Maximum CVaR Deviation. There are M Linear Loss scenario functions (every Linear Loss scenario function is defined by a Matrix of Scenarios). M new CVaR Deviation functions are calculated (for every Loss scenario function).  Maximum CVaR Deviation is calculated by taking Maximum over M CVaR Deviation  functions.

 

Syntax

max_cvar_dev(α,matrix_1,matrix_2,...,matrix_M)

short call

max_cvar_dev_name(α,matrix_1,matrix_2,...,matrix_M)

call with optional name

 

Parameters

matrix_m        is a Matrix of Scenarios:

       

where the header row contains names of variables (except scenario_probability, and scenario_benchmark). Other rows contain numerical data. The scenario_probability, and scenario_benchmark columns are optional.

 

is a confidence level,

.

 

Mathematical Definition

Maximum CVaR Deviation function is calculated as follows

,

where:

is CVaR Deviation function,

M =  number of random Loss Functions

,

 = vector of random coefficients for m-th Loss Function;

 = j-th scenario of the random vector ,

is an argument of Maximum CVaR Deviation function.

 

Remarks

Data for calculation of Maximum CVaR Deviation are represented by a set of matrices of scenarios which may be in pmatrix form.

 

Example

Calculation in Run-File Environment
Calculation in MATLAB Environment

 

See also

Maximum, Maximum for Gain, Maximum Deviation, Maximum Deviation for Gain, Maximum CVaR , Maximum CVaR for Gain, Maximum CVaR Deviation for Gain, Maximum VaR , Maximum VaR for Gain, Maximum VaR Deviation, Maximum VaR Deviation for Gain