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Hierarchical PCA and Applications to Portfolio Management

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  • Marco Avellaneda

Abstract

It is widely known that the common risk-factors derived from PCA beyond the first eigenportfolio are generally difficult to interpret and thus to use in practical portfolio management. We explore a alternative approach (HPCA) which makes strong use of the partition of the market into sectors. We show that this approach leads to no loss of information with respect to PCA in the case of equities (constituents of the S&P 500) and also that the associated common factors admit simple interpretations. The model can also be used in markets in which the sectors have asynchronous price information, such as single-name credit default swaps, generalizing the works of Cont and Kan (2011) and Ivanov (2016).

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  • Marco Avellaneda, 2019. "Hierarchical PCA and Applications to Portfolio Management," Papers 1910.02310, arXiv.org.
    • Handle: RePEc:arx:papers:1910.02310
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    File URL: http://arxiv.org/pdf/1910.02310
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    References listed on IDEAS

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    1. Marco Avellaneda & Jeong-Hyun Lee, 2010. "Statistical arbitrage in the US equities market," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 761-782.
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    Cited by:

    1. James, Nick & Menzies, Max & Gottwald, Georg A., 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Jan Rosenzweig, 2020. "Fat Tailed Factors," Papers 2011.13637, arXiv.org, revised Dec 2021.
    3. Nick James & Max Menzies & Georg A. Gottwald, 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Papers 2202.10623, arXiv.org, revised Jun 2022.
    4. Marco Avellaneda & Brian Healy & Andrew Papanicolaou & George Papanicolaou, 2020. "PCA for Implied Volatility Surfaces," Papers 2002.00085, arXiv.org.
    5. Jan Rosenzweig, 2021. "Power-law Portfolios," Papers 2104.07976, arXiv.org, revised Sep 2021.
    6. Marco Avellaneda & Juan Andr'es Serur, 2020. "Hierarchical PCA and Modeling Asset Correlations," Papers 2010.04140, arXiv.org.

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