predict function

predict method.

Conditional volatility (and predictive distribution) forecasts.

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

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

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

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.

Returns

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.

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)

## Not run:

set.seed(1234)
fit <- FitMCMC(spec = spec, data = SMI)
pred <- predict(object = fit, nahead = 5L, do.return.draw = TRUE)
plot(pred)
## End(Not run)