# Chapter 4. Risk Measures (in Python/Julia)

Copyright 2011 - 2022 Jon Danielsson. This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. The GNU General Public License is available at: https://www.gnu.org/licenses/.

##### Listing 4.1/4.2: ES in Python Last updated July 2020

from scipy import stats
p = [0.5, 0.1, 0.05, 0.025, 0.01, 0.001]
VaR = -stats.norm.ppf(p)
ES = stats.norm.pdf(stats.norm.ppf(p))/p
for i in range(len(p)):
print("VaR " + str(round(p[i]*100,3)) + "%: " + str(round(VaR[i],3)))
print("ES " + str(round(p[i]*100,3)) + "%: " + str(round(ES[i],3)), "\n")

##### Listing 4.1/4.2: ES in Julia Last updated June 2018

using Distributions;
p = [0.5, 0.1, 0.05, 0.025, 0.01, 0.001]
VaR = quantile.(Normal(0,1), p)
ES = pdf.(Normal(0,1), quantile.(Normal(0,1),p))./p
println(ES)