Combining forecasts have been proven as one of the most successful methods to improve predictive performance. However, while there often is a focus on theoretically optimal methods, this is an ill-posed issue in practice where the problem of robustness is of more empirical relevance. This thesis focuses on the latter issue, where the risk associated with different combination methods is examined. The problem is addressed using Monte Carlo experiments and an application to automated forecasting with data from the M4 competition. Overall, our results indicate that the choice of combining methodology could constitute an important source of risk. While equal weighting of forecasts generally works well in the application, there are also cases where estimating weights improve upon this benchmark. In these cases, many robust and simple alternatives perform the best. While estimating weights can be beneficial, it is important to acknowledge the role of estimation uncertainty as it could outweigh the benefits of combining. For this reason, it could be advantageous to consider methods that effectively acknowledge this source of risk. By doing so, a forecaster can effectively utilize the benefits of combining forecasts while avoiding the risk associated with uncertainty in weights.