This function multiple Bivariate DCC-GARCH models that captures more accurately conditional covariances and correlations
BivariateDCCGARCH(
x,
spec,
copula = "mvt",
method = "Kendall",
transformation = "parametric",
time.varying = TRUE,
asymmetric = FALSE,
eval.se = FALSE
)Estimate Bivariate DCC-GARCH
zoo dataset
A cGARCHspec A cGARCHspec object created by calling cgarchspec.
"mvnorm" or "mvt" (see, rmgarch package)
"Kendall" or "ML" (see, rmgarch package)
"parametric", "empirical" or "spd" (see, rmgarch package)
Boolean value to either choose DCC-GARCH or CCC-GARCH
Whether to include an asymmetry term to the DCC model (thus estimating the aDCC).
Boolean value to compute standard errors
David Gabauer
Cocca, T., Gabauer, D., & Pomberger, S. (2024). Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R2 decomposed connectedness measures. Energy Economics.
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.