A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations

By Y. Tse, Albert K. Tsui. 2002

1,167 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fee1e47865b8a5d599f9c8870d7c2c68d7c6d3ad

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Citations in the corpus

Modeling rainfall–runoff relationship using multivariate GARCH model (2013, methodology): “The CCC-MGARCH model was then widely applied in empirical research mostly because of its computational simplicity (Tse and Tsui, 2002).”
Ten Things You Should Know About the Dynamic Conditional Correlation Representation (2013, background): “An alternative representation which avoids this last criticism, but nevertheless has no discussion of the mathematical properties or demonstration of the asymptotic properties of the estimators, has been proposed by Tse and Tsui (2002).”
Forecasting time-varying covariance with a range-based dynamic conditional correlation model (2009, methodology): “Other econometric methods for estimating the time-varying correlation are proposed by Tsay (2002) and by Tse and Tsui (2002), too.”
Volatility transmission across markets: a Multichain Markov Switching model (2007, methodology): “Engle and Kroner, 1995 ; Engle, 2002 ; Tse and Tsui, 2002 ) allow for studying the dynamics of conditional covariances as representing co-movements in second moments; for example, this approach was used by Higgs and Worthington ( 2004 ), who study the transmission of returns and volatility among ”
The Copula-GARCH model of conditional dependencies: An international stock market application (2006, methodology): “Therefore, we first consider a time-varying correlation (TVC) approach, as proposed by Tse and Tsui (2002) in their modeling of the Pearson’s correlation in a GARCH context.”
A New Parametrization of Correlation Matrices (2020, methodology): “Modeling the correlation matrix separately from the individual variances is commonly done in multivariate GARCH models, see e.g. Bollerslev (1990), Engle (2002), Tse and Tsui (2002), and Engle and Kelly (2012).”
Modeling multivariate autoregressive conditional heteroskedasticity with the double smooth transition conditional correlation GARCH Model (2009, positive): “In order to remedy this problem, Tse and Tsui (2002) and Engle (2002) introduced models with dynamic conditional correlations called the VC–GARCH and the DCC–GARCH model, respectively, that impose GARCH-type structure on the correlations.”
Multivariate GARCH models (2008, positive): “Due to the intuitive interpretation of correlations, there exist a vast number of proposals for specifying Pt. Tse and Tsui (2002) imposed GARCH type of dynamics on the conditional correlations.”
Bayesian Inference Methods for Univariate and Multivariate GARCH Models: A Survey (2014, positive): “In the paper by Tse & Tsui (2002), the conditional correlation matrix is R where θ 1 and θ 2 are non-negative scalar parameters, such that θ 1 + θ 2 1 , R is a positive deﬁnite matrix such that ρ ii = 1 and Ψ t − 1 is a K × K sample correlation matrix of the past m standardized mean-corrected…”
Multivariate GARCH Models: A Survey (2003, methodology): “Definition 8 The DCC model of Tse and Tsui (2002) or DCCT (M) is defined as: Ht = DtRtDt, (35) where Dt is defined in (32), hiit can be defined as any univariate GARCH model, and Rt = (1 − θ1 − θ2)R + θ1Ψt−1 + θ2Rt−1.”
A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models (2005, methodology): “In a previous version of this article (Bauwens and Laurent 2002), we estimated the multivariate GARCH model of Tse and Tsui (2002) with a normal, Student, and skew-Student likelihood on four daily indexes.”
Temporal aggregation of univariate and multivariate time series models: a survey (2008, positive): “The multivariate GARCH literature is extensive, including, among others, Bollerslev et al. (1988), Engle et al. (1990), Engle and Kroner (1995), Kroner and Ng (1998), Hafner and Herwartz (1998), van der Weide (2002), Tse and Tsui (2002), Engle (2002) and Kawakatsu (2006).”
Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model (2009, methodology): “The VC model by Tse and Tsui (2002) specifies univariate GARCH(p,q) models for individual returns and GARCH-type dynamic evolutions for the conditional correlation process {Rt}: Rt = ( 1− p∑ i=1 γi − q∑ j=1 βj ) R̄+ p∑ i=1 γiRt−i + q∑ j=1 βjR̂t−j, (2.5) where R̄, Rt, and R̂t are the unconditional,…”
Forecasting value‐at‐risk with a parsimonious portfolio spillover GARCH (PS‐GARCH) model (2008, methodology): “Correlation (VCC) model of Tse and Tsui (2002) use a two-step estimation procedure to”
Is the Potential for International Diversification Disappearing? A Dynamic Copula Approach (2012, positive): “To capture these stylized facts, we generalize the flexible dynamic conditional correlation (DCC) model of Engle (2002) and Tse and Tsui (2002) in two ways: First, we do not model dependence as mean-reverting but instead allow it to mean revert to a possibly nonlinear trend.”
New Evidence of Asymmetric Dependence Structures in International Equity Markets (2008, positive): “I also model the dynamics of dependence parameters for copulas as in Patton (2006) in which the DCC framework performed in Engle (2002) and Tse and Tsui (2002) is utilized.”
Correlation Dynamics and International Diversification Benefits (2014, positive): “We then introduce the dynamic conditional correlation (DCC) model of Engle (2002) and Tse and Tsui (2002), which allows for added flexibility in that it separates the modeling of volatility dynamics from correlation dynamics the latter of which is our main focus.”