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The thesis studies the problem of predicting large covariance matrices, a central challenge in modern portfolio theory. In portfolios with many assets, the large number of covariance terms makes prediction difficult, often causing traditional methods to suffer from either substantial estimation bias or variance. To address this, the thesis proposes an approach that adapts conditional autoencoders,
