Dynamic Conditional Correlation: On Properties and Estimation

By Gian Piero Aielli. 2013

440 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/390fbd94b45739ea819890457cbc01147195c8ef

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Citations in the corpus

MGARCH models: Trade-off between feasibility and flexibility (2018, methodology): “Aielli (2013) also proves that if S is positive definite and a + b 1, the correlated and the standardized return process are strictly and covariance stationary.”
Interconnectedness in the global financial market (2017, neutral): “While advances have been made in applying these models to larger data sets they are still impractical for cases like ours (see also Aielli, 2013; Engle et al., 2019; Greenwood-Nimmo et al., 2015).”
Are Bitcoin and Ethereum safe-havens for stocks during the COVID-19 pandemic? (2020, positive): “As a robustness check, we also estimate the corrected-DCC (cDCC) (Aielli, 2013) and superimpose the dynamic correlations on the DCC plot (Fig.”
Are Correlations Constant? Empirical and Theoretical Results on Popular Correlation Models in Finance (2017, positive): “This includes MGARCH models with autoregressive covariances such as the corrected DCC model of Aielli (2013), the diagonal VECH model of Bollerslev, Engle, and Wooldridge (1988), or the diagonal BEKK model of Engle and Kroner (1995).”
Large Dynamic Covariance Matrices (2019, background): “Aielli (2013) showed that there is a consistency issue related to the estimation of the intercept matrix in the standardDCCmodel described above.”
Fitting Vast Dimensional Time-Varying Covariance Models (2020, neutral): “We next consider CL estimation of the cDCC model (Aielli 2013).”
Hedging Strategies of Green Assets against Dirty Energy Assets (2020, methodology): “However, Aielli [18] argues that the DCC process of Engle [17] incorporates an asymptotic bias in the estimator of the covariance matrix, proposing the corrected DCC process that adjusts the correlation evolution process as follows:”
Ten Things You Should Know About the Dynamic Conditional Correlation Representation (2013, background): “In fact, Aielli (2013) discusses targeting and a modification to DCC to enable consistent estimation.”
Estimating multivariate volatility models equation by equation (2016, methodology): “In the so-called corrected DCC model of Aielli (2013), the conditional correlation matrix is modelled as a function of the past standardized returns as Rt =QÅ−1=2 t QtQ t , Qt = .”
Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications (2017, positive): “In this context, Aielli (2013) proves that the estimation of the covariance matrix Qt in this way is inconsistent because E[Rt]≠E[Qt], and suggests the following consistent model (cDCC model) for the correlation-driving process: Qt ¼ 1−a−bð ÞS þ a Q t−11=2ut−1u0t−1Q t−11=2 þ bQt−1; ð7Þ where S∗ is…”
Is the Potential for International Diversification Disappearing? A Dynamic Copula Approach (2012, positive): “…dynamic is driven by the matrix Υt and the cross-products of the return shocks Γt = (1− βΓ − αΓ) [(1− ϕΓ)Ω + ϕΓΥt] + βΓΓt−1 + αΓz̄∗t−1z̄∗>t−1 (2.4) where z∗i,t = z ∗ i,t √ Γii,t using the Aielli (2009) modification, βΓ, αΓ, and ϕΓ are scalars, and z̄ ∗ t is an N -dimensional vector with…”
Robust Forecasting of Dynamic Conditional Correlation GARCH Models (2013, methodology): “However since Pt only involves the diagonal elements of Qt, the diagonal elements of which do not depend on Q (because Qii = 1 for i = 1, . . . , N), Aielli (2009) shows that for given values of α and β qii,t = (1− α− β) + αqii,t−1ỹ 2 i,t−1 + βqii,t−1, i = 1, . . . , N. (3.7) The remainder of the…”
On the links between stock and commodity markets' volatility (2013, background): “However, as noticed by Aielli (2011), the equality Q 1⁄4 E εtεt 1⁄2 & does not hold in the general case, and the interpretation of Q as well as its estimation are not straightforward (see Aielli, 2011 for some examples).”
Energy, precious metals, and GCC stock markets: Is there any risk spillover? (2019, methodology): “The basic formulation of DECO is derived from the consistent DCC (cDCC) of Aielli (2013) by the correlation-driving process: Q Q Q u u Q Q(1 ) ( ) Qt 1 a bS aQt 1 1/2ut 1ut 1’Qt 1 1/2 bQt 1, t t t t t t1 2 1 1 1/2 1 1 1 1/2 2 1= + + = + + (6) Engle and Kelly (2012) use the cDCC framework to…”
Correlation Dynamics and International Diversification Benefits (2014, positive): “Remember that in the DECO and DCC models, we employ the NGARCH(1,1) model of Engle and Ng (1993) in (2.8) for each market to pick up this second-moment dependence and to account for asymmetries.”
Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective? (2017, methodology): “problems of inconsistency and downward bias of the estimated parameters of the DCCGARCH model documented by Aielli (2013), it seems more appropriate to use an”