Analysis of sectoral credit default cycle dependency with wavelet networks: Evidence from Turkey

In this paper, we investigate the relationship between industrial production and sectoral credit defaults (non-performing loans ratio) cycle by wavelet network analysis in Turkey over the period January 2001–November 2007. We use feedforward neural network based wavelet decomposition to analyze the...

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Bibliographic Details
Published inEconomic modelling Vol. 26; no. 6; pp. 1382 - 1388
Main Authors Cifter, Atilla, Yilmazer, Sait, Cifter, Elif
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.11.2009
Elsevier
Elsevier Science Ltd
SeriesEconomic Modelling
Subjects
Business cycle transmissions
Business cycles
Correlation analysis
Credit
Credit market
Industrial production
Neural networks
Sectoral credit default cycles
Sectoral credit default cycles Business cycles Wavelets Wavelet networks
Studies
Turkey
Wavelet networks
Wavelets
Turkey
H81
Sectoral credit default cycles
Wavelets
Business cycles
E32
C45
Wavelet networks
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Summary:In this paper, we investigate the relationship between industrial production and sectoral credit defaults (non-performing loans ratio) cycle by wavelet network analysis in Turkey over the period January 2001–November 2007. We use feedforward neural network based wavelet decomposition to analyze the contemporaneous connection between industrial production cycles and sectoral credit default cycles at different time scales between 2 and 64 months. The main findings for Turkey indicates that industrial production cycles effect the sectoral credit default cycles at different time scales and thus indicate that the creditors should consider the multiscale sectoral cycles in order to minimize credit default rates.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0264-9993
1873-6122
DOI:10.1016/j.econmod.2009.07.014