MSGARCH (version 2.51)

predict.MSGARCH_SPEC: predict method.

Description

Conditional volatility (and predictive distribution) forecasts.

Usage

# S3 method for MSGARCH_SPEC
predict(
  object,
  newdata = NULL,
  nahead = 1L,
  do.return.draw = FALSE,
  par = NULL,
  do.cumulative = FALSE,
  ctr = list(),
  ...
)
# S3 method for MSGARCH_ML_FIT
predict(
  object,
  newdata = NULL,
  nahead = 1L,
  do.return.draw = FALSE,
  do.cumulative = FALSE,
  ctr = list(),
  ...
)
# S3 method for MSGARCH_MCMC_FIT
predict(
  object,
  newdata = NULL,
  nahead = 1L,
  do.return.draw = FALSE,
  do.cumulative = FALSE,
  ctr = list(),
  ...
)

Value

A list of class MSGARCH_FORECAST with the following elements:

• vol: Condititional volatility forecast (vector of size nahead).

• draw: If do.return.draw = TRUE:
  Draws sampled from the predictive distributions (matrix of size nahead x nsim).
  If do.return.draw = FALSE:
  NULL

The MSGARCH_FORECAST class contains the plot method.

Arguments

object

Model specification of class MSGARCH_SPEC created with CreateSpec or fit object of type MSGARCH_ML_FIT created with FitML or MSGARCH_MCMC_FIT created with FitMCMC.

newdata

Vector (of size T*) of new observations. (Default newdata = NULL)

nahead

Scalar indicating the number of step-ahead evaluation.

do.return.draw

Are simulation draws from the predictive distribution returned? (Default do.return.draw = FALSE)

par

Vector (of size d) or matrix (of size nmcmc x d) of parameter estimates where d must have the same length as the default parameters of the specification.

do.cumulative

Logical indicating if the conditional volatility prediction is computed on the cumulative simulations (typically log-returns, as they can be aggregated). (Default: do.cumulative = FALSE)

ctr

A list of control parameters:

• nsim (integer >= 0): Number indicating the number of simulation done for the conditional volatlity forecast at nahead > 1. (Default: nsim = 10000L)

...

Not used. Other arguments to predict.

Details

If a matrix of MCMC posterior draws is given, the Bayesian predictive conditional volatility (and predictive distribution) forecasts are returned.

Examples

# create specification
spec <- CreateSpec()

# load data
data("SMI", package = "MSGARCH")

# predict from specification
par <- c(0.1, 0.1, 0.8, 0.2, 0.1, 0.8, 0.99, 0.01)
set.seed(1234)
pred <- predict(object = spec, par = par, newdata = SMI, nahead = 5L)
head(pred)
plot(pred)

# predict from ML fit
fit <- FitML(spec = spec, data = SMI)
set.seed(1234)
pred <- predict(object = fit, nahead = 5L, do.return.draw = TRUE)
head(pred)
plot(pred)

if (FALSE) {
set.seed(1234)
fit <- FitMCMC(spec = spec, data = SMI)
pred <- predict(object = fit, nahead = 5L, do.return.draw = TRUE)
plot(pred)
}