Rmetrics - Modelling Extreme Events in Finance

Provides functions for analysing and modelling extreme events in financial time Series. The topics include: (i) data pre-processing, (ii) explorative data analysis, (iii) peak over threshold modelling, (iv) block maxima modelling, (v) estimation of VaR and CVaR, and (vi) the computation of the extreme index.

fExtremes

Rmetrics - Modelling Extreme Events in Finance

The fExtremes package provides functions for analysing and modelling extreme events in financial time Series. The topics include: (i) data pre-processing, (ii) explorative data analysis, (iii) peak over threshold modelling, (iv) block maxima modelling, (v) estimation of VaR and CVaR, and (vi) the computation of the extreme index. It is part of the Rmetrics software project.

An example

The following code simulates data from a GEV distribution and fits a GEV distribution to these data.

library(fExtremes)
# Simulate GEV Data, use default length n=1000
x <- gevSim(model = list(xi = 0.25, mu = 0 , beta = 1), n = 1000)

# Fit GEV data using maximum likelihood estimation
fit <- gevFit(x, type = "mle") 
fit
#> 
#> Title:
#>  GEV Parameter Estimation 
#> 
#> Call:
#>  gevFit(x = x, type = "mle")
#> 
#> Estimation Type:
#>   gev mle 
#> 
#> Estimated Parameters:
#>         xi         mu       beta 
#> 0.18304217 0.04548892 0.99014748 
#> 
#> Description
#>   Thu Dec 21 12:54:05 2023