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.