summary.MDSVfilter: Summarize and print MDSV Filtering

In Abdoulhaki/MDSV: Multifractal Discrete Stochastic Volatility

Summarize and print MDSV Filtering

Description

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

Usage

## S3 method for class 'MDSVfilter'
summary(object, ...)

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

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

Arguments

object	An object of class MDSVfilter, output of the function MDSVfilter.
...	Further arguments passed to or from other methods.
x	An object of class summary.MDSVfilter, output of the function summary.MDSVfilter or class MDSVfilter of the function MDSVfilter.

Value

A list consisting of:

• ModelType : type of model to be filtered.

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

• N : number of components for the MDSV process.

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

• data : data use for the filtering.

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

• estimates : input parameters.

• LogLikelihood : log-likelihood of the model on the data.

• AIC : Akaike Information Criteria of the model on the data.

• BIC : Bayesian Information Criteria of the model on the data.

• Levier : numeric vector representing the leverage effect at each date. Levier is 1 when no leverage is detected

• filtred_proba : matrix containing the filtred probabilities \mathbb{P}(C_t=c_i\mid x_1,\dots,\x_t) of the Markov Chain.

• smoothed_proba : matrix containing the smoothed probabilities \mathbb{P}(C_t=c_i\mid x_1,\dots,\x_T) of the Markov Chain.

• Marg_loglik : marginal log-likelihood corresponding to the log-likelihood of log-returns. This is only return when ModelType = 2.

• VaR : Value-at-Risk compute empirically.

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.