OpVaR: Statistical Methods for Modelling Operational Risk

Functions for computing the value-at-risk in compound Poisson models. The implementation comprises functions for modeling loss frequencies and loss severities with plain, mixed (Frigessi et al. (2012) <doi:10.1023/A:1024072610684>) or spliced distributions using Maximum Likelihood estimation and Bayesian approaches (Ergashev et al. (2013) <doi:10.21314/JOP.2013.131>). In particular, the parametrization of tail distributions includes the fitting of Tukey-type distributions (Kuo and Headrick (2014) <doi:10.1155/2014/645823>). Furthermore, the package contains the modeling of bivariate dependencies between loss severities and frequencies, Monte Carlo simulation for total loss estimation as well as a closed-form approximation based on Degen (2010) <doi:10.21314/JOP.2010.084> to determine the value-at-risk.

Version: 1.1.1
Imports: VineCopula, tea, actuar, truncnorm, ReIns, MASS, pracma, evmix
Suggests: knitr, rmarkdown
Published: 2020-07-02
Author: Christina Zou [aut,cre], Marius Pfeuffer [aut], Matthias Fischer [aut], Kristina Dehler [ctb], Nicole Derfuss [ctb], Benedikt Graswald [ctb], Linda Moestel [ctb], Jixuan Wang [ctb], Leonie Wicht [ctb]
Maintainer: Christina Zou <christina.zou at maths.ox.ac.uk>
License: GPL-3
NeedsCompilation: no
CRAN checks: OpVaR results

Downloads:

Reference manual: OpVaR.pdf
Vignettes: OpVaR: Modeling Operational (Value-At-)Risk in R
Package source: OpVaR_1.1.1.tar.gz
Windows binaries: r-devel: OpVaR_1.1.1.zip, r-release: OpVaR_1.1.1.zip, r-oldrel: OpVaR_1.1.1.zip
macOS binaries: r-release (arm64): OpVaR_1.1.1.tgz, r-release (x86_64): OpVaR_1.1.1.tgz, r-oldrel: OpVaR_1.1.1.tgz
Old sources: OpVaR archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=OpVaR to link to this page.