Plots the VaR of a portfolio against confidence level assuming that geometric returns are Student t distributed, for specified confidence level and holding period.
The input arguments contain either return data or else mean and standard deviation data. Accordingly, number of input arguments is either 6 or 8. In case there 6 input arguments, the mean, standard deviation and number of observations of the 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
investment Size of investment
perc Desired percentile
df Number of degrees of freedom in the t distribution
cl VaR confidence level and must be a scalar
hp VaR holding period and must be a a scalar
Percentiles of VaR distribution function
Dowd, K. Measuring Market Risk, Wiley, 2007.
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# Estimates Percentiles of VaR distribution data <- runif(5, min = 0, max = .2) LogtVaRDFPerc(returns = data, investment = 5, perc = .7, df = 6, cl = .95, hp = 60) # Computes v given mean and standard deviation of return data LogtVaRDFPerc(mu = .012, sigma = .03, n= 10, investment = 5, perc = .8, df = 6, cl = .99, hp = 40)
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