NormalVaRDFPerc function

Percentiles of VaR distribution function for normally distributed P/L

Estimates the percentile of VaR distribution function for normally distributed P/L, using the theory of order statistics.

NormalVaRDFPerc(...)

Arguments

• ...: The input arguments contain either return data or else mean and standard deviation data. Accordingly, number of input arguments is either 4 or 6. In case there 4 input arguments, the mean, standard deviation and number of observations of data are computed from returns 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

  perc Desired percentile

  cl VaR confidence level and must be a scalar

  hp VaR holding period and must be a a scalar

Returns

Percentiles of VaR distribution function and is scalar

Examples

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

   # Estimates Percentiles of VaR distribution
   NormalVaRDFPerc(mu = .012, sigma = .03, n= 10, perc = .8, cl = .99, hp = 40)

Author(s)

Dinesh Acharya

References

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