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OpVaR (version 1.0)

Statistical Methods for Modeling Operational Risk

Description

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) ) or spliced distributions using Maximum Likelihood estimation and Bayesian approaches (Ergashev et al. (2013) ). In particular, the parametrization of tail distributions includes 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

57

Version

1.0

License

GPL-3

Maintainer

Christina Zou

Last Published

January 9th, 2018

Functions in OpVaR (1.0)

Mixing

Extended Dynamic Weighted Mixture Model
OpVaR-package

OpVaR
buildPlainSevdist

Building a sevdist object with a plain distribution
buildSplicedSevdist

Building a sevdist object with a spliced distribution
fitSplicedBayes

Parameter Estimation for Spliced Distributions
fitSplicedBestFit

Fitting a spliced distribution over a given data set
sla

Single-Loss Approximation for Operational Value at Risk
buildFreqdist

Building a freqdist object
buildMixingSevdist

Building a dynamic mixture model as a sevdist object
fitWeights

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

Goodness of fit tests for severity distributions
fitMixing

Maximum Likelihood Estimation
fitPlain

Fit plain distribution models
fitSpliced

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

Threshold estimation for spliced distribution
fitDependency

Function for fitting bivariate Copulas
fitFreqdist

Fitting the frequency distribution
lossdat

Example loss data set
mcSim

Monte Carlo Simulation from opriskmodel objects for total loss estimation
gh

Tukey's gh Distribution
gpd

Generalized Pareto Distribution
dsevdist

Evaluating Plain, Spliced or Mixing Severity Distributions
spliced

Spliced Distributions