ASYMPTOTIC THEORY FOR A VECTOR ARMA-GARCH MODEL
By S. Ling, Michael Mcaleer. Econometric Theory, 2003
924 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/605729f9a562adc7406500066dae4481b1ff5d1b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Michael Mcaleer
- Timo Teräsvirta
- Christian Francq
- Christian M. Hafner
- George Filis
- Jean-Michel Zakoian
- Stavros Degiannakis *
- Marcelo C. Medeiros
In Finance
- Michael Mcaleer
- Christian Francq
- Timo Teräsvirta
- Christian M. Hafner
- Jean-Michel Zakoian
- Esther Ruiz
- Marcelo C. Medeiros
- Tim Bollerslev
Citations in the corpus
- Multivariate GARCH models (2008, positive): “Ling and McAleer (2003) complement Jeantheau's results and also prove the asymptotic normality of the QMLE in the case of the ECCC–GARCH model.”
- Estimation and testing stationarity for double‐autoregressive models (2004, methodology): “When yt follows a non-trivial AR model, only the consistency of the QMLE was proved by Ling and McAleer (2003).”
- Conditional correlations and volatility spillovers between crude oil and stock index returns (2010, positive): “In order to incorporate interdependencies of volatilities across different assets and/or markets, Ling and McAleer (2003) proposed a vector autoregressive moving average (VARMA) specification of the conditional mean in (1), and the following GARCH specification for the conditional variance: ( )( )…”
- A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries (2008, methodology): “In addition, becauseg(ψ) is stationary withE [g(ψ)] = 0, by Theorem 3.1 in Ling and McAleer (2003) it follows that sup ψ∈Ψ ∣∣∣T−1 ∑T t=1 g(ψ) ∣∣∣ = op(1) and Condition (4) is satisfied.”
- Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes (2004, methodology): “Recent works on the estimation of ARMA-GARCH processes are Ling and Li (1997; 1998) and Ling and McAleer (2003a).”
- MODELING MULTIPLE REGIMES IN FINANCIAL VOLATILITY WITH A FLEXIBLE COEFFICIENT GARCH(1,1) MODEL (2009, methodology): “More recently, Ling and McAleer (2003) proved the consistency of the global QMLE for a vector autoregressive moving average–GARCH (VARMA-GARCH) model under only the second-order moment condition.”
- 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…”
- QML ESTIMATION OF A CLASS OF MULTIVARIATE ASYMMETRIC GARCH MODELS (2011, background): “Asymptotic results were established by Ling and McAleer (2003) for the constant conditional correlation (CCC) formulation of an autoregressive moving average GARCH (ARMAGARCH) model.”
- A CLOSED-FORM ESTIMATOR FOR THE GARCH(1,1) MODEL (2006, positive): “…~QMLE!+ The asymptotic properties of this estimator are by now well established ~see Jeantheau, 1998; Lee and Hansen, 1994; Lumsdaine, 1996; Ling and McAleer, 2003!, and its finite-sample properties have been examined through Monte Carlo studies ~Fiorentini, Calzolari, and Panattoni, 1996;…”
- LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises (2015, positive): “First, by theorem 3.1 in Ling and McAleer (2003), the dominated convergence theorem and the same argument as for Lemma A.4 in Zhu and Ling (2012), we can show that ζ̄n = E(ζt ) + op(1) = op(1); 1 n n∑ t=1 [ζ̃t (θ̃sn) − ζ̄n] = var(ζt ) + op(1) = Ew2t + op(1); and hence √ nρ̃k = 1√ nEw2t n∑ t=k+1 ζt…”
- The structure of dynamic correlations in multivariate stochastic volatility models (2009, background): “This specification also guarantees the positive definiteness of the conditional covariance matrix.”
- Estimating multivariate volatility models equation by equation (2016, positive): “(Bollerslev (1990) introduced this model in the case of diagonal matrices Ai and Bj. Ling and McAleer (2003) proved the asymptotic properties of a general version of this model (without any diagonality assumption) which was subsequently called the extended CCC model by He and Teräsvirta (2004).)”
- A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations (2002, background): “Recently, Ling and McAleer (2000) examined the asymptotic distribution of a class of vector ARMA-GARCH models.”