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A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series

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  • Gautier Marti
  • Philippe Very
  • Philippe Donnat
  • Frank Nielsen

Abstract

We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights by providing through the data perturbations we propose a multi-view of the assets' clustering behaviour. The perturbation framework is illustrated on an extensive credit default swap time series database available online at www.datagrapple.com.

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  • Gautier Marti & Philippe Very & Philippe Donnat & Frank Nielsen, 2015. "A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series," Papers 1509.05475, arXiv.org.
    • Handle: RePEc:arx:papers:1509.05475
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    References listed on IDEAS

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    1. Sergio Mayordomo & Juan Ignacio Peña & Eduardo S. Schwartz, 2014. "Are All Credit Default Swap Databases Equal?," European Financial Management, European Financial Management Association, vol. 20(4), pages 677-713, September.
    2. Laurent Laloux & Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Random Matrix Theory And Financial Correlations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 391-397.
    3. Tumminello, Michele & Lillo, Fabrizio & Mantegna, Rosario N., 2010. "Correlation, hierarchies, and networks in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 75(1), pages 40-58, July.
    4. Joel Bun & Romain Allez & Jean-Philippe Bouchaud & Marc Potters, 2015. "Rotational invariant estimator for general noisy matrices," Papers 1502.06736, arXiv.org, revised Oct 2016.
    5. Borysov, Petro & Hannig, Jan & Marron, J.S., 2014. "Asymptotics of hierarchical clustering for growing dimension," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 465-479.
    6. Tola, Vincenzo & Lillo, Fabrizio & Gallegati, Mauro & Mantegna, Rosario N., 2008. "Cluster analysis for portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 235-258, January.
    7. T. Di Matteo & F. Pozzi & T. Aste, 2010. "The use of dynamical networks to detect the hierarchical organization of financial market sectors," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(1), pages 3-11, January.
    8. Victoria Lemieux & Payam S. Rahmdel & Rick Walker & B.L. William Wong & Mark D. Flood, 2015. "Clustering Techniques and Their Effect on Portfolio Formation and Risk Analysis," Staff Discussion Papers 15-01, Office of Financial Research, US Department of the Treasury.
    9. Martens, Martin & Poon, Ser-Huang, 2001. "Returns synchronization and daily correlation dynamics between international stock markets," Journal of Banking & Finance, Elsevier, vol. 25(10), pages 1805-1827, October.
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    Cited by:

    1. Gautier Marti & S'ebastien Andler & Frank Nielsen & Philippe Donnat, 2016. "Clustering Financial Time Series: How Long is Enough?," Papers 1603.04017, arXiv.org, revised Apr 2016.
    2. 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.
    3. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
    4. Jos'e Vin'icius de Miranda Cardoso & Jiaxi Ying & Daniel Perez Palomar, 2020. "Algorithms for Learning Graphs in Financial Markets," Papers 2012.15410, arXiv.org.
    5. Gautier Marti & Sébastien Andler & Frank Nielsen & Philippe Donnat, 2016. "Clustering Financial Time Series: How Long is Enough?," Post-Print hal-01400395, HAL.
    6. Douglas Castilho & Tharsis T. P. Souza & Soong Moon Kang & Jo~ao Gama & Andr'e C. P. L. F. de Carvalho, 2021. "Forecasting Financial Market Structure from Network Features using Machine Learning," Papers 2110.11751, arXiv.org.
    7. Arnav Hiray & Pratvi Shah & Vishwa Shah & Agam Shah & Sudheer Chava & Mukesh Tiwari, 2023. "Shifting Cryptocurrency Influence: A High-Resolution Network Analysis of Market Leaders," Papers 2307.16874, arXiv.org, revised Jan 2024.
    8. Andrea Di Iura, 2022. "Comparison of empirical and shrinkage correlation algorithm for clustering methods in the futures market," SN Business & Economics, Springer, vol. 2(8), pages 1-17, August.

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