Description Usage Arguments Value Author(s) References Examples
Estimates percentiles of ES distribution function for t-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 5 or 7. In case there 5 input arguments, the mean, standard deviation and assumed sampel size of 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 df Degrees of freedom perc Desired percentile df Number of degrees of freedom in the t distribution cl ES confidence level and must be a scalar hp ES holding period and must be a a scalar |
Percentiles of ES distribution function
Dinesh Acharya
Dowd, K. Measuring Market Risk, Wiley, 2007.
1 2 3 4 5 6 | # Estimates Percentiles of ES distribution given P/L data
data <- runif(5, min = 0, max = .2)
tESDFPerc(returns = data, perc = .7, df = 6, cl = .95, hp = 60)
# Estimates Percentiles of ES distribution given mean, std. deviation and sample size
tESDFPerc(mu = .012, sigma = .03, n= 10, perc = .8, df = 6, cl = .99, hp = 40)
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