Extract GARCH model volatility

Extracts volatility from a fitted GARCH object.

Usage

# S3 method for fGARCH
volatility(object, type = c("sigma", "h"), ...)

Arguments

object

an object of class "fGARCH" as returned by garchFit().

type

a character string denoting if the conditional standard deviations "sigma" or the variances "h" should be returned.

...

currently not used.

Details

volatility is an S3 generic function for computation of volatility, see volatility for the default method.

The method for "fGARCH" objects, described here, extracts the volatility from slot @sigma.t or @h.t of an "fGARCH" object usually obtained from the function garchFit().

The class of the returned value depends on the input to the function garchFit who created the object. The returned value is always of the same class as the input object to the argument data in the function garchFit, i.e. if you fit a "timeSeries" object, you will get back from the function fitted also a "timeSeries" object, if you fit an object of class "zoo", you will get back again a "zoo" object. The same holds for a "numeric" vector, for a "data.frame", and for objects of class "ts", "mts".

In contrast, the slot itself always contains a numeric vector, independently of the class of the input data input, i.e. the function call slot(object, "fitted") will return a numeric vector.

Author

Diethelm Wuertz for the Rmetrics R-port

Note

(GNB) Contrary to the description of the returned value of the "fGARCH" method, it is always "numeric".

TODO: either implement the documented behaviour or fix the documentation.

Methods

Methods for volatility defined in package fGarch:

object = "fGARCH": Extractor function for volatility or standard deviation from an object of class "fGARCH".

Examples

## Swiss Pension fund Index -
   stopifnot(require("timeSeries")) # need package 'timeSeries'
   x = as.timeSeries(data(LPP2005REC, package = "timeSeries"))

## garchFit
   fit = garchFit(LPP40 ~ garch(1, 1), data = 100*x, trace = FALSE)
   fit
#> 
#> Title:
#>  GARCH Modelling 
#> 
#> Call:
#>  garchFit(formula = LPP40 ~ garch(1, 1), data = 100 * x, trace = FALSE) 
#> 
#> Mean and Variance Equation:
#>  LPP40 ~ garch(1, 1)
#> <environment: 0x569a04c83580>
#>  [data = 100 * x]
#> 
#> Conditional Distribution:
#>  norm 
#> 
#> Coefficient(s):
#>        mu      omega     alpha1      beta1  
#> 0.0491045  0.0083931  0.0881843  0.8016409  
#> 
#> Std. Errors:
#>  based on Hessian 
#> 
#> Error Analysis:
#>         Estimate  Std. Error  t value Pr(>|t|)    
#> mu      0.049104    0.013478    3.643 0.000269 ***
#> omega   0.008393    0.003493    2.403 0.016268 *  
#> alpha1  0.088184    0.035316    2.497 0.012524 *  
#> beta1   0.801641    0.071544   11.205  < 2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Log Likelihood:
#>  -38.61265    normalized:  -0.1024208 
#> 
#> Description:
#>  Tue Apr 30 14:51:55 2024 by user: georgi 
#> 

## volatility -
   # Standard Deviation:
   vola = volatility(fit, type = "sigma")
   head(vola)
#> [1] 0.2805121 0.2674818 0.2608402 0.2645372 0.2603407 0.2558202
   class(vola)
#> [1] "numeric"
   # Variance:
   vola = volatility(fit, type = "h")
   head(vola)
#> [1] 0.07868705 0.07154652 0.06803761 0.06997993 0.06777730 0.06544396
   class(vola)
#> [1] "numeric"

## slot -
   vola = slot(fit, "sigma.t")
   head(vola)
#> [1] 0.2805121 0.2674818 0.2608402 0.2645372 0.2603407 0.2558202
   class(vola)
#> [1] "numeric"
   vola = slot(fit, "h.t")
   head(vola)
#> [1] 0.07868705 0.07154652 0.06803761 0.06997993 0.06777730 0.06544396
   class(vola)
#> [1] "numeric"