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The aftermath of the subprime crisis: a clustering analysis of world banking sector

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  • José Dias
  • Sofia Ramos

Abstract

The banking sector has been on the spotlight in both academic and policy circles since the outburst of the subprime bubble. The crisis has its roots in the US, but there were spillover effects around the world. We study the behavior of the banking sector of 40 countries during the period 2007–2010, using a new clustering methodology. Our methodology combines regime switching models in the modeling of longitudinal variations with cluster analysis that identifies groups of countries with similar profiles. Our results show that although there were periods of intense contagion, the impact was uneven among sample countries. The crisis had episodic effects on some countries, while others had severe devaluations after the Lehman Brothers bankruptcy. Finally, a small group of banking systems has plunged into a long severe crisis. Copyright Springer Science+Business Media New York 2014

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  • José Dias & Sofia Ramos, 2014. "The aftermath of the subprime crisis: a clustering analysis of world banking sector," Review of Quantitative Finance and Accounting, Springer, vol. 42(2), pages 293-308, February.
    • Handle: RePEc:kap:rqfnac:v:42:y:2014:i:2:p:293-308
    DOI: 10.1007/s11156-013-0342-3
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    Cited by:

    1. Peter Grundke, 2019. "Ranking consistency of systemic risk measures: a simulation-based analysis in a banking network model," Review of Quantitative Finance and Accounting, Springer, vol. 52(4), pages 953-990, May.
    2. Jianping Li & Lu Wei & Cheng-Few Lee & Xiaoqian Zhu & Dengsheng Wu, 2018. "Financial statements based bank risk aggregation," Review of Quantitative Finance and Accounting, Springer, vol. 50(3), pages 673-694, April.
    3. 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.
    4. Trindade, Graça & Dias, José G. & Ambrósio, Jorge, 2017. "Extracting clusters from aggregate panel data: A market segmentation study," Applied Mathematics and Computation, Elsevier, vol. 296(C), pages 277-288.
    5. Bhimjee, Diptes C. & Ramos, Sofia B. & Dias, José G., 2016. "Banking industry performance in the wake of the global financial crisis," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 376-387.
    6. Nathan Lael Joseph & Thi Thuy Anh Vo & Asma Mobarek & Sabur Mollah, 2020. "Volatility and asymmetric dependence in Central and East European stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 55(4), pages 1241-1303, November.
    7. Ken B. Cyree & Travis R. Davidson & John D. Stowe, 2020. "Forming appropriate peer groups for bank research: a cluster analysis of bank financial statements," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(2), pages 211-237, April.
    8. Mehrzad Azmi Shabestari & Kevin Moffitt & Bharat Sarath, 2020. "Did the banking sector foresee the financial crisis? Evidence from risk factor disclosures," Review of Quantitative Finance and Accounting, Springer, vol. 55(2), pages 647-669, August.
    9. Helena Isidro & José G. Dias, 2017. "Earnings quality and the heterogeneous relation between earnings and stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 49(4), pages 1143-1165, November.
    10. Grout, Paul A. & Zalewska, Anna, 2016. "Stock market risk in the financial crisis," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 326-345.

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    More about this item

    Keywords

    Banking sector; Clustering methods; Time series data; Regime switching models; Hidden Markov model; G21; C34; C38;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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