Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile function, distribution function, random number generator or probability density function. ES is also known as Conditional Value at Risk (CVaR). Virtually any continuous distribution can be specified. The functions are vectorised over the arguments. The computations are done directly from the definitions, see e.g. Acerbi and Tasche (2002). Some support for GARCH models is provided, as well.
The latest stable version is on CRAN.
install.packages("cvar")
The vignette
Guide_cvar shipping
with the package gives illustrative examples (can also be opened from R with
vignette("Guide_cvar", package = "cvar")).
You can install the development version of cvar from Github:
library(devtools)
install_github("GeoBosh/cvar")
Package cvar is a small R package with, essentially two
functions — ES for computing the expected shortfall
and VaR for Value at Risk. The user specifies the
distribution by supplying one of the functions that define a
continuous distribution—currently this can be a quantile
function (qf), cumulative distribution function (cdf) or
probability density function (pdf). Virtually any continuous
distribution can be specified.
The functions are vectorised over the parameters of the distributions, making bulk computations more convenient, for example for forecasting or model evaluation.
The name of this package, "cvar", comes from Conditional Value at Risk (CVaR), which is an alternative term for expected shortfall.
We chose to use the standard names ES and VaR,
despite the possibility for name clashes with same named
functions in other packages, rather than invent possibly
difficult to remember alternatives. Just call the functions as
cvar::ES and cvar::VaR if necessary.
Locations-scale transformations can be specified separately
from the other distribution parameters. This is useful when
such parameters are not provided directly by the distribution
at hand. The use of these parameters often leads to more
efficient computations and better numerical accuracy even if
the distribution has its own parameters for this purpose. Some
of the examples for VaR and ES illustrate this
for the Gaussian distribution.
Since VaR is a quantile, functions computing it for a given
distribution are convenience functions. VaR exported by
cvar could be attractive in certain workflows because of
its vectorised distribution parameters, the location-scale
transformation and the possibility to compute it from cdf's
when quantile functions are not available.