Building Diversified Portfolios that Outperform Out-of-Sample
Posted: 20 May 2019
Date Written: May 23, 2016
This paper introduces the Hierarchical Risk Parity (HRP) approach. HRP portfolios address three major concerns of quadratic optimizers in general and Markowitz’s 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-of-sample 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.
A presentation can be found at http://ssrn.com/abstract=2713516.
Keywords: Risk parity, tree graph, cluster, dendogram, linkage, metric space
JEL Classification: G0, G1, G2, G15, G24, E44
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