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Assuming the random vector $[X \ \ Y]’$ follows a bivariate Gaussian distribution with mean $[\mu_X \ \ \mu_Y]’$, and covariance matrix $ \left[ \begin{array}{cc}

\sigma_X ^2 & \sigma_Y \ \sigma_X \ \rho \\

\sigma_Y \ \sigma_X \ \rho & \sigma_Y^2

\end{array} \right]$, I am looking for the expression of $\text{Cov}(X^2, \exp{Y})$.

I know there exists a formula (Stein’s lemma) for $\text{Cov}(X, \exp{Y})$ and $\text{Cov}(X^2, {Y})$, but I did not manage to find a closed form formula for $\text{Cov}(X^2, \exp{Y})$ given the two transformations.

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- How would you explain why “e” is important? (And when it applies?)

Under the given assumptions, $(X,Y)$ have a bivariate Normal distribution with joint pdf $f(x,y)$:

You seek:

**Notes**

- The
`Cov`

function used above to automate the calculation of the covariance is from the*mathStatica*package for*Mathematica*. As disclosure, I should add that I am one of the authors.

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