How to compute the Fourier Transformation of a triple correlation involving three variables

Hi all,

I have three time series measured all simultaneously at a sampling frequency of 10 Hz for 30 minutes. So each of the time series contains 18000 points. Let me call these three time series as A,B and C.

Now I want to compute the triple correlation of these three in the frequency domain via taking the Fourier transform.

So basically I am interested at the Fourier transformation of mean(A'.*B'.*C') where A'=A-mean(A), B'=B-mean(B) and C'=C-mean(C)

Now lets call the individual Fourier transforms of A,B and C as F(A),F(B) and F(C).

If I had required co-variance between A and B, I could have easily computed that by taking Real(F(A).*(conj(F(B))) , where conj is the complex conjugate of F(B).

By extending the same logic, can I also express the triple correlation as defined above like this in the Fourier space

Real(F(A).*(conj(F(B)).*conj(F(C))) ?????

I would be very grateful if the experts here, can help me out with this and provide the solution.