NormalVaRDFPerc: Percentiles of VaR distribution function for normally...

In Dowd: Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's Book Measuring Market Risk

Description

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

Usage

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

Value

Percentiles of VaR distribution function and is scalar

Author(s)

Dinesh Acharya

References

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

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)

Example output

Loading required package: bootstrap
Loading required package: MASS
Loading required package: forecast
[1] -5.947847
[1] -0.3781884

Dowd documentation built on May 2, 2019, 10:16 a.m.