In this talk we discuss an alternative method for the spectral analysis of a strictly stationary time series. We define a “new” spectrum as the Fourier transform of the differences between copulas of the pairs with lag k and the independence copula. This object is called copula spectral density kernel and allows separating marginal and serial aspects of a time series. The copula spectral density kernel is substantially more informative than the “classical” spectral density obtained from the auto-covariances. In particular, it provides a complete description of the distributions of all pairs with arbitrary lag. We introduce a way to estimate of copula spectral density kernels, comment on the asymptotic properties of the proposed estimator, and discuss several possible extensions.