HCAA_Portfolio: Hierarchical Clustering-Based Asset Allocation

In HierPorfolios: Hierarchical Clustering-Based Portfolio Allocation Strategies

Description

Performs the Hierarchical Clustering-Based Asset Allocation strategy proposed by Raffinot (2017). Several linkage methods for the hierarchical clustering can be used, by default the "ward" linkage is used. The numbers of clusters is selected using the Gap index of Tibshirani et al. (2001).

Usage

HCAA_Portfolio(covar, linkage = "ward", graph = FALSE, clusters = NULL)

Arguments

covar	Covariance matrix of returns. The covariance matrix will be transformed into correlation matrix and then into a distance matrix.
linkage	Linkage method used in the hierarchical clustering. Allowed options are "single", "complete", "average" or "ward". Default option is "ward".
graph	To plot de dendrogram set this value to TRUE. By default this value is equal to FALSE.
clusters	Numbers of clusters. If NULL (default), the gap index is applied.

Value

portfolio weights.

References

Raffinot, Thomas. "Hierarchical clustering-based asset allocation." The Journal of Portfolio Management 44.2 (2017): 89-99.

Tibshirani, Robert, Guenther Walther, and Trevor Hastie. "Estimating the number of clusters in a data set via the gap statistic." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63.2 (2001): 411-423.

See Also

HRP_porfolio

Examples

covar <- cov(daily_returns)
HCAA_Portfolio(covar)

HierPorfolios documentation built on Nov. 8, 2021, 5:09 p.m.