We propose a novel approach combining vector autoregressive models and data assimilation to conduct econometric inference for high dimensional problems in cryptocurrency markets. We label this new model TVP-VAR-DA. As the resulting algorithm is computationally very expensive, it mandates the introduction of a problem decomposition and its implementation in a parallel computing environment. We study its scalability and prediction accuracy under various specifications.
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