How to generate random correlation matrix that has approximately normally distributed off-diagonal entries with given standard deviation? I would like to generate a random correlation matrix such that the distribution of its off-diagonal elements looks approximately like normal. How can I do it?
The motivation is this. For a set of $n$ time series data, the correlation distribution often looks quite close to normal. I would like to generate many "normal" correlation matrices to represent the general situation and use them to calculate risk number.

I know one method, but the resulting standard deviation (of the distribution of the off-diagonal elements) is too small for my purpose: generate $n$ uniform or normal random rows of a matrix $\mathbf X$, standardize the rows (subtract the mean, divide by standard deviation), then the sample correlation matrix $\frac{1}{n-1}\mathbf X \mathbf X^\top$ has normally distributed off-diagonal entries [Update after comments: standard deviation will be $\sim n^{-1/2}$].
Can anyone suggest a better method with which I can control the standard deviation?
 A: I have first provided what I now believe is a sub-optimal answer; therefore I edited my answer to start with a better suggestion.

Using vine method
In this thread: How to efficiently generate random positive-semidefinite correlation matrices? -- I described and provided the code for two efficient algorithms of generating random correlation matrices. Both come from a paper by Lewandowski, Kurowicka, and Joe (2009).
Please see my answer there for a lot of figures and matlab code. Here I would only like to say that the vine method allows to generate random correlation matrices with any distribution of partial correlations (note the word "partial") and can be used to generate correlation matrices with large off-diagonal values. Here is the relevant figure from that thread:

The only thing that changes between subplots, is one parameter that controls how much the distribution of partial correlations is concentrated around $\pm 1$. As OP was asking for an approximately normal distribution off-diagonal, here is the plot with histograms of the off-diagonal elements (for the same matrices as above):

I think this distributions are reasonably "normal", and one can see how the standard deviation gradually increases. I should add that the algorithm is very fast. See linked thread for the details.

My original answer
A straight-forward modification of your method might do the trick (depending on how close you want the distribution to be to normal). This answer was inspired by @cardinal's comments above and by @psarka's answer to my own question How to generate a large full-rank random correlation matrix with some strong correlations present?
The trick is to make samples of your $\mathbf X$ correlated (not features, but samples). Here is an example: I generate random matrix $\mathbf X$ of $1000 \times 100$ size (all elements from standard normal), and then add a random number from $[-a/2, a/2]$ to each row, for $a=0,1,2,5$. For $a=0$ the correlation matrix $\mathbf X^\top \mathbf X$ (after standardizing the features) will have off-diagonal elements approximately normally distributed with standard deviation $1/\sqrt{1000}$. For $a>0$, I compute correlation matrix without centering the variables (this preserves the inserted correlations), and the standard deviation of the off-diagonal elements grow with $a$ as shown on this figure (rows correspond to $a=0,1,2,5$):

All these matrices are of course positive definite. Here is the matlab code:
offsets = [0 1 2 5];
n = 1000;
p = 100;

rng(42) %// random seed

figure
for offset = 1:length(offsets)
    X = randn(n,p);
    for i=1:p
        X(:,i) = X(:,i) + (rand-0.5) * offsets(offset);
    end
    C = 1/(n-1)*transpose(X)*X; %// covariance matrix (non-centred!)

    %// convert to correlation
    d = diag(C);
    C = diag(1./sqrt(d))*C*diag(1./sqrt(d));

    %// displaying C
    subplot(length(offsets),3,(offset-1)*3+1)
    imagesc(C, [-1 1])

    %// histogram of the off-diagonal elements
    subplot(length(offsets),3,(offset-1)*3+2)
    offd = C(logical(ones(size(C))-eye(size(C))));
    hist(offd)
    xlim([-1 1])

    %// QQ-plot to check the normality
    subplot(length(offsets),3,(offset-1)*3+3)
    qqplot(offd)

    %// eigenvalues
    eigv = eig(C);
    display([num2str(min(eigv),2) ' ... ' num2str(max(eigv),2)])
end

The output of this code (minimum and maximum eigenvalues) is:
0.51 ... 1.7
0.44 ... 8.6
0.32 ... 22
0.1 ... 48

A: You might be interested in some of the code at the following link:
Correlation and Co-integration
A: If you are trying to generate random correlation matrices, consider sampling from the Wishart distribution. This following question provides information the Wishart distribution as well as advice on how to sample:
How to efficiently generate random positive-semidefinite correlation matrices?
A: This is not a very sophisticated answer, but I can't help but think it's still a good answer...
If your motivation is that correlation parameters produced by time series data tend to look normal, why not just simulate time series data, calculate the correlation parameters and use those?
You may have a good reason for not doing this, but it's not clear to me from your question. 
