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Lohre, H., Rother, C. and Schäfer, K.A. (2020) Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations. In: Jurczenko, E., Ed., Machine Learning and Asset Management: New Developments and Financial Applications, John Wiley & Sons, New Jersey, 329-368.
https://doi.org/10.1002/9781119751182.ch9

has been cited by the following article:

• TITLE: Evaluating Hierarchical Equal Risk Contribution Portfolios in the Chinese Stock Market

  AUTHORS: Weige Huang, Xiang Gao

  KEYWORDS: Hierarchical Equal Risk Contribution, Machine Learning, Hierarchical Risk Parity, Asset Allocation, Critical Line Algorithm, Inverse-Variance Portfolio

  JOURNAL NAME: Journal of Mathematical Finance, Vol.12 No.1, February 21, 2022

  ABSTRACT: This paper investigates the usefulness of the Hierarchical Equal Risk Contribution algorithm to exploit correlation structure in China’s equity market over 2001-2020. By running a horse race of different combinations of metrics and linkages, we demonstrate that the winner strategy always beats traditional portfolio construction techniques. Better-performing risk-based hierarchy strategies vary with stock-sorting methods by size, mean return, volatility, and Sharpe ratio. However, our treatment results in extremely imbalanced asset allocation, implying that we capture information other than the standard Chinese industrial classification.