Hierarchical Clustering Portfolios

Dany Cajas
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Dany Cajas: Orenji EIRL

Chapter Chapter 12 in Advanced Portfolio Optimization, 2025, pp 341-364 from Springer

Abstract: Abstract This chapter explains a group of asset allocation algorithms that takes advantage of the hierarchical relationships that can be identified using a special graph called dendrogram. These types of algorithms have become popular since the development of the hierarchical risk parity algorithm, because they combine hierarchical clustering algorithms and asset allocation techniques. The main advantage of these algorithms is that they can incorporate nonconvex risk measures easily. The main disadvantage of these algorithms is that they cannot incorporate real features like linear constraints or short because they are not proper optimization models.

Date: 2025
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DOI: 10.1007/978-3-031-84304-4_12

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