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Last data update: 2014.03.03

R: Plots normal VaR against confidence level

NormalVaRPlot2DCL

R Documentation

Plots normal VaR against confidence level

Description

Plots the VaR of a portfolio against confidence level assuming that P/L are normally distributed, for specified confidence level and holding period.

Usage

NormalVaRPlot2DCL(...)

Arguments

...

The input arguments contain either return data or else mean and standard deviation data. Accordingly, number of input arguments is either 3 or 4. In case there are 3 input arguments, the mean and standard deviation 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

cl VaR confidence level and must be a vector

hp VaR holding period and must be a scalar

Author(s)

Dinesh Acharya

References

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

Examples

# Plots VaR against confidene level given P/L data
   data <- runif(5, min = 0, max = .2)
   NormalVaRPlot2DCL(returns = data, cl = seq(.85,.99,.01), hp = 60)

   # Computes VaR against confidence level given mean and standard deviation of return data
   NormalVaRPlot2DCL(mu = .012, sigma = .03, cl = seq(.85,.99,.01), hp = 40)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(Dowd)
Loading required package: bootstrap
Loading required package: MASS
Loading required package: forecast
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: timeDate
This is forecast 7.1 

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Dowd/NormalVaRPlot2DCL.Rd_%03d_medium.png", width=480, height=480)
> ### Name: NormalVaRPlot2DCL
> ### Title: Plots normal VaR against confidence level
> ### Aliases: NormalVaRPlot2DCL
> 
> ### ** Examples
> 
> # Plots VaR against confidene level given P/L data
>    data <- runif(5, min = 0, max = .2)
>    NormalVaRPlot2DCL(returns = data, cl = seq(.85,.99,.01), hp = 60)
> 
>    # Computes VaR against confidence level given mean and standard deviation of return data
>    NormalVaRPlot2DCL(mu = .012, sigma = .03, cl = seq(.85,.99,.01), hp = 40)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>