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Description

Hierarchical Risk Clustering Portfolio Allocation Strategies.

Machine learning hierarchical risk clustering portfolio allocation strategies. The implemented methods are: Hierarchical risk parity (De Prado, 2016) <DOI: 10.3905/jpm.2016.42.4.059>. Hierarchical clustering-based asset allocation (Raffinot, 2017) <DOI: 10.3905/jpm.2018.44.2.089>. Hierarchical equal risk contribution portfolio (Raffinot, 2018) <DOI: 10.2139/ssrn.3237540>. A Constrained Hierarchical Risk Parity Algorithm with Cluster-based Capital Allocation (Pfitzingera and Katzke, 2019) <https://www.ekon.sun.ac.za/wpapers/2019/wp142019/wp142019.pdf>.

HierPortfolios

This second release is of this R package is already available on CRAN.

Four hierarchical portfolio allocation strategies are implemented, namely:

  • Hierarchical Risk Parity (De Prado, 2016)
  • Hierarchical Clustering-Based Asset Allocation (Raffinot, 2017)
  • Hierarchical Equal Risk Controbution (Raffinot, 2018)
  • A Constrained Hierarchical Risk Parity Algorithm with Cluster-based Capital Allocation (Pfitzingera and Katzke, 2019)

Each strategy was implemented in an easy-to-use function: HRP_Portfolio, HACC_Portfolio, HERC_Portfolio and DHRP_Portfolio.

References

  • De Prado, M. L. (2016). Building diversified portfolios that outperform out of sample. The Journal of Portfolio Management, 42(4), 59-69.
  • Raffinot, T. (2017). Hierarchical clustering-based asset allocation. The Journal of Portfolio Management, 44(2), 89-99.
  • Raffinot, T. (2018). The hierarchical equal risk contribution portfolio. Available at SSRN 3237540.
  • Pfitzingera, J. and Katzke, N. (2019). A Constrained Hierarchical Risk Parity Algorithm with Cluster-based Capital Allocation. Available at \url{https://www.ekon.sun.ac.za/wpapers/2019/wp142019/wp142019.pdf}

Installation

To install the latest version of this package use the following commands:

install.packages("devtools") devtools::install_github("ctruciosm/HierPorfolios")

Metadata

Version

1.0.1

License

Unknown

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