Large Scale Conditional Covariance Matrix Modeling, Estimation and Testing

By Zhuanxin Ding, Robert F. Engle. 2001

277 citations recorded by the source corpus.

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Citations in the corpus

THE INTERTEMPORAL RELATION BETWEEN EXPECTED RETURNS AND RISK (2008, methodology): “…covariance specifications are used in Baillie and Bollerslev (1992), Bollerslev (1990), Bollerslev, Engle, and Wooldridge (1988), Bollerslev and Wooldridge (1992), Ding and Engle (2001), Engle and Kroner (1995), Engle and Mezrich (1996), Engle, Ng, and Wooldridge (1990), and Kroner and Ng (1998).”
Modeling Realized Covariances and Returns (2013, methodology): “Ding and Engle (2001) introduce the vector-diagonal GARCH (VD-GARCH-t) model to which we add Student-t innovations as follows rt|r1:t−1 ∼ t(0, Ht, ζ) (28) Ht = CC ′ + aa′ rt−1r′t−1 + bb′ Ht−1, (29) where rt is a k-dimensional daily return series, and r1:t−1 = {r1, . . . , rt−1}.”
Multivariate Stochastic Volatility: A Review (2006, background): “According to Ding and Engle (2001) and Engle (2002), if A, B and (ll ′ − A − B) are positive semidefinite, then Qt will also be positive semidefinite.”
Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns (2006, background): “It is clear from Equation (5) that a sufficient condition for Qt to be positive definite for all possible realizations is that the intercept, P A0 PA B0 PB G0 NG, is positive semi-definite and the initial covariance matrix Q0 is positive definite [see Ding and Engle (2001) for further details].”
Dynamic Conditional Correlation (2002, methodology): “For example, the MARCH family of Ding and Engle (2001) can be expressed in rst-order form as Qt D S 4‰‰ 0 ƒ Aƒ B5 C A ˜tƒ1˜ 0 tƒ1 C B Qtƒ11 (24) where ‰ is a vector of ones and is the Hadamard product of two identically sized matrices, which is computed simply by element-by-element multiplication.”
Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, background): “Bekaert and Wu [32], Ding and Engle [98] and Tai [285] have also modified multivariate ARCH models to accommodate asymmetric effects on conditional variances and covariances.”
Bayesian Inference Methods for Univariate and Multivariate GARCH Models: A Survey (2014, unknown): “They use a MGARCH speciﬁcation, proposed by Ding & Engle (2001), which is a diﬀerent representation of a well known DVEC model, introduced above.”
Dynamic Conditional Correlation: On Properties and Estimation (2013, unknown): “(14) For this model, Qt is pd provided that the intercept is pd, Aq ≡ [αij,q] is psd for q = 1, 2, .”
GENERALIZED AUTOREGRESSIVE CONDITIONAL CORRELATION (2008, methodology): “Furthermore, using the multivar?ate GARCH model in Ding and Engle (2001), Engle (2002) suggested a more general specification of Pt as”
A New Approach to Measuring Financial Contagion (2000, unknown): “One such parameterization is the multivariate generalized autoregressive conditional heteroscedasticity (GARCH) model of Ding and Engle (1994) .9 This Ding and Engle model constitutes our third scenario.”
Modeling the conditional covariance between stock and bond returns : A multivariate GARCH approach (2004, unknown): “Ding and Engle (2001), e.g., show that the diagonal VECH model of Bollerslev, Engle and Wooldridge (1988) can be written in matrix notation as Σt+1 = C +B ¯Σt +A¯ εtε 0 t, (5) where Σt is the conditional covariance matrix at time t, C, A and B are all (N × N) parameter matrices and ¯ denotes the…”