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Commit 3e02e2f2 authored by Gianluca Frison's avatar Gianluca Frison
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added remaining solutions ex03

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exercises/ex03/solutions/maxreturn.mod 0 → 100644
View file @ 3e02e2f2
reset;
set A; # asset categories
set T := {1973..1994}; # years
#param K := 200; # risk tolerance parameter
param R {T,A};
param mean {j in A}
:= ( sum{i in T} R[i,j] )/card(T);
param Rtilde {i in T, j in A}
:= R[i,j] - mean[j];
param Cov {j in A, k in A}
:= sum {i in T} (Rtilde[i,j]*Rtilde[i,k]) / card(T);
param Corr {j in A, k in A}
:= Cov[j,k]/sqrt(Cov[j,j]*Cov[k,k]);
var x{A} >=0;
#minimize risk:
# K*sum{i in A, j in A} Cov[i,j]*x[i]*x[j] - sum{i in A} mean[i]*x[i];
maximize return:
sum{i in A} mean[i]*x[i];
subject to given_variance:
sum{i in T} ( sum{j in A} Rtilde[i,j]*x[j])^2 / card{T} = 1*0.0007648;
subject to tot_mass:
sum{j in A} x[j] = 1;
data;
set A :=
US_3-MONTH_T-BILLS US_GOVN_LONG_BONDS SP_500 WILSHIRE_5000 NASDAQ_COMPOSITE
LEHMAN_BROTHERS_CORPORATE_BONDS_INDEX EAFE GOLD;
param R:
US_3-MONTH_T-BILLS US_GOVN_LONG_BONDS SP_500 WILSHIRE_5000 NASDAQ_COMPOSITE
LEHMAN_BROTHERS_CORPORATE_BONDS_INDEX EAFE GOLD :=
1973 1.075 0.942 0.852 0.815 0.698 1.023 0.851 1.677
1974 1.084 1.020 0.735 0.716 0.662 1.002 0.768 1.722
1975 1.061 1.056 1.371 1.385 1.318 1.123 1.354 0.760
1976 1.052 1.175 1.236 1.266 1.280 1.156 1.025 0.960
1977 1.055 1.002 0.926 0.974 1.093 1.030 1.181 1.200
1978 1.077 0.982 1.064 1.093 1.146 1.012 1.326 1.295
1979 1.109 0.978 1.184 1.256 1.307 1.023 1.048 2.212
1980 1.127 0.947 1.323 1.337 1.367 1.031 1.226 1.296
1981 1.156 1.003 0.949 0.963 0.990 1.073 0.977 0.688
1982 1.117 1.465 1.215 1.187 1.213 1.311 0.981 1.084
1983 1.092 0.985 1.224 1.235 1.217 1.080 1.237 0.872
1984 1.103 1.159 1.061 1.030 0.903 1.150 1.074 0.825
1985 1.080 1.366 1.316 1.326 1.333 1.213 1.562 1.006
1986 1.063 1.309 1.186 1.161 1.086 1.156 1.694 1.216
1987 1.061 0.925 1.052 1.023 0.959 1.023 1.246 1.244
1988 1.071 1.086 1.165 1.179 1.165 1.076 1.283 0.861
1989 1.087 1.212 1.316 1.292 1.204 1.142 1.105 0.977
1990 1.080 1.054 0.968 0.938 0.830 1.083 0.766 0.922
1991 1.057 1.193 1.304 1.342 1.594 1.161 1.121 0.958
1992 1.036 1.079 1.076 1.090 1.174 1.076 0.878 0.926
1993 1.031 1.217 1.100 1.113 1.162 1.110 1.326 1.146
1994 1.045 0.889 1.012 0.999 0.968 0.965 1.078 0.990
;
#option solver snopt;
option solver ipopt;
#option solver cbc;
#option solver cplex;
#option solver gurobi;
solve;
display Corr;
printf: "-------------------------------------------------------------------\n";
printf: " Asset Mean Variance \n";
printf {j in A}: "%45s %10.7f %10.7f \n",
j, mean[j], sum{i in T} Rtilde[i,j]^2 / card(T);
printf: "\n";
printf: "Optimal Portfolio: Asset Fraction \n";
printf {j in A: x[j] > 0.001}: "%45s %10.7f \n", j, x[j];
printf: "Mean = %10.7f, Variance = %10.7f \n",
sum{j in A} mean[j]*x[j],
sum{i in T} (sum{j in A} Rtilde[i,j]*x[j])^2 / card(T);
exercises/ex03/solutions/portfolio.mod 0 → 100755
View file @ 3e02e2f2
reset;
set A; # asset categories
set T := {1973..1994}; # years
param K := 200; # risk tolerance parameter
param R {T,A};
param mean {j in A}
:= ( sum{i in T} R[i,j] )/card(T);
param Rtilde {i in T, j in A}
:= R[i,j] - mean[j];
param Cov {j in A, k in A}
:= sum {i in T} (Rtilde[i,j]*Rtilde[i,k]) / card(T);
param Corr {j in A, k in A}
:= Cov[j,k]/sqrt(Cov[j,j]*Cov[k,k]);
var x{A} >=0;
minimize risk:
K*sum{i in A, j in A} Cov[i,j]*x[i]*x[j] - sum{i in A} mean[i]*x[i];
subject to tot_mass:
sum{j in A} x[j] = 1;
data;
set A :=
US_3-MONTH_T-BILLS US_GOVN_LONG_BONDS SP_500 WILSHIRE_5000 NASDAQ_COMPOSITE
LEHMAN_BROTHERS_CORPORATE_BONDS_INDEX EAFE GOLD;
param R:
US_3-MONTH_T-BILLS US_GOVN_LONG_BONDS SP_500 WILSHIRE_5000 NASDAQ_COMPOSITE
LEHMAN_BROTHERS_CORPORATE_BONDS_INDEX EAFE GOLD :=
1973 1.075 0.942 0.852 0.815 0.698 1.023 0.851 1.677
1974 1.084 1.020 0.735 0.716 0.662 1.002 0.768 1.722
1975 1.061 1.056 1.371 1.385 1.318 1.123 1.354 0.760
1976 1.052 1.175 1.236 1.266 1.280 1.156 1.025 0.960
1977 1.055 1.002 0.926 0.974 1.093 1.030 1.181 1.200
1978 1.077 0.982 1.064 1.093 1.146 1.012 1.326 1.295
1979 1.109 0.978 1.184 1.256 1.307 1.023 1.048 2.212
1980 1.127 0.947 1.323 1.337 1.367 1.031 1.226 1.296
1981 1.156 1.003 0.949 0.963 0.990 1.073 0.977 0.688
1982 1.117 1.465 1.215 1.187 1.213 1.311 0.981 1.084
1983 1.092 0.985 1.224 1.235 1.217 1.080 1.237 0.872
1984 1.103 1.159 1.061 1.030 0.903 1.150 1.074 0.825
1985 1.080 1.366 1.316 1.326 1.333 1.213 1.562 1.006
1986 1.063 1.309 1.186 1.161 1.086 1.156 1.694 1.216
1987 1.061 0.925 1.052 1.023 0.959 1.023 1.246 1.244
1988 1.071 1.086 1.165 1.179 1.165 1.076 1.283 0.861
1989 1.087 1.212 1.316 1.292 1.204 1.142 1.105 0.977
1990 1.080 1.054 0.968 0.938 0.830 1.083 0.766 0.922
1991 1.057 1.193 1.304 1.342 1.594 1.161 1.121 0.958
1992 1.036 1.079 1.076 1.090 1.174 1.076 0.878 0.926
1993 1.031 1.217 1.100 1.113 1.162 1.110 1.326 1.146
1994 1.045 0.889 1.012 0.999 0.968 0.965 1.078 0.990
;
#option solver snopt;
option solver ipopt;
#option solver cbc;
#option solver cplex;
#option solver gurobi;
solve;
display Corr;
printf: "-------------------------------------------------------------------\n";
printf: " Asset Mean Variance \n";
printf {j in A}: "%45s %10.7f %10.7f \n",
j, mean[j], sum{i in T} Rtilde[i,j]^2 / card(T);
printf: "\n";
printf: "Optimal Portfolio: Asset Fraction \n";
printf {j in A: x[j] > 0.001}: "%45s %10.7f \n", j, x[j];
printf: "Mean = %10.7f, Variance = %10.7f \n",
sum{j in A} mean[j]*x[j],
sum{i in T} (sum{j in A} Rtilde[i,j]*x[j])^2 / card(T);
exercises/ex03/solutions/sol03.txt 0 → 100644
View file @ 3e02e2f2
|------------------|
| Solution of ex03 |
|------------------|
---
3.1
---
See bilevel.mod
Has hinted, Lagrange multipliers have to be added for the constraints on the sign on y considered as inequality constraints.
See bilevel_mpec.mod for the use of complementarity constraints.
---
3.2
---
See knp2.mod, knp3.mod, knp4.mod
In dimensions 2 and 3 e.g. IPOPT makes the job.
In dimensions 4, KNITRO with multistart is needed.
---
3.3
---
See portfolio.mod
---
3.4
---
See maxreturn.mod
---
3.5
---
IP methods in KNITRO work better for this test problem, followed by the AS method.
SQP is much much slower.
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