The researchers found that the maximum likelihood estimator used in dynamic conditional correlation models can be biased in high dimensions, especially when the cross-section dimension is close to the sample size. They suggest reducing this bias by using shrinkage to target methods for the sample covariance matrix. By doing this, they were able to improve the estimator's performance compared to other methods in a Monte Carlo study and when applied to financial time series data.