LogtVaRDFPerc function

Percentiles of VaR distribution function for Student-t

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

LogtVaRDFPerc(...)

Arguments

• ...: The input arguments contain either return data or else mean and standard deviation data. Accordingly, number of input arguments is either 6 or 8. In case there 6 input arguments, the mean, standard deviation and number of observations of the 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

  n Sample size

  investment Size of investment

  perc Desired percentile

  df Number of degrees of freedom in the t distribution

  cl VaR confidence level and must be a scalar

  hp VaR holding period and must be a a scalar

  Percentiles of VaR distribution function

Examples

# Estimates Percentiles of VaR distribution
   data <- runif(5, min = 0, max = .2)
   LogtVaRDFPerc(returns = data, investment = 5, perc = .7,
                 df = 6, cl = .95, hp = 60)

   # Computes v given mean and standard deviation of return data
   LogtVaRDFPerc(mu = .012, sigma = .03, n= 10, investment = 5,
                 perc = .8, df = 6, cl = .99, hp = 40)

Author(s)

Dinesh Acharya

References

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