class: ACD Rolling Forecast Class

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Description

Class for the ACD rolling forecast.

Slots

forecast:

Object of class "vector"

model:

Object of class "vector"

Methods

as.data.frame

signature(x = "ACDroll"): Extracts various values from object (see note).

resume

signature(object = "ACDroll"): Resumes a rolling backtest which has non-converged windows using alternative solver and control parameters.

coef

signature(object = "ACDroll"): Extracts the list of coefficients for each estimated window in the rolling backtest.

show

signature(object = "ACDroll"): Summary.

quantile

signature(x = "ACDroll"): Calculates and returns, given a vector of probabilities (additional argument “probs”), the conditional quantiles of the rolling object as an xts matrix.

pit

signature(object = "ACDroll"): Calculates and returns the conditional probability integral transform given the realized data and forecast density.

skew

signature(object = "ACDroll"): conditional skew.

sigma

signature(object = "ACDroll"): conditional volatility.

shape

signature(object = "ACDroll"): conditional shape.

skewness

signature(object = "ACDroll"): conditional skewness.

kurtosis

signature(object = "ACDroll"): conditional excess kurtosis.

convergence

signature(object = "ACDroll"): Returns the convergence code for the estimation windows, with 0 indicating that all have converged and 1 that there were non-converged windows. In the latter case the ‘nonconverged’ attribute is also printed of those windows which failed to converge.

show

signature(object = "ACDroll"): Summary.

Note

The as.data.frame extractor method allows the extraction of either the conditional forecast density or the VaR. It takes additional argument which with valid values either “density” or “VaR”.
The coef method will return a list of the coefficients and their robust standard errors (assuming the keep.coef argument was set to TRUE in the acdroll function), and the ending date of each estimation window.

Author(s)

Alexios Ghalanos