A new multivariate normal mean-variance heterogeneous tails mixture distribution (MNMVHM) is proposed for modeling financial asset returns that captures, along with the obligatory asymmetry and leptokurtosis, different tail behavior among the assets. Its construction allows for joint maximum likelihood estimation of all model parameters via an expectation-maximization algorithm and thus is applicable with a large number of assets. A useful and unique feature of the model is that the tail behavior of the individual assets is driven by asset-specific news effects. In the bivariate iid case, the model corresponds to the standard CAPM model, but enriched with a filter for capturing the news impact associated with both the market and asset excess returns. An empirical application with hourly returns of 22 crypto-currencies and realistic transaction costs shows superior out-of-sample portfolio performance compared to numerous competing models.