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 vectorized over the arguments. The computations are
done directly from the definitions, see e.g. Acerbi and Tasche (2002)
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 shipping with the package gives illustrative examples
(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 â€” 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 distributionâ€”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.
now \VignetteIndexEntry
in Guide_cvar.Rnw
is plain text.
added experimental support for GARCH models - currently GARCH(1,1) (the API may change).
now the first argument of VaR()
and ES()
can be a numeric vector. This is
useful, e.g., for computing VaR by simulation.
bugfix: in VaR_cdf()
and VaR_qf()
, the code for the if/else
clauses
had been wrongly swappped.
suggest 'covr'.
setup for travisCI and Coveralls.
more tests.
corrected \VignetteIndexEntry in the vignette --- I used the vignette for Rdpack as a template but didn't change this entry, which resulted in the vignette appearing with a puzzling title on CRAN and other sites.!
added [email protected] in DESCRIPTION.
added experimental web site (docs/)