Modeling multivariate autoregressive conditional heteroskedasticity with the double smooth transition conditional correlation GARCH Model

By Annastiina Silvennoinen, Timo Teräsvirta. 2009

169 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/3ba17f658a6bf60da12b59e58177367976224974

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Citations in the corpus

Financialization, Crisis and Commodity Correlation Dynamics (2013, methodology): “Financialization and macroeconomic commodity price drivers are likely to change volatility and correlation gradually, so we model dynamics using Double Smooth Transition Conditional Correlation models (DSTCC GARCH) (Silvennoinen and Teräsvirta 2005, 2009).”
Effectiveness of copula-extreme value theory in estimating value-at-risk: empirical evidence from Asian emerging markets (2012, background): “Silvennoinen and Terasvirta (2009) and Tsafack (2009) find that the presence of asymmetry in the tail correlation may lead the DCC family models to biased estimation in portfolio management.”
Efficient estimation of a multivariate multiplicative volatility model (2010, background): “(36) We can bound this by the uniform convergence rate of Σ̃(·) times a factor that is Op(1).”
Dynamic Conditional Correlation: On Properties and Estimation (2013, unknown): “Related examples are the models by Pelletier (2006), McAleer, Chan, Hoti, and Lieberman (2008), Silvennoinen and Teräsvirta (2009), and Kwan, Li, and Ng (2010).”
MGARCH models: Trade-off between feasibility and flexibility (2018, methodology): “Also, Silvennoinen and Teräsvirta (2009b, 2015) propose the smooth transition conditional correlation (STCC) model allowing the correlations to vary smoothly between two different states.”
On the Forecasting Accuracy of Multivariate GARCH Models (2010, background): “A large number of multivariate GARCH models have been proposed in the literature, see Bauwens, Laurent, and Rombouts (2006) and Silvennoinen and Terasvirta (2009) for extensive surveys.”
Conditional Correlation Models of Autoregressive Conditional Heteroscedasticity With Nonstationary GARCH Equations (2014, methodology): “For recent surveys of multivariate GARCH models see Bauwens, Laurent, and Rombouts (2006) and Silvennoinen and Teräsvirta (2009).”
Multivariate GARCH models (2008, unknown): “ Silvennoinen and Terasvirta (2007) extend the Taylor expansion based test to the situation where the STCC–GARCH model is the null model and the alternative is the DSTCC–GARCH model.”