Building Diversified Portfolios that Outperform Out of Sample
In this article, the author introduces the Hierarchical Risk Parity (HRP) approach to address three major concerns of quadratic optimizers, in general, and Markowitz's critical line algorithm (CLA) in particular: instability, concentration, and underperformance. HRP applies modern mathematics (graph theory and machine learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill degenerated or even a singular covariance matrix an impossible feat for quadratic optimizers. Monte Carlo experiments show that HRP delivers lower out ofsample variance than CLA, even though minimum variance is CLA's optimization objective. HRP also produces less risky portfolios out of sample compared to traditional risk parity methods.