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

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Author Info

  • Cifter, Atilla
  • Yilmazer, Sait
  • Cifter, Elif

Abstract

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 64Â 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.

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Bibliographic Info

Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 26 (2009)
Issue (Month): 6 (November)
Pages: 1382-1388

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Handle: RePEc:eee:ecmode:v:26:y:2009:i:6:p:1382-1388

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Web page: http://www.elsevier.com/locate/inca/30411

Related research

Keywords: Sectoral credit default cycles Business cycles Wavelets Wavelet networks;

References

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Citations

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Cited by:
  1. Dimitrios P. Louzis & Aggelos T. Vouldis & Vasilios L. Metaxas, 2010. "Macroeconomic and bank-specific determinants of non-performing loans in Greece: a comparative study of mortgage, business and consumer loan portfolios," Working Papers 118, Bank of Greece.
  2. Vítor Castro, 2012. "Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI," NIPE Working Papers 11/2012, NIPE - Universidade do Minho.
  3. Castro, Vítor, 2013. "Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI," Economic Modelling, Elsevier, vol. 31(C), pages 672-683.

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