DCCGARCHselection: DCC-GARCH selection specification

In ConnectednessApproach: Connectedness Approach

DCC-GARCH selection specification

Description

This function calculates the optimal DCC-GARCH specification

Usage

DCCGARCHselection(
  x,
  distributions = c("norm", "snorm", "std", "sstd", "ged", "sged"),
  models = c("sGARCH", "eGARCH", "gjrGARCH", "iGARCH", "TGARCH", "AVGARCH", "NGARCH",
    "NAGARCH", "APARCH", "ALLGARCH"),
  prob = 0.05,
  conf.level = 0.9,
  lag = 20,
  ar = 0,
  ma = 0
)

Arguments

x	zoo data matrix
distributions	Vector of distributions
models	Vector of GARCH models
prob	The quantile (coverage) used for the VaR.
conf.level	Confidence level of VaR test statistics
lag	Lag length of weighted Portmanteau statistics
ar	AR(p)
ma	MA(q)

Value

Get best DCC-GARCH

Author(s)

David Gabauer

References

Ghalanos, A. (2014). rugarch: Univariate GARCH models, R package version 1.3-3.

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2021). The impact of Euro through time: Exchange rate dynamics under different regimes. International Journal of Finance & Economics, 26(1), 1375-1408.

ConnectednessApproach documentation built on Aug. 31, 2022, 5:05 p.m.