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This article focuses on estimating relative transfer functions (RTFs) for beamforming applications. While traditional methods assume that spectra are uncorrelated, this assumption is often violated in practical scenarios due to natural phenomena such as the Doppler effect, artificial manipulations like time-domain windowing, or the non-stationary nature of the signals, as observed in speech. To address this, we propose an RTF estimation technique that leverages spectral and spatial correlations through subspace analysis. To overcome the challenge of estimating second-order spectral statistics for real data, we employ a phase-adjusted estimator originally proposed in the context of engine fault detection. Additionally, we derive Cram\'er--Rao bounds (CRBs) for the RTF estimation task, providing theoretical insights into the achievable estimation accuracy. The bounds show that channel estimation can be performed more accurately if the noise or the target presents spectral correlations. Experiments on real and synthetic data show that our technique outperforms the narrowband maximum-likelihood estimator when the target exhibits spectral correlations. Although the accuracy of the proposed algorithm is generally close to the bound, there is some room for improvement, especially when noise signals with high spectral correlation are present. While the applications of channel estimation are diverse, we demonstrate the method in the context of array processing for speech., Comment: Under review at IEEE/ACM Transactions on Audio, Speech, and Language Processing