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Hierarchical Clustering Portfolios

In: Advanced Portfolio Optimization

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  • Dany Cajas

    (Orenji EIRL)

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.

Suggested Citation

  • Dany Cajas, 2025. "Hierarchical Clustering Portfolios," Springer Books, in: Advanced Portfolio Optimization, chapter 0, pages 341-364, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-84304-4_12
    DOI: 10.1007/978-3-031-84304-4_12
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