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OpVaR (version 1.0.5)

Statistical Methods for Modeling Operational Risk

Description

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) ) or spliced distributions using Maximum Likelihood estimation and Bayesian approaches (Ergashev et al. (2013) ). In particular, the parametrization of tail distributions includes the fitting of Tukey-type distributions (Kuo and Headrick (2014) ). 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) to determine the value-at-risk.

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Version

Install

install.packages('OpVaR')

Monthly Downloads

55

Version

1.0.5

License

GPL-3

Maintainer

Christina Zou

Last Published

May 29th, 2018

Functions in OpVaR (1.0.5)

sla

Single-Loss Approximation for Operational Value at Risk
goftest

Goodness of fit tests for severity distributions
mcSim

Monte Carlo Simulation from opriskmodel objects for total loss estimation
lossdat

Example loss data set
spliced

Spliced Distributions
Mixing

Extended Dynamic Weighted Mixture Model
buildSplicedSevdist

Building a sevdist object with a spliced distribution
buildFreqdist

Building a freqdist object
gh

Tukey's gh Distribution
fitThreshold

Threshold estimation for spliced distribution
gpd

Generalized Pareto Distribution
buildPlainSevdist

Building a sevdist object with a plain distribution
OpVaR-package

OpVaR
fitSpliced

Estimation of the threshold, the body and the tail parameters for a spliced distribution
fitSplicedBayes

Parameter Estimation for Spliced Distributions
fitFreqdist

Fitting the frequency distribution
fitDependency

Function for fitting bivariate Copulas
fitPlain

Fit plain distribution models
fitMixing

Maximum Likelihood Estimation
dsevdist

Evaluating Plain, Spliced or Mixing Severity Distributions
buildMixingSevdist

Building a dynamic mixture model as a sevdist object
fitSplicedBestFit

Fitting a spliced distribution over a given data set
fitWeights

Fitting the weights of the body and the tail for a spliced distribution