A Multivariate GARCH Model with Time-Varying Correlations

By Y. Tse, Albert K. Tsui. 2000

386 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/4d2e332c7bc9244290170a2105ad6bdb5d0f9162

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Citations in the corpus

VOLATILITY AND CORRELATION FORECASTING (2006, background): “…toolbox discussed in econometrics and empirical oriented finance textbooks; see, e.g., Hamilton (1994), Mills (1993), Franses and van Dijk (2000), Gouriéroux and Jasiak (2001), Alexander (2001), Brooks (2002), Chan (2002), Tsay (2002), Christoffersen (2003), Enders (2004), and Taylor (2004).”
Residual-Based Diagnostics for Conditional Heteroscedasticity Models (2002, neutral): “Pij = plim( 1 T ( X dtij(3ti 3tj ¡ 1⁄2tij)) @`(μ) @μ0 ): (26)”
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison (2006, background): “Important contributions are Bollerslev et al. (1998), Diebold and Nerlove (1989), Bollerslev (1990), Engle et al. (1990), Engle and Kroner (1995), Braun et al. (1995), Engle (2002), Tse and Tusi (2002) , among many others.”
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2011, background): “by Ding and Engle (2001), Engle (2002), Engle and Sheppard (2001), and Tse and Tsui (2002) .”
MGARCH models: Trade-off between feasibility and flexibility (2018, background): “Consequently, several authors suggest models in which the correlations are time-varying; see Engle (2002), Tse and Tsui (2002) and Silvennoinen and Teräsvirta (2009b).”
Stress-testing Financial Systems: An Overview of Current Methodologies (2004, methodology): “The GARCH framework (eg. Bollerslev, Engle and Wooldridge,1988; Engle, 2002; Tse and Tsui, 2002; Lediot et al, 2003 and Zaffaroni, 2003) is among the most popular techniques to estimate timedependent conditional second moments.”
A New Class of Multivariate Skew Densities, with Application to GARCH Models (2002, methodology): “proposed by Tse and Tsui (1998) , with Þrst-order ARMA dynamics in the conditional variances”
Model Averaging in Risk Management with an Application to Futures Markets (2009, methodology): “(vi) The importance of the condition T1/T0 → 0 in cross validation of forecasts was put forward by West (1996). McCracken (2000) extends West’s framework to allow for nondifferentiable loss functions in a regression set-up.”
Forecasting time-varying covariance with a range-based dynamic conditional correlation model (2009, methodology): “3 Other econometric methods for estimating the time-varying correlation are proposed by Tsay (2002) and”
Sequential Conditional Correlations: Inference and Evaluation (2009, neutral): “time t 1, I will follow the of idea Tse and Tsui (2002) and instead use an average”
Estimating multivariate volatility models equation by equation (2016, methodology): “…conditional correlation (CCC) model that were introduced by Bollerslev (1990) and extended by Jeantheau (1998), the Baba, Engle, Kraft and Kroner (BEKK) model of Engle and Kroner (1995), and the dynamic conditional correlation (DCC) models that were proposed by Tse and Tsui (2002) and Engle (2002).”
Semi-Parametric Modelling of Correlation Dynamics (2005, methodology): “Second, Engle (2002) and Tse and Tsui (2002) introduced the class of dynamic conditional correlation (DCC) models, where Rt is allowed to vary according to a GARCH-type process, such as Qt = (1 − α − β)Q̄ + αεt−1ε′t−1 + βQt−1, (5) Rt = Q ∗ −1 t QtQ ∗ −1 t , (6) where Q̄ = E[εtε ′ t] is the…”
Efficient factor GARCH models and factor-DCC models (2009, methodology): “To relax the constant conditional correlation assumption, Engle (2002), Christodoulakis and Satchell (2002) and Tse and Tsui (2002) proposed the dynamic conditional correlation (DCC) model, the Correlated ARCH (CorrARCH) model, and the variable conditional correlation (VCC) model, respectively.”