StdDev of a portfolio

Matrix.java

public class Matrix {
	Double[][] data;

	public Matrix(int m, int n) {
		data = new Double[n][m];
	}

	Matrix(Double[][] data) {
		this.data = data;
	}

	Matrix transpose() {
		if (data == null) {
			System.out.println("invalid Matrix");
		} else if (data.length < 1) {
			System.out.println("Atleast one row should be there");
		} else if (data[0].length == 0) {
			System.out.println("Atleast one column should be there");
		}
		Double[][] temp = new Double[data[0].length][data.length];
		for (int i = 0; i < data.length; i++) {
			for (int j = 0; j < data[0].length; j++) {
				temp[j][i] = data[i][j];
			}
		}
		return new Matrix(temp);
	}

	public Matrix multiply(Matrix x) {
		Double[][] temp = new Double[data.length][x.data.length];
		for (int i = 0; i < data.length; i++) {
			for (int j = 0; j < x.data.length; j++) {
				temp[i][j] = 0.0;
				for (int j2 = 0; j2 < x.data[0].length; j2++) {
					temp[i][j] += data[i][j] * x.data[j][j2];
				}
			}
		}
		return new Matrix(temp);
	}

	@Override
	public String toString() {
		StringBuilder sb = new StringBuilder();
		for (int i = 0; i < data.length; i++) {
			for (int j = 0; j < data[0].length; j++) {
				sb.append(data[i][j] + "\t");
			}
			sb.append("\n");
		}
		return sb.toString();
	}

	public static void main(String[] args) {
		Matrix a = new Matrix(new Double[][] { new Double[] { 0.1, 0.5 } });
		System.out.println(a);
		System.out.println(a.transpose());
		System.out.println(a.transpose().multiply(a));
	}
}

PortfolioAnalysis.java

public class PortfolioAnalysis {
	public static void main(String[] args) {
		for (int i = 0; i <= 25; i++)
			getRetStdDev(i / 25.0);
	}

	private static void getRetStdDev(Double weight1) {
		Double covariance = 0.5;
		Double[] weights = new Double[] { weight1, 1 - weight1 };
		Double[] returns = new Double[] { 0.15, 0.05 };
		Double[] sigmas = new Double[] { 0.15, 0.10 };
		Double[][] covariances = new Double[][] {
				new Double[] { 1.0, covariance },
				new Double[] { covariance, 1.0 } };
		Double pReturn = 0.0;
		for (int i = 0; i < weights.length; i++) {
			pReturn += weights[i] * returns[i];
		}
		Double pVariance = 0.0;
		for (int i = 0; i < weights.length; i++) {
			for (int j = 0; j < weights.length; j++) {
				pVariance += weights[i] * weights[j] * sigmas[i] * sigmas[j]
						* covariances[i][j];
			}
		}
		System.out.println(String.format("%1$3.5f %2$3.5f", pReturn,
				Math.sqrt(pVariance)));
	}
}