GARCH processes: structure and estimation

By István Berkes, Lajos Horv 'ath, Piotr Kokoszka. 2003

507 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dff8d564c77b6f3a1b322759d3ab77819be00abb

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Citations in the corpus

Pseudo-maximum likelihood estimation of ARCH(∞) models (2005, unknown): “By Peter M. Robinson1 and Paolo Zaffaroni”
QML INFERENCE FOR VOLATILITY MODELS WITH COVARIATES (2015, methodology): “The following lemma, which was obtained by Berkes, Horváth, and Kokoszka (2003) for standard GARCH and by Ling (2007) for ARMA-GARCH models, shows that A6 can be suppressed when (ηt ) is iid.”
Changes of structure in financial time series and the GARCH model (2004, background): “estimators; see Giraitis and Robinson [22] and Mikosch and Straumann [33] for the former case and Berkes et al. [ 5 ] for the latter case.”
ASYMPTOTIC NORMALITY OF THE QUASI MAXIMUM LIKELIHOOD ESTIMATOR FOR MULTIDIMENSIONAL CAUSAL PROCESSES (2007, background): “ntee the existence of moments of order 2 or 4 of X, respectively. Notice that for one-dimensional GARCH models these moment conditions on X can be relaxed, see Francq and Zako¨ıan [15], Berkes et al. [2]. For Markovian models, Straumann and Mikosch [26] achieved the asymptotic normality assuming mom”
ESTIMATION FOR A NONSTATIONARY SEMI-STRONG GARCH(1,1) MODEL WITH HEAVY-TAILED ERRORS (2009, methodology): “…finite fourth moment, i.e., Eε4t < ∞, the consistency and asymptotic normality of quasi-maximum likelihood estimators (QMLE) for ARCH/GARCH models have been established under different conditions (see Weiss, 1986; Lee and Hansen, 1994; Lumsdaine, 1996; Berkes, Horváth, and Kokoszka, 2003, etc.).”
High moment partial sum processes of residuals in GARCH models and their applications (2005, unknown): “AND H. YU Since ρN can be close enough to 1 if Nis large enough, the above inequality follows from the same proof of Lemma 3.7 of [2]. For completeness, we give a detailed proof here. By Lemma 3.1 of [3], there are constants C2 and 0<ρ<1 such that |ci(u)|≤C2ρi for all u∈Θ and all i. Then for a”
Quasi‐maximum likelihood estimation of periodic GARCH and periodic ARMA‐GARCH processes (2009, methodology): “This research, including contributions by authors such as Lee and Hansen (1994) , Lumsdaine (1996), Boussama (2000), Berkes et al. (2003), Ling and McAleer (2003), Berkes and Horvath (2004), Jensen and Rahbek (2004a,b), Straumann and Mikosch (2006), and Francq and Zakoı̈an (2004, 2007), is aimed at…”
QUASI-MAXIMUM LIKELIHOOD ESTIMATION OF SEMI-STRONG GARCH MODELS (2009, unknown): “Berkes, Horváth, and Kokoszka (2003) gave rigorous proofs of CAN for the global QMLE for GARCH(p,q) models.”
QUASI‐LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS (2013, methodology): “QUASI-LIKELIHOOD INFERENCE Inspired by the success of quasi-likelihood methodology on the estimation for GARCH models, see for instance Berkes et al. (2003), Francq and Zakoïan (2004), Mikosch and Straumann (2006) and more recently Bardet and Wintenberger (2010) among others, we propose to use the P”
Optimal predictions of powers of conditionally heteroscedastic processes (2013, methodology): “…case To our knowledge, the mildest assumptions for the root n consistency and asymptotic normality of the Gaussian QMLE for standard GARCH with independent and identically distributed errors admitting a finite fourth-order moment were obtained by Berkes et al. (2003) and Francq and Zakoïan (2004).”
Statistical inference for time-varying ARCH processes (2006, background): “Since L(u0, � ) is the same function as in the stationary case, this follows from Lemma 5.5 of [ 2 ].”