arfimaroll-methods: function: ARFIMA Rolling Density Forecast and Backtesting
In rgarch: Flexible GARCH modelling in R

Description Usage Arguments Details Value Author(s)

Method for creating rolling density forecast from ARFIMA models with option for refitting every n periods and some multicore parallel functionality.

arfimaroll(spec, data, n.ahead = 1, forecast.length = 500, refit.every = 25, 
refit.window = c("recursive", "moving"), parallel = FALSE, 
parallel.control = list(pkg = c("multicore", "snowfall"), cores = 2), 
solver = "solnp", fit.control = list(), solver.control = list(), 
calculate.VaR = TRUE, VaR.alpha = c(0.01, 0.05), ...)

`spec`	An ARFIMA spec object specifiying the desired model for testing.
`data`	A univariate dataset.
`n.ahead`	The number of periods to forecast.
`forecast.length`	The length of the total forecast for which out of sample data from the dataset will be excluded for testing.
`refit.every`	Determines every how many periods the model is re-estimated.
`refit.window`	Whether the refit is done on an expanding window including all the previous data or a moving window, the length of the window determined by the argument above (“refit.every”).
`parallel`	Whether to make use of parallel processing on multicore systems.
`parallel.control`	The parallel control options including the type of package for performing the parallel calculations (‘multicore’ for non-windows O/S and ‘snowfall’ for all O/S), and the number of cores to make use of.
`solver`	The solver to use.
`fit.control`	Control parameters parameters passed to the fitting function.
`solver.control`	Control parameters passed to the solver.
`calculate.VaR`	Whether to calculate forecast Value at Risk during the estimation.
`VaR.alpha`	The Value at Risk tail level to calculate.
`...`	.

ARFIMA models generate a partially time varying density based on the variation in the conditional mean values (sigma, skewness and shape are not time varying). The function first generates rolling forecasts of the ARFIMA model and then rescales the density from a standardized (0, 1, skew, shape) to the one representing the underlying return process (mu, sigma, skew, shape). Given this information it is then a simple matter to generate any measure of risk through the analytical evaluation of some type of function of the density. The function calculates one such measure (VaR), but since the full density parameters are returned, the user can calculate many others.
The argument refit.every determines every how many periods the fit is recalculated and the total forecast length actually calculated. For example, for a forecast length of 500 and refit.every of 25, this is 20 windows of 25 periods each for a total actual forecast length of 500. However, for a refit.every of 30, we take the floor of the division of 500 by 30 which is 16 windows of 30 periods each for a total actual forecast length of 480 (16 x 30). The important thing to remember about the refit.every is that it acts like the n.roll argument in the arfimaforecast function as it determines the number of rolls to perform. For example for n.ahead of 1 and refit.every of 25, the forecast is rolled every day using the filtered (actual) data of the previous period while for n.ahead of 1 and refit.every of 1 we will get 1 n.ahead forecasts for every day after which the model is refitted and reforecast for a total of 500 refits (when length.forecast is 500)!
The function has one main methods for viewing the data, namely the ‘report’ method. (see class ARFIMAroll for details on how to use these methods).

An object of class ARFIMAroll.

Alexios Ghalanos

rgarch documentation built on May 2, 2019, 5:22 p.m.