Description
Hierarchical Risk Clustering Portfolio Allocation Strategies.
Description
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>.
README.md
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")