BivariateDCCGARCH {ConnectednessApproach}

Bivariate DCC-GARCH

Description

This function multiple Bivariate DCC-GARCH models that captures more accurately conditional covariances and correlations

Usage

BivariateDCCGARCH(
  x,
  spec,
  copula = "mvt",
  method = "Kendall",
  transformation = "parametric",
  time.varying = TRUE,
  asymmetric = FALSE
)

Arguments

x

zoo dataset

spec

A cGARCHspec A cGARCHspec object created by calling cgarchspec.

copula

"mvnorm" or "mvt" (see, rmgarch package)

method

"Kendall" or "ML" (see, rmgarch package)

transformation

"parametric", "empirical" or "spd" (see, rmgarch package)

time.varying

Boolean value to either choose DCC-GARCH or CCC-GARCH

asymmetric

Whether to include an asymmetry term to the DCC model (thus estimating the aDCC).

Value

Estimate Bivariate DCC-GARCH

Author(s)

David Gabauer

References

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.