Struggling with two-stage stochastic model implementation

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confused
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Struggling with two-stage stochastic model implementation

Post by confused » 4 weeks ago

Hello!

Being new to GAMS I´m struggling with implementation of a simple two stage model, especially the EMP annotation part.

The model is described in the picture:
model.PNG
Variable r-2 - stock returns - is random and I want to approximate it by scenarios. lambda_2 and alpha are constants.

I started with the following code, but I can´t get the EMP part right probably:

Code: Select all

   
$title Two stage asset allocation model

Sets   n       "assets"     / att, gmc, usx /
         j       "scenarios"  / s1*s4 / ;

Table r_scenarios(j,n) "scenario returns from assets"
     att     gmc     usx
 s1  1.300   1.225   1.149
 s2  1.103   1.290   1.260
 s3  1.216   1.216   1.419
 s4  0.954   0.728   0.922 ;

Parameters
      r(n)    "return from assets"
      p(j)    "probability" / #j [1/card(j)] / ;


Scalars   lambda_2      "mean-risk ratio"         / 0.5 /
          alpha         "CVaR level"                / 0.05/ ;


Variables
      Q_1            "objective"
      x_1(n)         "first stage weights"
      x_2(n)         "second stage weights"
      u_1             "first stage CVaR minimizer"
      R_2             "recoursive part"
      Q_2             "second stage objective" ;
      
Positive Variables x_1, x_2;

Equations
      Q_1_equation    "objective function"
      budget_first       "budget constraint for x_1"
      R_2_equation     "R_2 equation"
      Q_2_equation     "second stage objective function"
      budget_second   "budget constraint for x_2"
      take_max           "max(Q_2 - u_1, 0)";

Q_1_equation..    Q_1 =e= - sum(n, x_1(n)) + lambda_2 * u_1  + R_2;
budget_first..       sum(n, x_1(n)) =e= 1;
R_2_equation..    R_2 =e= (1-lambda_2)* Q_2 + (lambda_2 / alpha) * (Q_2 - u_1)
Q_2_equation..    Q_2 =e= - sum(n, x_2(n))
budget_second..  sum(n, x_2(n)) =e= sum(n, x_1(n)*r(n))
take_max..          Q_2 - u_1 =g= 0

Model portfolio / all /;


* EMP Annotations
File emp / '%emp.info%' /;
emp.nd=4;
put emp '* problem %gams.i%' 
    / 'stage 2 ?????'
    / "jrandvar r('att') r('gmc') r('usx')"
loop(j,
  put / p(j) r_scenarios(j,"att") r_scenarios(j,"gmc") r_scenarios(j,"usx"));
putclose emp;   
Is my overall approach right and could you please help me to continue? The next step for me is to extend the model for multiple stages, but I guess it won´t be much different.

Thank you!

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bussieck
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Re: Struggling with two-stage stochastic model implementation

Post by bussieck » 4 weeks ago

Here is a syntactically correct EMP model, but the feasible region is empty and the DE solver returns infeasible. Good luck debugging the model. -Michael

Code: Select all

$title Two stage asset allocation model

Sets   n       "assets"     / att, gmc, usx /
         j       "scenarios"  / s1*s4 / ;

Table r_scenarios(j,n) "scenario returns from assets"
     att     gmc     usx
 s1  1.300   1.225   1.149
 s2  1.103   1.290   1.260
 s3  1.216   1.216   1.419
 s4  0.954   0.728   0.922 ;

Parameters
      r(n)    "return from assets"
      p(j)    "probability" / #j [1/card(j)] / ;


Scalars   lambda_2      "mean-risk ratio"         / 0.5 /
          alpha         "CVaR level"                / 0.05/ ;


Variables
      Q_1            "objective"
      x_1(n)         "first stage weights"
      x_2(n)         "second stage weights"
      u_1             "first stage CVaR minimizer"
      R_2             "recoursive part"
      Q_2             "second stage objective" ;
      
Positive Variables x_1, x_2;

Equations
      Q_1_equation    "objective function"
      budget_first       "budget constraint for x_1"
      R_2_equation     "R_2 equation"
      Q_2_equation     "second stage objective function"
      budget_second   "budget constraint for x_2"
      take_max           "max(Q_2 - u_1, 0)";

Q_1_equation..    Q_1 =e= - sum(n, x_1(n)) + lambda_2 * u_1  + R_2;
budget_first..       sum(n, x_1(n)) =e= 1;
R_2_equation..    R_2 =e= (1-lambda_2)* Q_2 + (lambda_2 / alpha) * (Q_2 - u_1);
Q_2_equation..    Q_2 =e= - sum(n, x_2(n));
budget_second..  sum(n, x_2(n)) =e= sum(n, x_1(n)*r(n));
take_max..          Q_2 - u_1 =g= 0

Model portfolio / all /;


* EMP Annotations
file emp / '%emp.info%' /; emp.nd=4;
put emp '* problem %gams.i%' 
    / 'stage 2 r x_2 R_2 Q_2_equation budget_second take_max R_2_equation'
    / "jrandvar r('att') r('gmc') r('usx')"
loop(j,
  put / p(j) r_scenarios(j,"att") r_scenarios(j,"gmc") r_scenarios(j,"usx"));
putclose emp;

r(n) = sum(j,r_scenarios(j,n))/card(j);

Parameter
    srep(j,*)    scenario attributes / #j.prob 0 /
    j_r(j,n)
    s_x_2(j,n); 

Set dict / j     .scenario.''
           ''    .opt.     srep
           r     .randvar. j_r
           x_2   .level.  s_x_2 /;

solve portfolio min Q_1 us emp scenario dict;

confused
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Re: Struggling with two-stage stochastic model implementation

Post by confused » 3 weeks ago

Thank you very much! You helped me a lot.

Here is slightly changed verison which should work.

Code: Select all

$title Two stage asset allocation model

Sets   n       "assets"     / att, gmc, usx /
       s       "scenarios"  / s1*s12 / ;

Table r_scenarios(s,n) "scenario returns from assets"
     att     gmc     usx
 s1  1.300   1.225   1.149
 s2  1.103   1.290   1.260
 s3  1.216   1.216   1.419
 s4  0.954   0.728   0.922
 s5  0.929   1.144   1.169
 s6  1.056   1.107   0.965
 s7  1.038   1.321   1.133
 s8  1.089   1.305   1.732
 s9  1.090   1.195   1.021
s10  1.083   1.390   1.131
s11  1.035   0.928   1.006
s12  1.176   1.715   1.908;

Parameters
      r(n)    "return from assets"
      p(s)    "probability" / #s [1/card(s)] / ;


Scalars   lambda_2      "mean-risk ratio"         / 0.5 /
          alpha         "CVaR level"              / 0.05/ ;


Variables
      Q_1            "objective"
      x_1(n)         "first stage weights"
      x_2(n)         "second stage weights"
      u_1             "first stage CVaR minimizer"
      R_2             "recoursive part"
      Q_2             "second stage objective"
      non_neg_part   "non-negative part of Q_2 - u_1";
      
Positive Variables x_1, x_2, non_neg_part;

Equations
      Q_1_equation    "objective function"
      budget_first    "budget constraint for x_1"
      R_2_equation    "R_2 equation"
      Q_2_equation    "second stage objective function"
      budget_second   "budget constraint for x_2"
      nnp_greater_than_Q_u     " non_neg_part >= Q_2 - u_1";

Q_1_equation..          Q_1 =e= - sum(n, x_1(n)) + lambda_2 * u_1  + R_2;
budget_first..          sum(n, x_1(n)) =e= 1;
R_2_equation..          R_2 =e= (1-lambda_2)* Q_2 + (lambda_2 / alpha) * non_neg_part;
Q_2_equation..          Q_2 =e= - sum(n, x_2(n));
budget_second..         sum(n, x_2(n)) =e= sum(n, x_1(n)*r(n));
nnp_greater_than_Q_u..   non_neg_part =g= Q_2 - u_1;


Model portfolio / all /;


* EMP Annotations
file emp / '%emp.info%' /; emp.nd=4;
put emp '* problem %gams.i%' 
    / 'stage 2 r x_2 Q_2 R_2 non_neg_part R_2_equation Q_2_equation budget_second nnp_greater_than_Q_u'
    / "jrandvar r('att') r('gmc') r('usx')"
loop(s,
  put / p(s) r_scenarios(s,"att") r_scenarios(s,"gmc") r_scenarios(s,"usx"));
putclose emp;

r(n) = sum(s,r_scenarios(s,n))/card(s);

Parameter
    srep(s,*)    scenario attributes / #s.prob 0 /
    s_r(s,n)
    s_x_2(s,n); 

Set dict / s     .scenario.''
           ''    .opt.     srep
           r     .randvar. s_r
           x_2   .level.  s_x_2 /;

solve portfolio min Q_1 us emp scenario dict;
display srep, s_r, s_x_2, x_1.l;
One thing is not completely right - s_x-2 is displayed only for "usx". The same for x_1.l. How can I display the rest?

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bussieck
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Re: Struggling with two-stage stochastic model implementation

Post by bussieck » 3 weeks ago

Perhaps, the optimization only selects usx for your portfolio. GAMS does not display zeros. You can force some other stocks with some constraints. -Michael

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