On a Mixture Autoregressive Conditional Heteroscedastic Model

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Mixed Normal Conditional Heteroskedasticity (2004, methodology): “An approach similar to ours has recently been explored by Wong and Li (2001), who also argue against use of a latent Markov structure; see Section 5 below for some discussion of their work.”
Glossary to ARCH (GARCH) (2008, unknown): “MGARCH 3 (Mixture GARCH) The MAR-ARCH model of Wong and Li (2001) and the MGARCH model Zhang, Li and Yuen (2006) postulates that the time t conditional variance is given by a time-invariant mixture of diﬀerent GARCH models (see also GRS-GARCH, NM-GARCH and SWARCH).”
The ACR Model: A Multivariate Dynamic Mixture Autoregression (2008, unknown): “Note in this respect that the model in Wong and Li (2001a) and our ACR model are extensions of the mentioned static MAR in Wong and Li (2000) where (xt−1)=a, that is, with constant switching probability.”
On a mixture vector autoregressive model (2007, unknown): “Wong & Li (2001a), Berchtold (2003) and Lanne & Saikkonen (2003) further extended the mixture model to capture heteroscedasticity in time series.”
A comprehensive review of Value at Risk methodologies (2014, methodology): “(2010) extend the AViaR model to include the Threshold GARCH (TGARCH) model an extension of the double threshold ARCH (DTARCH) of Li and i (1996)) and a mixture (an extension of Wong and Li (2001)’s”