OpVaR: Statistical Methods for Modeling Operational Risk

Functions for modeling operational (value-at-)risk. 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 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: 1.0
Imports: evmix, VineCopula, tea, actuar, vcd, goftest, truncnorm, ReIns, MASS
Suggests: knitr, rmarkdown
Published: 2018-01-09
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_1.0.tar.gz
Windows binaries: r-devel: OpVaR_1.0.zip, r-release: OpVaR_1.0.zip, r-oldrel: OpVaR_1.0.zip
OS X El Capitan binaries: r-release: OpVaR_1.0.tgz
OS X Mavericks binaries: r-oldrel: not available

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