CMV: Clustered Minimum Variance

In jpfitzinger/ClusterPortfolios: Portfolio Optimization based on statistical clustering techniques

Clustered Minimum Variance

Description

Computes optimal CMV portfolio with full investment and weight constraints.

Usage

CMV(sigma, UB = NULL, LB = NULL, n_clusters = "auto", ...)

Arguments

sigma	a (N \times N) covariance matrix.
UB	scalar or (N\times 1) vector of upper bound weight constraint.
LB	scalar or (N\times 1) vector of lower bound weight constraint.
n_clusters	trade-off between naive (0) or cluster-based (1) tree-splitting (see Details).
...	arguments passed to cluster::agnes method.

Details

The argument sigma is a covariance matrix.

Hierarchical clustering is performed using the cluster-package. If cluster_method == 'DIANA', the function cluster::diana is used to compute a cluster dendrogram, otherwise the function cluster::agnes(., method = cluster_method) is used. Default is single-linkage agglomerative nesting. The number of clusters can be passed using the n_clusters argument, calculated automatically with n_clusters='auto' using the Silhouette criterion.

CMV calculates between cluster minimum variance portfolios, with funds allocated equally to each cluster constituent. Constraints are implemented using an iterative convergence algorithm.

Value

A (N \times 1) vector of optimal portfolio weights.

Author(s)

Johann Pfitzinger

Examples

# Load returns of assets or portfolios
data("Industry_10")
rets <- Industry_10
sigma <- cov(rets)
CMV(sigma, UB = 0.15, n_clusters = 'auto')

jpfitzinger/ClusterPortfolios documentation built on Sept. 27, 2024, 11:18 p.m.