Decomposing the global financial crisis: A Self-Organizing Time Map

Peter Sarlin*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

A key starting point for financial stability surveillance is understanding past, current and possible future risks and vulnerabilities. Through temporal data and dimensionality reduction, or visual dynamic clustering, this paper aims to present a holistic view of cross-sectional macro-financial patterns over time. The Self-Organizing Time Map (SOTM) is a recent adaptation of the Self-Organizing Map for exploratory temporal structure analysis, which disentangles cross-sectional data structures over time. We apply the SOTM, as well as its combination with classical cluster analysis, in financial stability surveillance. Thus, this paper uses the SOTM for decomposing and identifying temporal structural changes in macro-financial data before, during and after the global financial crisis of 2007-2009.

Original languageEnglish
Peer-reviewed scientific journalPattern Recognition Letters
Volume34
Issue number14
Pages (from-to)1701-1709
Number of pages9
ISSN0167-8655
DOIs
Publication statusPublished - 2013
MoE publication typeA1 Journal article - refereed

Keywords

  • 511 Economics
  • Exploratory temporal structure analysis
  • Financial crisis
  • Financial stability surveillance
  • Self-Organizing Time Map
  • Visual dynamic clustering

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