If I'm given the expected returns, standard deviations, and correlation coefficients how can I compute the covariance matrix C?
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Do you really think that "return" means something for the vast majority of mathematicians who read you ? You must give references! Besides, the principle of this site is to show one's attempts... where you are blocked, etc... – Jean Marie Jun 02 '17 at 20:38
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The covariance matrix of the returns $r_1, r_2, \ldots, r_n$ looks like
$$ \begin{pmatrix} \operatorname{Var}(r_1) & \operatorname{Cov}(r_1,r_2) & \cdots & \operatorname{Cov}(r_1,r_n) \\ \\ \operatorname{Cov}(r_2,r_1) & \operatorname{Var}(r_2) & \cdots & \operatorname{Cov}(r_2,r_n) \\ \\ \vdots & \vdots & \ddots & \vdots \\ \\ \operatorname{Cov}(r_n,r_1) & \operatorname{Cov}(r_n,r_2) & \cdots & \operatorname{Var}(r_n) \end{pmatrix}$$
And the correlation coefficient of $r_i$ and $r_j $ is
$$\large{\rho_{r_i,r_j}}\normalsize =\frac{Cov(r_i,r_j)}{\sqrt{Var(r_i)\cdot Var(r_j)}}, \quad 1\leq i,j \leq n$$
Since you know the variances and correlation coefficients the corresponding covariances can be calculated straightforward.
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