Recent Advances in ARCH Modelling

By Liudas Giraitis, Remigijus Leipus, Donatas Surgailis. 2007

90 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/db0ade7f85fbf87a34cecb1e42d0de727c2d0bb3

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Citations in the corpus

Probability and moment inequalities for sums of weakly dependent random variables, with applications (2007, background): “where βi ≥ 0, γ => 0 and γ j ≥ 0 ( j ≥ 1), and the variables εt are centered at expectation; see Giraitis, Leipus and Surgailis [16].”
GARCH Models (2010, unknown): “See, among others, Bollerslev, Chou and Kroner (1992), Bollerslev, Engle and Nelson (1994), Pagan (1996), Palm (1996), Shephard (1996), Kim, Shephard, and Chib (1998), Engle (2001, 2002b, 2004), Engle and Patton (2001), Diebold (2004), Bauwens, Laurent and Rombouts (2006) and Giraitis et al. (2006).”
Detection of multiple change-points in multivariate time series (2006, unknown): “[14] for a survey on volatility models.”
Stationarity, Mixing, Distributional Properties and Moments of GARCH(p, q)-Processes (2009, methodology): “This was shown by Giraitis et al. (2000) for m = 2 and observed to extend to hold for general m ≥ 1 by Giraitis et al. (2006).”
Inequality Constraints in the Fractionally Integrated GARCH Model (2006, background): “For an up-to-date overview of theoretical findings on long-memory GARCH processes, see Giraitis, Leipus, and Surgailis (2005).”
Mixing properties of ARCH and time-varying ARCH processes (2011, methodology): “The ARCH family of processes is widely used in finance to model the evolution of returns on financial instruments; we refer the reader to the review article of [18] for a comprehensive overview of mathematical properties of ARCH processes and a list of further references.”
Normalized least-squares estimation in time-varying ARCH models (2008, methodology): “In the stationary ARCH framework, Giraitis and Robinson (2001), Bose and Mukherjee (2003), Horváth and Liese (2004) and Ling (2007) have considered the weighted least-squares estimator for different weight functions.”
ASYMPTOTIC NORMALITY OF THE QUASI MAXIMUM LIKELIHOOD ESTIMATOR FOR MULTIDIMENSIONAL CAUSAL PROCESSES (2007, methodology): “tationary solution and of its r-th order moments is also settled in Giraitis et al. [16]. For an excellent survey about results and applications of ARCH models, we refer the reader to Giraitis et al. [18]. Here we formulate a version of Theorems 1 and 2 adapted to the context. ASYMPTOTIC NORMALITY O”
Quasi-maximum likelihood estimation in GARCH processes when some coefficients are equal to zero (2007, background): “See Ling and McAleer [19], Giraitis, Leipus and Surgailis [13] for recent surveys on theoretical results for GARCH models.”
An Introduction to Univariate GARCH Models (2006, background): “Granger and Ding (1995) listed a few more such features. Among them is the empirical observation that in a remarkable number of nancial series, the autocorrelations of the powers of observations, j"tj, peak around k = 1:Granger and Ding called this stylized fact the Taylor e¤ect as Taylor (1986) wa”