Abstract
We give an explicit algorithm and source code for
constructing risk models based on machine learning techniques. The resultant
covariance matrices are not factor models. Based on empirical backtests, we
compare the performance of these machine learning risk models to other
constructions, including statistical risk models, risk models based on
fundamental industry classifications, and also those utilizing multilevel
clustering based industry classifications.