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Portfolio Optimization

Hierarchical Clustering-Based Risk Parity Portfolio

Compute the asset weights of the hierarchical clustering-based risk parity portfolio, optionally subject to:

Minimum and maximum weights constraints
Minimum and maximum portfolio exposure constraints

References

Machine Learning for Asset Management: New Developments and Financial Applications, Emmanuel Jurczenko, Chapter 9, Harald Lohre,Carsten Rother,Kilian Axel Schäfer, Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations
Thomas Raffinot, Hierarchical Clustering-Based Asset Allocation, The Journal of Portfolio Management Multi-Asset Special Issue 2018, 44 (2) 89-99
Raffinot, Thomas, The Hierarchical Equal Risk Contribution Portfolio
Johann Pfitzinger & Nico Katzke, 2019. A constrained hierarchical risk parity algorithm with cluster-based capital allocation. Working Papers 14/2019, Stellenbosch University, Department of Economics

post/portfolios/optimization/hierarchical-risk-parity/clustering-based

Request body

assetsinteger required

The number of assets

clusteringMethod'singleLinkage' | 'averageLinkage' | 'completeLinkage' | 'wardLinkage'

The hierarchical clustering method to use

clusteringOrdering'r-hclust' | 'optimal'

The order to impose on the hierarchical clustering tree leaves

clustersinteger

The number of clusters to use in the hierarchical clustering tree; if not provided, the number of clusters to use is computed using the gap statistic method, as described in the first reference

acrossClusterAllocationMethod'equalWeighting' | 'inverseVolatility' | 'inverseVariance'

The allocation method to use across clusters

withinClusterAllocationMethod'equalWeighting' | 'inverseVolatility' | 'inverseVariance'

The allocation method to use within clusters

Response

assetsWeightsnumber[] required

assetsWeights[i] is the weight of the asset i in the portfolio, in percentage