GARCHselection: Univariate GARCH selection criterion

In YiffyGuo/GabauerDavid-ConnectednessApproach: Connectedness Approach

Univariate GARCH selection criterion

Description

This function estimates and evaluates a combination of GARCH models with different distributions and suggests the best GARCH models among all alternatives given some test statistics

Usage

GARCHselection(
  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 optimal univariate GARCH model specification

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.

YiffyGuo/GabauerDavid-ConnectednessApproach documentation built on April 15, 2022, 5:20 p.m.