Estimation in Conditionally Heteroscedastic Time Series Models

By R. Lund. 2006

154 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7706eecc95eece96b9eecc9b9da97ae7d2a0486d

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): “First approaches can be found in Davis and Mikosch (2008), Huser and Davison (2013), Kabluchkko (2009) and Davis et al. (2011).”
QUASI-MAXIMUM LIKELIHOOD ESTIMATION OF SEMI-STRONG GARCH MODELS (2009, unknown): “See Straumann (2005) for a recent reference on GARCH and other volatility models.”
Merits and Drawbacks of Variance Targeting in GARCH Models (2011, background): “In recent years, special attention has been given to the asymptotic properties of the Gaussian quasi-maximum likelihood estimation (QMLE) (see Berkes, Horváth, and Kokoszka (2003), Francq and Zakoïan (2004), and the recent monograph by Straumann (2005), among others).”
An Introduction to Univariate GARCH Models (2006, background): “The focus in Straumann (2004) is on estimation in models of conditional heteroskedasticity.”
Nonstationarities in Stock Returns (2005, background): “See Straumann (2005).”
Normalized least-squares estimation in time-varying ARCH models (2008, background): “In contrast, Straumann (2005) has shown that under misspecification of the innovation distribution, the resulting non-Gaussian maximum likelihood estimator is inconsistent.”
GARCH Models (2010, unknown): “We refer the reader to Straumann (2005) for a detailed monograph on the estimation of GARCH models, to Francq and Zakoı̈an (2009a) for a recent review of the literature, and to Straumann and Mikosch (2006) and Bardet and Wintenberger (2009) for extensions to other conditionally heteroscedastic…”
Stable limits for sums of dependent infinite variance random variables (2009, background): “Various other financial time series models fit into the framework of stochastic recurrence equations, such as the AGARCH and EGARCH models; see e.g. the treatment in Straumann and Mikosch (53) and the lecture notes by Straumann (52).”
Feasible invertibility conditions and maximum likelihood estimation for observation-driven models (2018, methodology): “As in [42], [43] and [47], the conditions we consider are based on Theorem 3.”
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.”