fscenario-methods: Scenario Generation
In rmgarch: Multivariate GARCH Models

Description Usage Arguments Details Value Author(s)

Generates a 1-ahead forecast scenario given a choice of data generating processes (for use in stochastic programming or risk management).

fscenario(Data, sim = 1000, roll = 0, 
	model = c("gogarch", "dcc", "cgarch", "var", "mdist"), 
	spec = NULL, 
	var.model = list(lag = 1, lag.max = NULL, 
				lag.criterion = c("AIC", "HQ", "SC", "FPE"), 
				robust = FALSE, robust.control = list("gamma" = 0.25, 
				"delta" = 0.01, "nc" = 10, "ns" = 500)),
	mdist.model = list(distribution = c("mvn", "mvt", "manig"), 
				AR = TRUE, lag = 1),
	spd.control = list(lower = 0.1, upper = 0.9, type = "pwm", 
				kernel = "epanech"),
	cov.method = c("ML", "LW", "EWMA", "MVE", "MCD", "MVT", "BS"),
	cov.options = list(shrinkage=-1, lambda = 0.96),
	solver = "solnp", solver.control = list(), 
	fit.control = list(eval.se = FALSE), 
	cluster = NULL, save.output = FALSE, save.dir = getwd(),
	save.name = paste("S", sample(1:1000, 1), sep = ""), rseed  = NULL, ...)

`Data`	An n-by-m data matrix or data.frame.
`sim`	The size of the simulated 1-ahead forecast.
`roll`	Whether to fit the data using (n - roll) periods and then return a (roll+1) 1-ahead rolling simulated scenarios.
`model`	A choice of 5 models for generating scenarios.
`spec`	Required if choosing ‘gogarch’, ‘dcc’ or ‘cgarch’, in which case this represents a specification object (see rmgarch package) .
`var.model`	Required if model is var.
`mdist.model`	Required if model is mdist, and provides details for the model estimation (not yet implemented).
`spd.control`	Required if model is “cgarch” and transformation is spd.
`cov.method`	For model “var” this represents the choice of covariance matrix to use to generate random deviates.
`cov.options`	For model “var” this provides the optional parameters to certain types of covariance estimation methods.
`solver`	The choice of solver to use for all models but “var”, and includes ‘solnp’, ‘nlminb’ and ‘nloptr’.
`solver.control`	Optional control options passed to the appropriate solver chosen.
`fit.control`	Control arguments passed to the fitting routine.
`cluster`	A cluster object created by calling `makeCluster` from the parallel package. If it is not NULL, then this will be used for parallel estimation of the refits (remember to stop the cluster on completion).
`save.output`	Whether output should be saved to file instead of being returned to the workspace.
`save.dir`	The directory to save output if save.output is TRUE.
`save.name`	The name of the file to save the output list.
`rseed`	A vector of length sim to initiate the random number generator.
`...`	Additional parameters passed to the model fitting routines. In particular, for the ‘gogarch’ model additional parameters are passed to the ICA routines, whereas for the ‘dcc’ and ‘cgarch’ models this would include the ‘realizedVol’ xts matrix for the realGARCH model.

The functionality here provides some wrapper functions, to create 1-ahead (and optionally rolling, useful for backtesting) scenarios for use in portfolio optimization using stochastic programming methods. The nature of these data generating processes (as implemented here) and resulting optimization problems results in the so called anticipative class of stochastic programming models. If save.output is chosen, and given a save.dir, the scenario is saved (using save.name) and an object is returned containing an empty list for the scenario but with a model details list and the seed values. This can then be passed on to the goload function which can read from the directory and return a complete object with the scenario.

A fScenario object containing the scenario and the model details (list). The scenario list contains a list of the (roll+1) simulated forecast scenarios, the list of (roll+1) simulated forecast residuals, the forecast conditional mean, the forecast covariance and the list of random generator seed values used for replication. In addition, for the gogarch model the ICA whitening (K) and rotation matrices are also returned and required for replication of results (these may be entered in the ‘gogarchspec’ function). Use the fitted method on the object to extract the simulated returns forecast.

Alexios Galanos

rmgarch documentation built on Feb. 5, 2022, 1:07 a.m.