On Adaptive Estimation in Nonstationary Arma Models with Garch Errors

By S. Ling, Michael Mcaleer. 2003

145 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/eeb9be679629957d634a47a027edd5c5fc7f3355

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Citations in the corpus

Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes (2004, methodology): “These time series models capture several important features of financial series, such as leptokurticity and volatility clustering – see Mikosch (2001) for a recent paper on GARCH and stochastic volatility models. The asymptotic properties of the quasi-maximum likelihood estimator (QMLE) were first e”
Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models (2007, methodology): “Similar to the proof of Lemma 4.2 in Ling and McAleer (2003a), we can show that S0 and O0 are positive definite (see also Francq and Zakoı̈an, 2004).”
Fitting an Error Distribution in Some Heteroscedastic Time Series Models (2006, unknown): “1 in [26], under P0n it follows that λn(n −1/2δ) = δn−1/2 n ∑ i=1 ζi(ηi(θ)) − (1)2δ(2)σ 2 + op(1).”
GARCH Models (2010, unknown): “Theorems 11.7 and 11.8 are concerned with the CCC formulation, and allow us to study a subclass of the models considered by Ling and McAleer (2003a), but do not cover the models studied by Comte and Lieberman (2003) or those studied by Hafner and Preminger (2009b).”
Self‐weighted least absolute deviation estimation for infinite variance autoregressive models (2005, methodology): “Even if contiguity holds, it would be difficult to prove that in the usual method as in Ling and McAleer (2003) .”
A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors (2001, methodology): “This method was extended to nonstationary ARMA models with higher-order GARCH(r,s) errors by Ling and McAleer (2002a) .”
An econometric analysis of asymmetric volatility : Theory and application to patents (2007, methodology): “Ling and McAleer (2002b) derived the unique strictly stationary and ergodic solution of a family of GARCH processes, which includes GJR(1,1) as a special case, a simple sufficient condition for the existence of the solution, and the necessary and sufficient condition for the existence of the…”
Modelling multivariate volatilities via conditionally uncorrelated components (2008, background): “Conditionally Uncorrelated Components 685 Model (2.10) may be viewed as a special case of the vectorized auto-regressive moving average–GARCH model of which the conditions for stationarity and ergodicity may be found in, for example, Ling and McAleer (2003).”
TESTING FOR A LINEAR MA MODEL AGAINST THRESHOLD MA MODELS (2005, background): “2) in [25], we can show that the conclusions hold.”
Structure and Asymptotic Theory for Multivariate Asymmetric Conditional Volatility (2009, background): “Ling and McAleer ( 2003b ) investigate the properties of adaptive estimators for univariate nonstationary ARMA models with GARCH( p , q ) errors.”
Testing the Nullity of GARCH Coefficients: Correction of the Standard Tests and Relative Efficiency Comparisons (2009, background): “From Drost and Klaassen (1997), Drost, Klaassen, and Werker (1997), and Ling and McAleer (2003), it is known that, under mild regularity conditions, GARCH processes are locally asymptotically normal with information matrix If ¼ if 4 E 1 s4t @s2t @u @s2t @u9 ðu0Þ ¼ if 4 J: ð19Þ In this framework the…”