HERC: Design of hierarchical equal risk contribution portfolios

In jackylauu/hierarchicalPortfolios: Design of Hierarchical Clustering-Based Portfolios

Description

This function designs hierarchical equal risk contribution portfolios based on the method proposed by Raffinot (2018).

Usage

HERC(
  asset_prices = NULL,
  asset_returns = NULL,
  Sigma = NULL,
  risk_measure = c("variance", "standard-deviation", "equal-weighting", "CVaR", "CDaR"),
  method = "ward.D2",
  num_clusters = NULL
)

Arguments

asset_prices	An XTS object of the asset prices.
asset_returns	An XTS object of the asset returns.
Sigma	Covariance matrix of returns. If none is provided, the covariance matrix will be computed from the returns.
risk_measure	String indicating the desired risk measure for assigning portfolio weights. Must be one of c('variance', 'standard-deviation', 'equal-weighting', 'CVaR', 'CDaR')
method	String indicating the desired hierarchical clustering method. Must be one of c("single", "complete", "average" ,"ward.D", "ward.D2", "divisive"). If method="divisive", divisive clustering (or the DIANA algorithm)is used, otherwise agglomerative clustering is used with method referring to the desired linkage function.
num_clusters	Integer value representing the optimal number of clusters. If no value is given, the optimal number of clusters will be computed automatically.

Details

This portfolio allocation method makes use of hierarchical clustering to assign portfolio weights.

jackylauu/hierarchicalPortfolios documentation built on Dec. 20, 2021, 8:06 p.m.