Mixed Normal Conditional Heteroskedasticity

By Markus Haas, Stefan Mittnik, Marc S. Paolella. 2004

214 citations recorded by the source corpus.

Subfields: Demography, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f6a538d7dfc21c67b3f4bef0a20a98af11987a7a

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Citations in the corpus

A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models (2005, background): “Among these, we mention briefly the density of Jones (2001), where the covariances are necessarily positive; Jones (2002); and the heavily parameterized Edgeworth–Sargan density used by Mauleón and Perote (1999) for a bivariate GARCH model.”
A comprehensive review of Value at Risk methodologies (2014, background): “…in the following studies: Abad and Benito (2013), Sener et al. (2012), Chen et al. (2009, 2011), Sajjad et al. (2008), Bali and Theodossiou (2007), Angelidis et al. (2007), Haas et al. (2004), Li and Lin (2004), Carvalho et al. (2006), González-Rivera et al. (2004), Giot and Laurent (2004),…”
Skewness in Stock Returns: Reconciling the Evidence on Firm Versus Aggregate Returns (2012, unknown): “Finally, there is evidence that firm-level stock returns are well described by a mixture of normals distribution (see Kon, 1984, Zangari, 1996, and Haas et al., 2004).”
Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach (2012, methodology): “[26] or Lee [27] among others, modify Gray’s approach offering more parsimonious specifications of multivariate MS-GARCH models free from the problem of path”
Value-at-Risk Prediction: A Comparison of Alternative Strategies (2005, methodology): “We restrict Yj to be diagonal, which, as discussed in Haas, Mittnik, and Paolella (2004a), yields a much more parsimonious model with little loss in the quality of in-sample fit and forecasting ability.5 It is plausible that the component of the mixture assigned to the most volatile observations…”
Understanding volatility dynamics in the EU-ETS market (2009, background): “For an extensive overview of the properties of the mixture of distributions see Alexander and Lazar (2006) and Haas et al. (2004), among others.”
Time-dependent series variance learning with recurrent mixture density networks (2013, result): “…results show that, when compared with a traditional linear heteroskedastic model, as well as with the nonlinear mixture density network trained with static derivatives, our dynamic recurrent network converges to more accurate results with better statistical characteristics and economic performance.”