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Ultrametricity in fund of funds diversification

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  • Miceli, M.A.
  • Susinno, G.

Abstract

Minimum market transparency requirements impose hedge fund (HF) managers to use the statement declared strategy in practice. However, each declared strategy may actually generate a multiplicity of implemented management decisions. Is then the “actual ” strategy the same as the “announced” strategy? Can the actual strategy be monitored or compared to the actual strategy of HF belonging to the same “announced” class? Can the announced or actual strategy be used as a quantitative argument in the fund of funds policy? With the appropriate metric, it is possible to draw a minimum spanning tree (MST) to emphasize the similarity structure that could be hidden in the raw correlation matrix of HF returns.

Suggested Citation

  • Miceli, M.A. & Susinno, G., 2004. "Ultrametricity in fund of funds diversification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 95-99.
    • Handle: RePEc:eee:phsmap:v:344:y:2004:i:1:p:95-99
    DOI: 10.1016/j.physa.2004.06.094
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    References listed on IDEAS

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    1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    2. V. Plerou & P. Gopikrishnan & B. Rosenow & L. A. N. Amaral & T. Guhr & H. E. Stanley, 2001. "A Random Matrix Approach to Cross-Correlations in Financial Data," Papers cond-mat/0108023, arXiv.org.
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    Cited by:

    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Gloria Polinesi & Maria Cristina Recchioni, 2021. "Filtered clustering for exchange traded fund," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 75(1), pages 125-135, January-M.
    3. Conlon, T. & Ruskin, H.J. & Crane, M., 2007. "Random matrix theory and fund of funds portfolio optimisation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 565-576.
    4. Paolo Giudici & Gloria Polinesi, 2021. "Crypto price discovery through correlation networks," Annals of Operations Research, Springer, vol. 299(1), pages 443-457, April.
    5. Dhagash Mehta & Dhruv Desai & Jithin Pradeep, 2020. "Machine Learning Fund Categorizations," Papers 2006.00123, arXiv.org.
    6. Sieds, 2021. "Complete Volume LXXV n. 1 2021," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 75(1), pages 1-138, January-M.
    7. Jerinsh Jeyapaulraj & Dhruv Desai & Peter Chu & Dhagash Mehta & Stefano Pasquali & Philip Sommer, 2022. "Supervised similarity learning for corporate bonds using Random Forest proximities," Papers 2207.04368, arXiv.org, revised Oct 2022.
    8. Nick James, 2021. "Evolutionary correlation, regime switching, spectral dynamics and optimal trading strategies for cryptocurrencies and equities," Papers 2112.15321, arXiv.org, revised Mar 2022.

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