summary.MDSVroll: Summarize and print MDSV Rolling estimates, volatility...

In Abdoulhaki/MDSV: Multifractal Discrete Stochastic Volatility

Summarize and print MDSV Rolling estimates, volatility forecast and backtesting

Description

Summary and print methods for the class MDSVroll as returned by the function MDSVroll.

Usage

## S3 method for class 'MDSVroll'
summary(
  object,
  VaR.test = TRUE,
  Loss.horizon = c(1, 5, 10, 25, 50, 75, 100),
  Loss.window = 756,
  ...
)

## S3 method for class 'summary.MDSVroll'
print(x, ...)

## S3 method for class 'MDSVroll'
print(x, ...)

Arguments

object	An object of class MDSVroll, output of the function MDSVroll.
VaR.test	Whether to perform Value at Risk forecast backtesting.
Loss.horizon	Horizon to summary the forecasts (cummulative and marginal).
Loss.window	Window on which the forecasts are summarized.
...	Further arguments passed to or from other methods.
x	An object of class summary.MDSVroll, output of the function summary.MDSVroll or class MDSVroll of the function MDSVroll.

Details

The summary.MDSVroll function compute the Root Mean Square Error, the Mean Average Error and the Quasi-Likehood error to summarize the forecasts. Those loss functions are compute for cummulative (by horizon) and marginal forecasts. For univariate realized variances model and joint log-returns and realized variances model, the loss functions are computed for the realized variances and for the univariate log-returns model, the loss functions are computed for the log-returns. For the Value-at-Risk basktest, the unconditionnal coverage test (see. Kupiec), the independance test (see Christoffersen) and the conditional coverage test (see Christoffersen and ) are performed.

Value

A list consisting of:

• N : number of components for the MDSV process.

• K : number of states of each MDSV process component.

• ModelType : type of models fitted.

• LEVIER : wheter the fit take the leverage effect into account or not.

• n.ahead : integer designing the forecast horizon.

• forecast.length : length of the total forecast for which out of sample data from the dataset will be used 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 where all previous data is used for the first estimation and then moved by a length equal to refit.every (unless the window.size option is used instead).

• window.size : If not NULL, determines the size of the moving window in the rolling estimation, which also determines the first point used.

• calculate.VaR : Whether to calculate forecast Value at Risk during the estimation.

• VaR.alpha : The Value at Risk tail level to calculate.

• cluster : A cluster object created by calling makeCluster from the parallel package.

• data : data use for the fitting.

• dates : vector or names of data designing the dates.

• estimates : matrix of all the parameters estimates at each date.

• prevision : matrix of all prevision made a each date.

• VaR.test : Whether to perform Value at Risk forecast backtesting.

• Loss.horizon : Horizon to summary the forecasts.

• Loss.window : Window on which the forecasts are summarized.

• Loss : Matrice containing the forecasts summary.

See Also

For fitting MDSVfit, filtering MDSVfilter, bootstrap forecasting MDSVboot and rolling estimation and forecast MDSVroll.

Abdoulhaki/MDSV documentation built on July 6, 2024, 4:03 p.m.