GEV: Generalized Extreme Value Distribution

In QRM: Provides R-Language Code to Examine Quantitative Risk Management Concepts

Description

Density, quantiles, cumulative probability, and fitting of the Generalized Extreme Value distribution.

Usage

pGEV(q, xi, mu = 0, sigma = 1) 
qGEV(p, xi, mu = 0, sigma = 1) 
dGEV(x, xi, mu = 0, sigma = 1, log = FALSE) 
rGEV(n, xi, mu = 0, sigma = 1)
fit.GEV(maxima, ...)

Arguments

log	logical, whether log values of density should be returned, default is FALSE.
maxima	vector, block maxima data
mu	numeric, location parameter.
n	integer, count of random variates.
p	vector, probabilities.
q	vector, quantiles.
sigma	numeric, scale parameter.
x	vector, values to evaluate density.
xi	numeric, shape parameter.
...	ellipsis, arguments are passed down to optim().

Value

numeric, probability (pGEV), quantile (qGEV), density (dGEV) or random variates (rGEV) for the GEV distribution with shape parameter xi, location parameter mu and scale parameter sigma. A list object in case of fit.GEV().

See Also

GPD

Examples

quantValue <- 4.5
pGEV(q = quantValue, xi = 0, mu = 1.0, sigma = 2.5) 
pGumbel(q = quantValue, mu = 1.0, sigma = 2.5)
## Fitting to monthly block-maxima
data(nasdaq)
l <- -returns(nasdaq)
em <- timeLastDayInMonth(time(l))
monmax <- aggregate(l, by = em, FUN = max) 
mod1 <- fit.GEV(monmax) 

Example output

Loading required package: gsl
Loading required package: Matrix
Loading required package: mvtnorm
Loading required package: numDeriv
Loading required package: timeSeries
Loading required package: timeDate

Attaching package: 'QRM'

The following object is masked from 'package:base':

    lbeta

[1] 0.7814556

QRM documentation built on April 14, 2020, 6:49 p.m.