VaR: Value at Risk
In OCA: Optimal Capital Allocations

VaR	R Documentation

Value at Risk

Description

\loadmathjax

Analytical approach for calculating VaR based on Variance-Covariance Method based on both normal and t-student distribution.

Usage

VaR(
  variance,
  alpha = 0.95,
  weights = NULL,
  model = c("normal", "t-student", "both"),
  df = NULL,
  percentage = FALSE
)

Arguments

`variance`	It could be either a scalar or a matrix containing the variances and covariances of the losses. Provide a covariance matrix when analyzing correlated losses or a scalar when dealing with a single loss.
`alpha`	The confidence level at which either the VaR will be computed, by default `alpha` is set to 0.95.
`weights`	A vector of weights of size N for weighting the variance of losses. When `weights=NULL`, variances used to compute VaR are the original values supplied to `variance` with no weighting scheme.
`model`	A character string indicating which probability model has to be used for computing the risk measures, it could be a normal distribution or a t-student distribution with \mjteqnvv degrees of freedom. The normal distibution is the default model for this funcion. `model` also allows the user to set `'both'` if she wishes both normal and t-student VaR or ES depending on what she choses in `measure`. See example below.
`df`	An integer (df>2) denoting the degrees of freedom, only required if `model='t-student'`. Otherwise it has to be `NULL`.
`percentage`	Logical indicating whether the file names in the VaR table should be presented in percentage or decimal.

Value

A data.frame containing the VaR at its corresponding confidence level.

Author(s)

Jilber Urbina

References

Dhaene J., Tsanakas A., Valdez E. and Vanduffel S. (2011). Optimal Capital Allocation Principles. The Journal of Risk and Insurance. Vol. 00, No. 0, 1-28.

Urbina, J. (2013) Quantifying Optimal Capital Allocation Principles based on Risk Measures. Master Thesis, Universitat Politècnica de Catalunya.

Urbina, J. and Guillén, M. (2014). An application of capital allocation principles to operational risk and the cost of fraud. Expert Systems with Applications. 41(16):7023-7031.

Examples


# Reproducing VaR from Table 2.1 in page 47 of
# McNeal A., Frey R. and Embrechts P (2005).

alpha <- c(.90, .95, .975, .99, .995)
VaR(variance=(10000*0.2/sqrt(250))^2, alpha=alpha, model='both', df=4)

# only normal VaR results
VaR(variance=(10000*0.2/sqrt(250))^2, alpha=alpha)

OCA documentation built on Feb. 16, 2023, 8:27 p.m.