Forecasting large covariance matrices for portfolio selection is challenging due to high-dimensional data. Different models were tested, and it was found that using latent Wishart processes with shrinkage towards diagonal matrices leads to more stable portfolios with lower turnover and higher returns, even when considering transaction costs. This approach outperformed other models in terms of portfolio optimization.