BivariateDCCGARCH: Bivariate DCC-GARCH
In ConnectednessApproach: Connectedness Approach

BivariateDCCGARCH

R Documentation

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.

ConnectednessApproach documentation built on June 22, 2024, 10:22 a.m.