Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH

By Robert F. Engle, Kevin Sheppard. 2001

1,303 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/470ef9ea90323eeb3ad38cf2215ce09d39525501

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Citations in the corpus

Estimating High Dimensional Covariance Matrices and its Applications (2011, neutral): “Related issues can be found in Bollerslev (1987), Engle (2002), Engle and Kroner (1995), Engle et al. (1990), and Engle and Sheppard (2001), Ledoit et al. (2003), and Tsay (2002).”
VOLATILITY AND CORRELATION FORECASTING (2006, positive): “However, as formally shown in Engle and Sheppard (2001), the Ω t*t&1 parameters in (6.”
Continuous Time Wishart Process for Stochastic Risk (2006, neutral): “The need for this feature justified the introduction of the DCC model in the multivariate ARCH literature and explained why the other multivariate ARCH models are less interesting than the DCC.”
Systemic Risk in Europe (2013, positive): “Ht+1 = D 1/2 t+1 t+1D 1/2 t+1 , (10) t+1 = (diag (Qt+1)) 1/2 Qt+1 (diag (Qt+1)) 1/2 , (11) Qt+1 = ⌦+ 1Qt + 2 D 1/2 t 1 rt 1r 0 t 1D 1/2 t 1 , (12) 6Using daily returns between 2000 and 2012, the correlations between MSCI Europe and MSCI world indices are corr(rE,t, rW,t) = 0.”
Volatility linkages between energy and agricultural commodity prices (2016, positive): “+ u>t D−2t ut } =− 1 2 n∑ i=1 [ T log(2π) + T∑ t=1 { log(hit) + u2it hit ]} , (9) which is the sum of log-likelihoods of univariate GARCH equations (Engle and Sheppard, 2001).”
Stock Market Comovements in Central Europe: Evidence from Asymmetric DCC Model (2013, positive): “A necessary and sufficient condition for Rt to be positive definite is that Qt must be positive definite (Engle and Sheppard, 2001).”
Forecasting time-varying covariance with a range-based dynamic conditional correlation model (2009, methodology): “(17) It is shown in Engle and Sheppard (2001) that under some regularity conditions, the condition for consistency will be satisfied.”
Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008, neutral): “…to be caused by having relatively more noise in the intraday returns data and a well-known phenomenon in multivariate GARCH models (for daily returns) that the larger the number of assets, the lower the decay parameter, see Engle and Sheppard (2001) and Hafner and Franses (2003) for discussion.”
Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, positive): “For the multivariate case, we consider the approach of Engle and Sheppard (2001) where the multi-step ahead forecasts of the DCC matrices are computed as E½RhþljF h 1 ¼ Xl 1 j¼0 ð1 d1 d2ÞCðd1 þ d2Þj þ ðd1 þ d2ÞlE½RhjF h 1 ; (2.8) 242 Journal of Financial Econometrics D ow nloaded from…”
Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model (2009, neutral): “by Tse and Tsui (2002), the DCC model by Engle (2002) and Engle and Sheppard (2001) ,”
Simple Tests for Models of Dependence between Multiple Financial Time Series, with Applications to U.S. Equity Returns and Exchange Rates (2004, positive): “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).”
New frontiers for arch models (2002, neutral): “Engle and Sheppard(2001) estimate and test models of up to 100 assets.”
The 'Greatest' Carry Trade Ever? Understanding Eurozone Bank Risks (2013, neutral): “Fischer et al. (2012) focus on the investment behavior of German Landesbanken and document a searching for yield due to risk shifting incentives after the announcement that government guarantees will be revoked. We show in our paper that yield chasing by European banks implies investing in high yiel”