期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2007
卷号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：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 modeled 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.
