Extremal dependence is naturally described by the angular law of large multivariate observations. We introduce anchored geodesic component analysis (AGCA), a dimension-reduction method for extremal angular laws on the positive unit sphere. AGCA approximates angular variation by great subspheres constrained to pass through a chosen reference direction, with balanced complete dependence as the default anchor. Under a bounded sine-squared geodesic loss, the population and empirical problems reduce exactly to eigenanalysis of a second-moment matrix of anchored tangent departures. The resulting scores, loadings, residual risks and explained-variation summaries describe departures from the benchmark and remain well defined for face and near-axis extremes. Low-rank AGCA reconstructions also support tail simulation: bounded Lipschitz functionals and homogeneous tail scores, including portfolio capped excesses and value-at-risk, inherit explicit error bounds from the AGCA residual risk. We establish top-\(k\) consistency for oracle and rank-Pareto AGCA summaries and an oracle central limit theorem whose covariance is that of an independent sample from the limiting angular law. In daily equity-portfolio losses, AGCA finds concentrated benchmark-relative tail directions: ten components explain about \(91\%\) of anchored variation and approximate capped-excess and normalized value-at-risk summaries with about \(1.25\%\) average relative error.

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