Dynamic Conditional Correlation a Simple Class of Multivariate GARCH Models

By Robert F. Engle. 2000

1,575 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/a353c6a37995afba4606d7e04ec37f1ab3c53a5b

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Citations in the corpus

Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification (2006, background): “It can be regarded as a special case of the DCC model proposed in Engle (2002) and Engle and Sheppard (2001).”
Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries (2011, methodology): “Engle (2002) introduced the Dynamic Conditional Correlation GARCH, or the DCC-GARCH, model.”
Copula-Based Models for Financial Time Series (2009, methodology): “…copula-based model of up to ve assets, building on Chen and Fan (2006b), discussed below, where the marginal distributions are estimated nonparametrically and the conditional copula is speci ed to be Normal, with a correlation matrix that evolves according to the DCC speci cation of Engle (2002).”
On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? (2017, positive): “Unlike other multivariate GARCH models, such as the BEKK (Baba-Engle-Kraft-Kroner) and constant conditional correlation (CCC) models, which may experience convergence problems and unreasonable parameter estimates, the DCC model of Engle (2002) has the ability to capture the time-varying and dynamic ”
How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets (2014, methodology): “Following Burns, Engle, and Mezrich (1998), we estimate the unobserved component using the linear projection of the observed unsynchronized return on the information set that includes all returns known at the time of synchronization.”
Factor stochastic volatility with time varying loadings and Markov switching regimes (2007, methodology): “This form is used, for example, in Geweke and Zhou (1996), Aguilar and West (2000) and Lopes and Migon (2002), and provides both identification and, often, useful interpretation of the factor model.”
“De-financialization” of commodities? Evidence from stock, crude oil and natural gas markets (2017, methodology): “Correlation of Engle (2002); VECH: Multivariate GARCHwith time-varying covariances of”
Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management (2004, neutral): “Of course, the use of model averaging techniques in econometrics is not new and dates back to the work of Granger and Newbold (1977) on forecast combination.”
Consistently determining the number of factors in multivariate volatility modelling (2015, methodology): “To overcome the difficulties caused by overparametrization for multivariate GARCH(p, q) models, there are such proposals as those of Engle (2002), Fan, Wang, and Yao (2008), and Pan et al. (2011), among others.”
On the links between stock and commodity markets' volatility (2013, methodology): “From a methodological viewpoint, we follow the dynamic conditional correlation (DCC) GARCH approach introduced by Engle (2002)which allows the assessment of the changes in correlations between commodity and stock returns over time.”
Fitting and Testing Vast Dimensional Time-Varying Covariance Models (2007, methodology): “When K is small compared to T then inference can be thought of as a two stage GMM problem, whose theory is spelt out in, for example, Newey and McFadden (1994) and Engle and Sheppard (2001). All this is well known.”
Modelling multivariate volatilities via conditionally uncorrelated components (2008, methodology): “To overcome the computational difficulty, Engle (2002) proposes a two-step estimation procedure as follows: first fit each σt,ii in (1.”
Simple Tests for Models of Dependence between Multiple Financial Time Series, with Applications to U.S. Equity Returns and Exchange Rates (2004, methodology): “To allow for possible time variation in the conditional copula of these assets we employ the Dynamic Conditional Correlation (DCC) model of Engle (2002) and Engle and Sheppard (2001).”