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dccmidas (version 0.1.2)

DCC Models with GARCH and GARCH-MIDAS Specifications in the Univariate Step, RiskMetrics, Moving Covariance and Scalar and Diagonal BEKK Models

Description

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) , the DCC-MIDAS of Colacito et al. (2011) , the Asymmetric DCC of Cappiello et al. , and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) . 'dccmidas' offers the possibility of including standard GARCH , GARCH-MIDAS and Double Asymmetric GARCH-MIDAS models in the univariate estimation. Moreover, also the scalar and diagonal BEKK 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.

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Version

Install

install.packages('dccmidas')

Monthly Downloads

195

Version

0.1.2

License

GPL-3

Maintainer

Vincenzo Candila

Last Published

February 21st, 2024

Functions in dccmidas (0.1.2)

ftse100

FTSE 100 data
indpro

Monthly U.S. Industrial Production
nasdaq

NASDAQ data
sBEKK_mat_est

sBEKK covariance matrix
riskmetrics_mat

RiskMetrics model
print.dccmidas

Print method for 'dccmidas' class
sBEKK_loglik

sBEKK log-likelihood
%^%

Power of a matrix
summary.dccmidas

Summary method for 'dccmidas' class
sp500

S&P 500 data
plot_dccmidas

Plot method for 'dccmidas' class
moving_cov

Moving Covariance model
dBEKK_loglik

dBEKK log-likelihood
bekk_fit

BEKK fit
Det

Matrix determinant
cov_eval

Var-cov matrix evaluation
Inv

Inverse of a matrix
dcc_fit

DCC fit (first and second steps)
QMLE_sd

Standard errors for the Quasi Maximum Likelihood estimator
a_dccmidas_loglik

A-DCC-MIDAS log-likelihood (second step)
deco_loglik

DECO log-likelihood (second step)
dcc_loglik

cDCC log-likelihood (second step)
dcc_mat_est

Obtains the matrix H_t and R_t, under the cDCC model
a_dcc_loglik

A-DCC log-likelihood (second step)
a_dccmidas_mat_est

Obtains the matrix H_t, R_t and long-run correlations, under the A-DCC-MIDAS model
a_dcc_mat_est

Obtains the matrix H_t and R_t, under the A-DCC model
deco_mat_est

Obtains the matrix H_t and R_t, under the DECO model
dBEKK_mat_est

dBEKK covariance matrix
dccmidas_loglik

DCC-MIDAS log-likelihood (second step)
dccmidas_mat_est

Obtains the matrix H_t, R_t and long-run correlations, under the DCC-MIDAS model