BivariateDCCGARCH function

Bivariate DCC-GARCH

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
)

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).
• eval.se: Boolean value to compute standard errors

Returns

Estimate Bivariate DCC-GARCH

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.

Author(s)

David Gabauer