[ojAlgo-user] Markowitz Problem 2 :)
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[ojAlgo-user] Markowitz Problem 2 :)
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From: Matteo B. <mat...@gm...> - 2013-04-04 14:16:30
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Hi
we have a second problem with MarkowitzModel.
I have produced a little source that explain the problem.
The problem is similar with the previous: 3 different TargetReturn 2 ok
and 1 ko-
This is the source that reproduce the problem
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//package org.ojalgo.finance.portfolio;
import java.math.BigDecimal;
import java.util.List;
import org.ojalgo.matrix.BasicMatrix;
import org.ojalgo.matrix.BigMatrix;
import org.ojalgo.matrix.store.BigDenseStore;
import org.ojalgo.matrix.store.MatrixStore;
import org.ojalgo.optimisation.Optimisation.State;
import org.ojalgo.finance.portfolio.MarketEquilibrium;
import org.ojalgo.finance.portfolio.MarkowitzModel;
public class MarkowitzProblems2 {
public static void main(final String[] args) {
//BigDecimal nTarget = new BigDecimal("0.07"); // OK
//0.0401
//0.0
//0.0
//0.0
//0.0239
//0.0
//0.0051
//0.0
//0.5305
//0.1669
//0.0
//0.0335
//0.2
BigDecimal nTarget = new BigDecimal("0.08"); // ERROR
// 0.0828
// 0.0164
// 0.0
// 0.0
// 0.0501
// 0.0
// -0.0396 -> Out of Weight Range
// 0.0
// 0.5876
// 0.1908
// -0.0049 -> Out of Weight Range
// 0.0
// 0.1169
//BigDecimal nTarget = new BigDecimal("0.09"); // OK
//0.0788
//0.0045
//0.0
//0.0
//0.0
//0.0
//0.0711
//0.0
//0.6272
//0.1628
//0.0057
//0.0
//0.05
final double[][] cm=
{{0.04206601, 0.044550845724, 0.048217369200, 0.054350966740,
0.022460320512, 0.055208551370, 0.034165558000, 0.029414658518,
-0.000907567500, -0.003266560017, 0.011408652633, 0.007174996892, 0},
{0.044550845724, 0.06796449, 0.063983606214, 0.072022499274,
0.032393205504, 0.085397746665, 0.046597518000, 0.040498055664,
-0.001203130500, -0.003712871151, 0.016252486404, 0.008933472075, 0},
{0.048217369200, 0.063983606214, 0.09084196, 0.096768635348,
0.043186328064, 0.083969449256, 0.054541344000, 0.049330020454,
-0.001261359000, -0.004770776208, 0.023684614800, 0.016492134802, 0},
{0.054350966740, 0.072022499274, 0.096768635348, 0.11874916,
0.039110390784, 0.091360338216, 0.058037532000, 0.051565647644,
-0.001011401000, -0.004914519792, 0.024900113692, 0.014709412962, 0},
{0.022460320512, 0.032393205504, 0.043186328064, 0.039110390784,
0.05308416, 0.064832995584, 0.040674816000, 0.041904582912,
-0.001357056000, -0.003786856704, 0.018876923136, 0.017478379008, 0},
{0.055208551370, 0.085397746665, 0.083969449256, 0.091360338216,
0.064832995584, 0.14661241, 0.060023404000, 0.054428675966,
-0.000761971000, -0.004890659172, 0.020088021436, 0.009159105844, 0},
{0.034165558000, 0.046597518000, 0.054541344000, 0.058037532000,
0.040674816000, 0.060023404000, 0.04000000, 0.030711460000,
-0.001110000000, -0.003517058000, 0.013415070000, 0.007178534000, 0},
{0.029414658518, 0.040498055664, 0.049330020454, 0.051565647644,
0.041904582912, 0.054428675966, 0.030711460000, 0.08300161, 0.000404780500,
-0.001591573878, 0.020639653979, 0.010983694379, 0},
{-0.000907567500, -0.001203130500, -0.001261359000,
-0.001011401000, -0.001357056000, -0.000761971000,
-0.001110000000, 0.000404780500, 0.00250000, 0.002084702500,
0.000854111500, 0.000385734000, 0},
{-0.003266560017, -0.003712871151, -0.004770776208,
-0.004914519792, -0.003786856704, -0.004890659172,
-0.003517058000, -0.001591573878, 0.002084702500, 0.00426409,
0.000608437974, 0.001034468234, 0},
{0.011408652633, 0.016252486404, 0.023684614800, 0.024900113692,
0.018876923136, 0.020088021436, 0.013415070000, 0.020639653979,
0.000854111500, 0.000608437974, 0.03500641, 0.003756060565, 0},
{0.007174996892, 0.008933472075, 0.016492134802, 0.014709412962,
0.017478379008, 0.009159105844, 0.007178534000, 0.010983694379,
0.000385734000, 0.001034468234, 0.003756060565, 0.01471369, 0},
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}
;
final int assetNum = cm.length;
final int row = cm.length;
if (row > 0) {
final int col = cm[0].length;
final MatrixStore<BigDecimal> a =
BigDenseStore.FACTORY.makeZero(row, col);
BasicMatrix covariances = BigMatrix.FACTORY.copy(a);
for (int i = 0; i < row; i++) {
for (int j = 0; j < col; j++) {
covariances = covariances.replace(i, j, cm[i][j]);
}
}
final double nRisk = 1000.0;
final BigDecimal riskAversion = new BigDecimal(nRisk);
final MarketEquilibrium marketEquilibrium = new
MarketEquilibrium(covariances, riskAversion);
final MatrixStore<BigDecimal> ret_matrix =
BigDenseStore.FACTORY.makeZero(assetNum, 1);
BasicMatrix expectedExcessReturns1 =
BigMatrix.FACTORY.copy(ret_matrix);
expectedExcessReturns1 = expectedExcessReturns1.replace(0, 0,
0.1389);
expectedExcessReturns1 = expectedExcessReturns1.replace(1, 0,
0.1545);
expectedExcessReturns1 = expectedExcessReturns1.replace(2, 0,
0.0736);
expectedExcessReturns1 = expectedExcessReturns1.replace(3, 0,
0.0648);
expectedExcessReturns1 = expectedExcessReturns1.replace(4, 0,
0.0915);
expectedExcessReturns1 = expectedExcessReturns1.replace(5, 0,
0.1802);
expectedExcessReturns1 = expectedExcessReturns1.replace(6, 0,
0.1331);
expectedExcessReturns1 = expectedExcessReturns1.replace(7, 0,
0.0816);
expectedExcessReturns1 = expectedExcessReturns1.replace(8, 0,
0.0874);
expectedExcessReturns1 = expectedExcessReturns1.replace(9, 0,
0.0829);
expectedExcessReturns1 = expectedExcessReturns1.replace(10, 0,
0.1038);
expectedExcessReturns1 = expectedExcessReturns1.replace(11, 0,
0.0759);
expectedExcessReturns1 = expectedExcessReturns1.replace(12, 0,
0.0000);
final MarkowitzModel markowitzModel = new
MarkowitzModel(marketEquilibrium, expectedExcessReturns1);
markowitzModel.setLowerLimit(0, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(0, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(1, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(1, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(2, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(2, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(3, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(3, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(4, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(4, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(5, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(5, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(6, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(6, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(7, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(7, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(8, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(8, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(9, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(9, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(10, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(10, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(11, new BigDecimal(0.0000));
markowitzModel.setUpperLimit(11, new BigDecimal(1.0000));
markowitzModel.setLowerLimit(12, new BigDecimal(0.0500));
markowitzModel.setUpperLimit(12, new BigDecimal(0.2000));
final boolean bShort = false;
markowitzModel.setShortingAllowed(bShort);
markowitzModel.setTargetReturn(nTarget);
final List<BigDecimal> re = markowitzModel.getWeights();
final State tmpOptState = markowitzModel.getOptimisationState();
for (int nn = 0; nn < re.size(); nn++) {
System.out.println(re.get(nn));
}
}
}
}
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All the best
Matteo Baccan
--
Matteo Baccan
http://www.baccan.it
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