Description Usage Arguments Value Author(s) References Examples
Estimates the percentile of VaR distribution function for normally distributed P/L, using the theory of order statistics.
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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 |
Percentiles of VaR distribution function and is scalar
Dinesh Acharya
Dowd, K. Measuring Market Risk, Wiley, 2007.
1 2 3 4 5 6 | # 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)
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Loading required package: bootstrap
Loading required package: MASS
Loading required package: forecast
[1] -5.947847
[1] -0.3781884
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