LogtVaR function

VaR for t distributed geometric returns

Estimates the VaR of a portfolio assuming that geometric returns are Student t distributed, for specified confidence level and holding period.

LogtVaR(...)

Arguments

• ...: The input arguments contain either return data or else mean and standard deviation data. Accordingly, number of input arguments is either 5 or 6. In case there 5 input arguments, the mean and standard deviation of data is computed from return data. See examples for details.

  returns Vector of daily geometric return data

  mu Mean of daily geometric return data

  sigma Standard deviation of daily geometric return data

  investment Size of investment

  df Number of degrees of freedom in the t distribution

  cl VaR confidence level

  hp VaR holding period

Returns

Matrix of VaRs whose dimension depends on dimension of hp and cl. If cl and hp are both scalars, the matrix is 1 by 1. If cl is a vector and hp is a scalar, the matrix is row matrix, if cl is a scalar and hp is a vector, the matrix is column matrix and if both cl and hp are vectors, the matrix has dimension length of cl * length of hp.

Examples

# Computes VaR given geometric return data
   data <- runif(5, min = 0, max = .2)
   LogtVaR(returns = data, investment = 5, df = 6, cl = .95, hp = 90)

   # Computes VaR given mean and standard deviation of return data
   LogtVaR(mu = .012, sigma = .03, investment = 5, df = 6, cl = .95, hp = 90)

Author(s)

Dinesh Acharya

References

Dowd, K. Measuring Market Risk, Wiley, 2007.