Hierarchical Risk Clustering Portfolio Allocation Strategies

Machine learning hierarchical risk clustering portfolio allocation strategies. The implemented methods are: Hierarchical risk parity (De Prado, 2016) . Hierarchical clustering-based asset allocation (Raffinot, 2017) . Hierarchical equal risk contribution portfolio (Raffinot, 2018) . 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>. Hierarchical Risk Budgeting (Koumou, 2024) .

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")