelled as a convex combination of unobserved GARCH components where the combination weights are time varying as a function of appropriately chosen state variables. In order to make inference on the model parameters, we develop a Gibbs sampling algorithm. Adopting a fully Bayesian approach allows to easily obtain medium and long term predictions of relevant risk measures such as value at risk and expected shortfall. Finally we discuss the results of an application to a series of daily returns on the S&P500.
Keywords: GARCH; persistence; volatility components; value-at-risk; expected shortfall (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 (search for similar items in EconPapers)
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Journal Article: A Component GARCH Model with Time Varying Weights (2009)
Working Paper: A component GARCH model with time varying weights (2009)
Working Paper: A Component GARCH Model with Time Varying Weights (2007)
Working Paper: A component GARCH model with time varying weights (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2007019
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