GARCHselection {ConnectednessApproach}

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.