In the Elements of Statistical Learning book at page 588 the author states that the average of $B$ i.i.d. random variables, each with variance $\sigma^2$, has variance $\frac{1}{B} \sigma^2$.
If the variables are simply i.d. (identically distributed, but not necessarily independent) with positive pairwise correlation $\rho$, the variance of the average is
$$\rho \sigma^2 + \frac{1 - \rho}{B} \sigma^2.$$
I don't understand the calculation of the second case.
Let $X_1, \dots, X_B$ be the corresponding random variables, and let $$\bar{X}_B = \dfrac{1}{B}\sum_{i=1}^{B}X_i$$ be their average.
Then
$$\text{Var}(\bar{X}_B) = \dfrac{1}{B^2}\text{Var}\left(\sum_{i=1}^{B}X_i\right) = \dfrac{1}{B^2}\sum_{i=1}^{B}\sum_{j=1}^{B}\text{Cov}(X_i, X_j)$$ Suppose, in the above summation, that $i = j$. Then $\text{Cov}(X_i, X_j) = \sigma^2$. Exactly $B$ of these occur.
Suppose, in the above summation, that $i \neq j$. Then $\text{Cov}(X_i, X_j) = \rho\sigma^2$ since the variances are identical. There are $B^2 - B = B(B-1)$ of these occurrences. (Notice that there are $B * B = B^2$ total terms in the summmation, so $B^2 - B$ is the number of terms that aren't equal to $\sigma^2$, as above.)
Hence, $$\sum_{i=1}^{B}\sum_{j=1}^{B}\text{Cov}(X_i, X_j) = B\sigma^2+B(B-1)\rho\sigma^2$$ from which we obtain $$\text{Var}(\bar{X}_B) = \dfrac{1}{B^2}\left(B\sigma^2+B(B-1)\rho\sigma^2\right) = \dfrac{\sigma^2}{B}+\dfrac{B-1}{B}\rho\sigma^2 = \dfrac{\sigma^2}{B}+\rho\sigma^2-\dfrac{1}{B}\rho\sigma^2$$ or $$\rho\sigma^2 +\dfrac{\sigma^2}{B}(1-\rho)$$ as desired.
As discussed in the comments, nothing in the demonstration of Clarinetist states that the correlation has to be positive. Nevertheless, it cannot be negative otherwise, the covariance matrix would not be semi-positive definite.
You can suspect that an assumption would be incorrect because when B goes to infinity, Var(X¯B) is negative if rho is negative as well.
