DCC Models with GARCH and GARCH-MIDAS Specifications in the Univariate Step, RiskMetrics, Moving Covariance and Scalar and Diagonal BEKK Models
A-DCC log-likelihood (second step)
Obtains the matrix H_t and R_t, under the A-DCC model
A-DCC-MIDAS log-likelihood (second step)
Obtains the matrix H_t, R_t and long-run correlations, under the A-DCC...
BEKK fit
Var-cov matrix evaluation
dBEKK log-likelihood
dBEKK covariance matrix
DCC fit (first and second steps)
cDCC log-likelihood (second step)
Obtains the matrix H_t and R_t, under the cDCC model
DCC-MIDAS log-likelihood (second step)
Obtains the matrix H_t, R_t and long-run correlations, under the DCC-M...
DECO log-likelihood (second step)
Obtains the matrix H_t and R_t, under the DECO model
Matrix determinant
Power of a matrix
Inverse of a matrix
Moving Covariance model
Plot method for 'dccmidas' class
Print method for 'dccmidas' class
Standard errors for the Quasi Maximum Likelihood estimator
RiskMetrics model
sBEKK log-likelihood
sBEKK covariance matrix
Summary method for 'dccmidas' class
Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) <doi:10.1080/07350015.2013.771027>, the DCC-MIDAS of Colacito et al. (2011) <doi:10.1016/j.jeconom.2011.02.013>, the Asymmetric DCC of Cappiello et al. <doi:10.1093/jjfinec/nbl005>, and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) <doi:10.1080/07350015.2011.652048>. 'dccmidas' offers the possibility of including standard GARCH <doi:10.1016/0304-4076(86)90063-1>, GARCH-MIDAS <doi:10.1162/REST_a_00300> and Double Asymmetric GARCH-MIDAS <doi:10.1016/j.econmod.2018.07.025> models in the univariate estimation. Moreover, also the scalar and diagonal BEKK <doi:10.1017/S0266466600009063> models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications.