A Component Model for Dynamic Correlations

By Riccardo Colacito, Robert F. Engle, Eric Ghysels. 2011

318 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/67d42fd5b27f12ed0fc6962f5058e9821d842b17

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Citations in the corpus

Stock Market Volatility and Macroeconomic Fundamentals (2013, background): “Engle and Lee (1999), Ding and Granger (1996), Gallant, Hsu, and Tauchen (1999), Alizadeh, Brandt, and Diebold (2002), Chernov, Gallant, Ghysels, and Tauchen (2003) and Adrian and Rosenberg (2004), among many others.”
Multivariate Rotated ARCH Models (2014, unknown): “Also, a component structure which decomposes the conditional covariance matrix into long- and short-run components can be adopted as proposed in, for example, Colacito et al. (2011) for the DCC model.”
MGARCH models: Trade-off between feasibility and flexibility (2018, background): “Finally, several authors propose different short- and long-run sources that affect correlations; see, for example, Colacito et al. (2011), Rangel and Engle (2012), Audrino and Trojani (2011) and Audrino (2014).”
The shifting dependence dynamics between the G7 stock markets (2018, unknown): “Prior studies have mainly measured crossmarket dependence through linear correlation, GARCH-based dynamic correlations (e.g. Asai and McAleer 2009, Colacito et al. 2011, Javed and Virk 2017), copula-based dependence coefficient (e.g. Caillault and Guégan 2005, Huang et al. 2016) and realized…”
Variance Bounds on the Permanent and Transitory Components of Stochastic Discount Factors (2012, unknown): “The importance of linking markets has been emphasized by Campbell (1986); Campbell and Ammer (1993), Baele, Bekaert, and Inghelbrecht (2010), Colacito, Engle, and Ghysels (2011), and David and Veronesi (2008).”
Is the Potential for International Diversification Disappearing? A Dynamic Copula Approach (2012, unknown): “It may also prove useful to investigate the robustness of our findings to allowing for multiple regimes, or to the inclusion of multiple stochastic components, as for example in the model of Colacito, Engle, and Ghysels (2011).”
Correlation Dynamics and International Diversification Benefits (2014, unknown): “It may also prove useful to investigate the robustness of our findings to allowing for multiple regimes, or to the inclusion of multiple components, as for example in the model of Colacito, Engle, and Ghysels (2011).”
Anticipating Long‐Term Stock Market Volatility (2014, unknown): “A natural multivariate extension of the GARCH-MIDAS specification to the modeling of dynamic correlations is presented in Colacito et al. (2011) and applied in Conrad et al. (2012).”
On the Economic Sources of Stock Market Volatility (2009, unknown): “Engle and Lee (1999), Ding and Granger (1996), Gallant, Hsu, and Tauchen (1999), Alizadeh, Brandt, and Diebold (2002), Chernov, Gallant, Ghysels, and Tauchen (2003) and Adrian and Rosenberg (2004), among many others.”
The Conditional Autoregressive Wishart Model for Multivariate Stock Market Volatility (2010, unknown): “In the following, we introduce two of such extensions, where we combine the CAW model with the MIDAS (mixed data sampling) approach of Engle et al. (2008) and Colacito et al. (2009), and with an HAR (heterogenous autoregressive) component as used by Corsi (2009) and Bonato et al. (2009).”
Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification (2016, unknown): “First, we discuss the bivariate DCC-MIDAS model of Colacito et al. (2011). Second, we introduce the new DCC-MIDAS-XC model in which the long-run stock-bond correlation depends on a macro-finance variable (denoted by “X”) as well as the lagged realized correlation (denoted by “C”).”
On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets (2014, unknown): “The basic idea of Colacito et al. (2011) is to replace this unconditional correlationwith a slowly time-varying long-term component.”
The effect of economic policy uncertainty on the long-term correlation between U.S. stock and bond markets (2017, methodology): “DCC-MIDAS model Structural break Economic policy uncertainty Long-term correlation A B S T R A C T We investigate the time-varying long-term correlation of U.S. stock and bond markets, as influenced by an economic policy uncertainty (EPU) index based on the modified DCC-MIDAS model.”
Ten Things You Should Know About the Dynamic Conditional Correlation Representation (2013, unknown): “…and Franses and Hafner (2009), among others), the introduction of additional elements, such as asymmetry (see Cappiello et al. (2006) and Kasch and Caporin (2013), among others), and the proposal of alternative estimation methods (see Engle et al. (2008) and Colacito et al. (2011), among others).”