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Graphical Bayesian Networks for Predicting Asset Covariances

Covariance matrix prediction is a long-standing challenge in modern portfolio theory and quantitative finance. In this project, we investigate the effectiveness of Bayesian networks in predicting the covariance matrix of financial assets (specifically a subset of the S&P 500), evaluated against Heterogeneous Autoregressive (HAR) models.