Arbeitspapier

Nonparametric Multiple Change Point Analysis of the Global Financial Crisis

This paper presents an application of a recently developed approach by Matteson and James (2012) for the analysis of change points in a data set, namely major financial market indices converted to financial return series. The general problem concerns the inference of a change in the distribution of a set of time-ordered variables. The approach involves the nonparametric estimation of both the number of change points and the positions at which they occur. The approach is general and does not involve assumptions about the nature of the distributions involved or the type of change beyond the assumption of the existence of the a absolute moment, for some a e (0,2). The estimation procedure is based on hierarchical clustering and the application of both divisive and agglomerative algorithms. The method is used to evaluate the impact of the Global Financial Crisis (GFC) on the US, French, German, UK, Japanese and Chinese markets, as represented by the S&P500, CAC, DAX, FTSE All Share, Nikkei 225 and Shanghai A share Indices, respectively, from 2003 to 2013. The approach is used to explore the timing and number of change points in the datasets corresponding to the GFC and subsequent European Debt Crisis.

Sprache
Englisch

Erschienen in
Series: Tinbergen Institute Discussion Paper ; No. 13-072/III

Klassifikation
Wirtschaft
Portfolio Choice; Investment Decisions
Mathematical Methods
Thema
Nonparametric Analysis
Multiple Change Points
Cluster Analysis
Global Financial Crises
Finanzkrise
Clusteranalyse
Nichtparametrisches Verfahren

Ereignis
Geistige Schöpfung
(wer)
Allen, David E.
McAleer, Michael
Powell, Robert J.
Singh, Abhay K.
Ereignis
Veröffentlichung
(wer)
Tinbergen Institute
(wo)
Amsterdam and Rotterdam
(wann)
2013

Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Allen, David E.
  • McAleer, Michael
  • Powell, Robert J.
  • Singh, Abhay K.
  • Tinbergen Institute

Entstanden

  • 2013

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