Estimation in Conditionally Herteroscedastic Time Series Models

By D. Straumann. 2004

120 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/db2a339596c46d347dc400463b4e259e5148f2e4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Statistical inference for max‐stable processes in space and time (2012, methodology): “∞: By theorem 2.7 in Straumann (2004) uniform convergence in step 1 follows.”
Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model (2012, methodology): “In accordance with the notions of invertibility given in [18, 36, 33, 34], we will say that the model is invertible if the SRE (4) is stable.”
Quasi-maximum likelihood estimation in GARCH processes when some coefficients are equal to zero (2007, methodology): “Recent references dealing with the QML estimation of general GARCH(p, q) are the dissertation by Boussama [9], the monograph by Straumann [25] and the papers by Berkes and Horváth [4,3], Berkes, Horváth and Kokoszka [5], Hall and Yao [14] for GARCH models with heavy tailed errors, and Francq and Zak”
Stable limits for sums of dependent infinite variance random variables (2009, background): “the treatment in Straumann and Mikosch [53] and the lecture notes by Straumann [52].”
An Introduction to Univariate GARCH Models (2006, background): “The focus in Straumann (2004) is on estimation in models of conditional heteroskedasticity.”
Normalized least-squares estimation in time-varying ARCH models (2008, background): “d as if the innovations were Gaussian. Themain advantage of the QML estimator is that, even in the case that the innovations are non-Gaussian, it is consistent and asymptotically normal. In contrast, Straumann (2005) has shown that under misspeciﬁcation of the innovation distribution, the resulting ”