mfGARCH (version 0.2.1)

fit_mfgarch: This function estimates a multiplicative mixed-frequency GARCH model. For the sake of numerical stability, it is best to multiply log returns by 100.

Description

This function estimates a multiplicative mixed-frequency GARCH model. For the sake of numerical stability, it is best to multiply log returns by 100.

Usage

fit_mfgarch(
  data,
  y,
  x = NULL,
  K = NULL,
  low.freq = "date",
  var.ratio.freq = NULL,
  gamma = TRUE,
  weighting = "beta.restricted",
  x.two = NULL,
  K.two = NULL,
  low.freq.two = NULL,
  weighting.two = NULL,
  multi.start = FALSE,
  control = list(par.start = NULL)
)

Arguments

data

data frame containing a column named date of type 'Date'.

y

name of high frequency dependent variable in df.

x

covariate employed in mfGARCH.

K

an integer specifying lag length K in the long-term component.

low.freq

a string of the low frequency variable in the df.

var.ratio.freq

specify a frequency column on which the variance ratio should be calculated.

gamma

if TRUE, an asymmetric GJR-GARCH is used as the short-term component. If FALSE, a simple GARCH(1,1) is employed.

weighting

specifies the weighting scheme employed in the long-term component. Options are "beta.restricted" (default) or "beta.unrestricted"

x.two

optional second covariate

K.two

lag lgenth of optional second covariate

low.freq.two

low frequency of optional second covariate

weighting.two

specifies the weighting scheme employed in the optional second long-term component. Currently, the only option is "beta.restricted"

multi.start

if TRUE, optimization is carried out with multiple starting values

control

a list

Value

A list of class mfGARCH with letters and numbers.

• par - vector of estimated parameters

• rob.std.err - sandwich/HAC-type standard errors

• broom.mgarch - a broom-like data.frame with entries 1) estimate: column of estimated parameters 2) rob.std.err - sandwich/HAC-type standard errors 3) p.value - p-values derived from sandwich/HAC-type standard errors 4) opg.std.err - Bollerslev-Wooldrige/OPG standard errors for GARCH processes 5) opg.p.value - corresponding alternative p-values

• tau - fitted long-term component

• g - fitted short-term component

• df.fitted - data frame with fitted values and residuals

• K - chosen lag-length in the long-term component

• weighting.scheme - chosen weighting scheme

• llh - log-likelihood value at estimated parameter vector

• bic - corresponding BIC value

• y - dependent variable y

• optim - output of the optimization routine

• K.two - lag-lenth of x.two if two covariates are employed

• weighting.scheme.two - chosen weighting scheme of x.two (if K.two != NULL)

• tau.forecast - one-step ahead forecast of the long-term component

• variance.ratio - calculated variance ratio

• est.weighting - estimated weighting scheme

• est.weighting.two - estimated weighting scheme of x.two (if K.two != NULL)

Examples

# NOT RUN {
fit_mfgarch(data = df_financial, y = "return", x = "nfci", low.freq = "week", K = 52)
fit_mfgarch(data = df_mfgarch, y = "return", x = "nfci", low.freq = "year_week", K = 52,
x.two = "dindpro", K.two = 12, low.freq.two = "year_month", weighting.two = "beta.restricted")
# }