Percentiles of ES distribution function for normally distributed geometric returns

Share:

Description

Estimates the percentiles of ES distribution for normally distributed geometric returns, for specified confidence level and holding period using the theory of order statistics.

Usage

1

Arguments

...

The input arguments contain either return data or else mean and standard deviation data. Accordingly, number of input arguments is either 5 or 7. In case there 5 input arguments, the mean, standard deviation and number of samples 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

cl ES confidence level and must be a scalar

hp ES holding period and must be a a scalar

Value

Percentiles of ES distribution function

Author(s)

Dinesh Acharya

References

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

Examples

1
2
3
4
5
6
# Estimates Percentiles of ES distribution
   data <- runif(5, min = 0, max = .2)
   LogNormalESDFPerc(returns = data, investment = 5, perc = .7, cl = .95, hp = 60)

   # Estimates Percentiles given mean, standard deviation and number of sambles of return data
   LogNormalESDFPerc(mu = .012, sigma = .03, n= 10, investment = 5, perc = .8, cl = .99, hp = 40)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.