VaR Group

Full Name

Brief Name

Short Description

VaR

var_risk

Value-at-Risk for Linear Loss  scenarios, i.e., α%  percentile of Linear Loss scenarios.  

VaR for Gain

var_risk_g

Value-at-Risk for -(Linear Loss ) scenarios, i.e., α%  percentile of -(Linear Loss) scenarios.

VaR Normal Independent

var_risk_ni

Special case of the VaR  when all coefficients in Linear Loss function are independent normally distributed random values.

VaR for Gain Normal Independent

var_risk_ni_g

Special case of the VaR for Gain  when all coefficients in Linear Loss function are independent normally distributed random values.

VaR  Normal Dependent

var_risk_nd

Special case of the VaR when all coefficients in Linear Loss function are mutually dependent  normally distributed random values. 

VaR for Gain Normal Dependent

var_risk_nd_g

Special case of the VaR for Gain when all coefficients in Linear Loss function are mutually dependent  normally distributed random values.

VaR Deviation

var_dev

Value-at-Risk for (Linear Loss ) - (Average over Linear Loss scenarios) , i.e., α%  percentile of (Linear Loss) - (Average over Linear Loss scenarios) scenarios.  

VaR Deviation for Gain

var_dev_g

Value-at-Risk for -(Linear Loss ) + (Average over Linear Loss scenarios) , i.e.,  α%  percentile of  -(Linear Loss) + (Average over Linear Loss scenarios) scenarios.  

VaR Deviation Normal Independent

var_ni_dev

Special case of the VaR Deviation when all coefficients in Linear Loss function are independent normally distributed random values.

VaR Deviation for Gain Normal Independent

var_ni_dev_g

Special case of the VaR Deviation for Gain when all coefficients in -(Linear Loss ) function are independent normally distributed random values

VaR Deviation Normal Dependent

var_nd_dev

Special case of the VaR Deviation when all coefficients in Linear Loss function are mutually dependent normally distributed random values

VaR Deviation for Gain Normal Dependent

var_nd_dev_g

Special case of the VaR Deviation for Gain when all coefficients in -(Linear Loss ) function are mutually dependent normally distributed random values

VaR for Mixture of Normal Independent

avg_var_risk_ni

Consider a mixture of (random) Linear Loss functions with positive weights summing up to one. Coefficients in all  Linear Loss functions are independent normally distributed random values. avg_var_risk_ni is the VaR of the mixture of Normally Independent random values.

VaR for Gain for Mixture of Normal Independent

avg_var_risk_ni_g

Consider a mixture of (random) Linear Loss functions with positive weights summing up to one. Coefficients in all  Linear Loss functions are independent normally distributed random values. avg_var_risk_ni_g is the VaR of the mixture of Normally Independent random values.

VaR Deviation for Mixture of Normal Independent

avg_var_ni_dev

Consider a mixture of (random) Linear Loss functions with positive weights summing up to one. Coefficients in all  Linear Loss functions are independent normally distributed random values. avg_var_ni_dev is the VaR of the mixture of Normally Independent random values.

VaR Deviation for Gain for Mixture of Normal Independent

avg_var_ni_dev_g

Consider a mixture of (random) Linear Loss functions with positive weights summing up to one. Coefficients in all  Linear Loss functions are independent normally distributed random values. avg_var_ni_dev_g is the VaR of the mixture of Normally Independent random values.

VaR  Recourse

var_risk(recourse(.))

VaR of Recourse scenarios. Recourse scenarios are obtained by solving LP at the second stage of two-stage stochastic programming problem for every scenario.

VaR for Gain Recourse

var_risk_g(recourse(.))

VaR of -(Recourse) scenarios.  Recourse  scenarios are obtained by solving LP at the second stage of two-stage stochastic programming problem for every scenario.

VaR Deviation Recourse

var_dev(recourse(.))

VaR of (Deviation Recourse) = (Recourse-(Expected Recourse)) scenarios.  Recourse  scenarios are obtained by solving LP at the second stage of two-stage stochastic programming problem for every scenario.

VaR Deviation for Gain Recourse

var_dev_g(recourse(.))

VaR of -(Deviation Recourse) = (-Recourse+(Expected Recourse)) scenarios.  Recourse  scenarios are obtained by solving LP at the second stage of two-stage stochastic programming problem for every scenario.