This is an expression for the computation of kurtosis.
For a stationary process $x$, moments and cumulants, when they exist, depend on relative time lags, hence $n-1$ variables when the moment or cumulant is of order $n$ (see for instance Basic Signal Processing and Statistics, p. 446 sq.):

$ \mathcal{M} \{ x(k),x(k+\tau_1),x(k+\tau_2),x(k+\tau_3) \} = \mathrm{E} \{ x(k)x(k+\tau_1)x(k+\tau_2)x(k+\tau_3) \}\,. $

A standard notation is $m_{4x}(\tau_1,\tau_2,\tau_3)$, where $x$ denotes the  process, $4$ the $n$-th order, $\tau_1,\tau_2,\tau_3$ the lags, with a triple $0$ for the $0$-lag cumulants. The first $0$-lag cumulants are classicaly named variance, skewness and kurtosis. Your version seems a normalized kurtosis, which could be written, with the above notations:
$$ \kappa_{4x}(0,0,0) = \frac{m_{4x}(0,0,0)}{m_{2x}^2(0)}\,. $$ 
I am quite surprised thant you only have two zeroes in your parenthesis.

On the  origin side, kurtosis comes for a greek word meaning arched, curved, measuring the tailedness of a distribution.