ASYMPTOTIC INFERENCE FOR NONSTATIONARY GARCH

By Saskia Jensen, Anders Rahbek. Econometric Theory, 2004

225 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/927216b0a185494a8972e52fe3a1e6904a360094

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Citations in the corpus

Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model (2012, background): “The inference by QMLE remains stable due to this very specific behaviour, see [22].”
GARCH Models (2010, unknown): “7.13 (Constrained QMLE of the parameter α0 of a nonstationary ARCH(1) process) Jensen and Rahbek (2004a) consider the ARCH(1) model (7.15), in which the parameter ω0 > 0 is assumed to be known (ω0 = 1 for instance) and where only α0 is unknown.”
Feasible invertibility conditions and maximum likelihood estimation for observation-driven models (2018, methodology): “1 of [29] and rely on the restrictive and non-standard assumption that the true value of the unobserved time-varying parameter is known at time t = 0.”
Realized GARCH: a joint model for returns and realized measures of volatility (2012, unknown): “These results strongly favor the inclusion of the leverage function and show that the realized GARCH framework is superior to standard GARCH models, because the partial log-likelihood of any Realized GARCH models is substantially better than that of a standard GARCH(1,1).”
Unit root vector autoregression with volatility induced stationarity (2014, background): “Proof of Theorem 3: The result follows by establishing regularity conditions in Lemma 1 from Jensen and Rahbek (2004) for the score, information and third order derivatives of the log-likelihood function respectively.”
Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach (2009, methodology): “In particular, Jensen and Rahbek (2004) assume that zt i:i:d:(0; 1), with V ar(z2t ) = k < 1, and that the true parameters satisfy the condition E ln( 0z 2 t + 0) 0, where 0 and 0 denote the true values of the parameters and , i.e. the squared innovation and lagged conditional variance parameters,…”
Multivariate Variance Targeting in the BEKK-GARCH Model (2014, unknown): “Note that one could weaken this assumption so that {Xt}t=0,...,T is initiated from a fixed value, see Jensen and Rahbek (2004).”
Time‐series models with an EGB2 conditional distribution (2014, unknown): “2 C &2 2&2 # C 1: Consistency and asymptotic normality follow by verifying the conditions in Jensen and Rahbek (2004).”
Asymptotic inference for a nonstationary double AR(1) model (2008, background): “This phenomenon was observed by Jensen & Rahbek (2004a) when they studied the nonstationary arch(1) model.”
PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS (2011, unknown): “Contributions in the case of linear pure GARCH models include Lee and Hansen (1994), Lumsdaine (1996), Berkes, Horváth, and Kokoszka (2003), Jensen and Rahbek (2004), and Francq and Zakoı̈an (2004, 2007). These papers also contain further references. The linear ARMA–GARCH case has been studied in We”
Asymptotic Theory for the QMLE in GARCH-X Models With Stationary and Nonstationary Covariates (2013, methodology): “This conjecture is supported by the analysis of the QMLE in pure GARCH models (π = 0) by Jensen and Rahbek (2004) and Francq and Zakoïan (2012). They found that when the volatility process is nonstationary, ω0 is not identified.”
Asymptotic Properties of GARCH-X Processes (2015, result): “The constant term parameter ! is not identi…ed in our model, precisely for the same reason as in the nonstationary GARCH model studied by Jensen and Rahbek (2004).”