Julia library for computing risk measures for random variables. The random variable represents profits or rewards that are to be maximized. The computed risk value is also better when greater.
The following risk measures are currently supported
- VaR: Value at risk
- CVaR: Conditional value at risk
- ERM: Entropic risk measure
- EVaR: Entropic value at risk
The focus is currently on random variables with categorical (discrete) probability distributions, but continuous probabilty distributions may be supported in the future too.
In general, the smaller value of the risk parameter indicates that the risk measure is less risk-averse or that it is closer to the expectation operator.
Warning: This is package is in development and the computed values should be treated with caution.
using RiskMeasures
X = [1, 5, 6, 7, 20]
p = [0.1, 0.1, 0.2, 0.5, 0.1]
var(X, p, 0.1) # value at risk
cvar(X, p, 0.1) # conditional value at risk
evar(X, p, 0.1) # entropic value at risk
erm(X, p, 0.1) # entropic risk measure