Change-point estimation in ARCH models

By Piotr Kokoszka, Remigijus Leipus. 2000

285 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b5625abb7e8ac80bf5f266ced6f80a03a7ab7ed1

How researchers see this paper

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

In Finance

In Statistics and Probability

Citations in the corpus

Break detection in the covariance structure of multivariate time series models (2009, unknown): “More recently, [37] have introduced tests for a single change in the volatility of an ARCH(∞) series, while [20] analyze parametric nonlinear time series models by means of minimum description length procedures.”
Detection of multiple change-points in multivariate time series (2006, unknown): “The journal articles by Giraitis and Leipus [16] [15], Hawkins [19] [20], Chen and Gupta [9], Mia and Zhao [32], Sen and Srivastava [35] among others, are also of interest.”
STATIONARY ARCH MODELS: DEPENDENCE STRUCTURE AND CENTRAL LIMIT THEOREM (2000, background): “~see Kokoszka and Leipus, 2000!+ We show, in particular, that if bj ; Cj2g for someg .”
Detecting multiple breaks in financial market volatility dynamics (2002, methodology): “We focus on the Kokoszka and Leipus (2000) and Lavielle and Moulines (2000) tests which monitor nonlinear transformations of returns processes (in square and absolute returns) without the need to specify any particular, restrictive functional form of the process.”
Extensions of some classical methods in change point analysis (2014, methodology): “In a nonlinear time series setting, parametric procedures were utilized by Berkes et al. (2004a) and Kokoszka and Leipus (2000) to detect breaks in the parameters of ARCH and GARCH processes, and by Berkes et al. (2004b) to sequentially monitor for breaks in the parameters of GARCH processes.”
Estimating Structural Changes in Regression Quantiles (2011, background): “Kokoszka and Leipus (1999, 2000) and Berkes et al. (2004) studied parameter change in GARCH processes.”
Structural breaks in time series (2013, methodology): “In a nonlinear time series setting parametric procedures were utilized by Kokoszka and Leipus (2000) to detect breaks in the parameters of ARCH processes, and by Berkes et al. (2009b) to sequentially monitor for breaks in the parameters of GARCH processes.”
Multiple breaks detection in general causal time series using penalized quasi-likelihood (2012, methodology): “, tk}, k 6= i (for instance in [18], [14] and [7]).”
Finite sample change point inference and identification for high‐dimensional mean vectors (2017, unknown): “…1987; Brodsky & Darkhovsky, 1993; Carlstein, 1988; Csörgo˝ & Horváth, 1997; Frick et al., 2014; Fryzlewicz, 2014; Garreau & Arlot, 2018; Harchaoui & LévyLeduc, 2010; Hariz et al., 2007; Harlé et al., 2016; Kokoszka & Leipus, 2000; Loader, 1996; Ombao et al., 2005; Yao, 1987; Zhang et al., 2010).”
Recent Advances in ARCH Modelling (2007, methodology): “The uniqueness and the necessity of (5) for the existence of such a solution also follow easily, see Kokoszka and Leipus (2000), Giraitis et al. (2000a).”