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The new robust conic GPLM method with an application to finance: prediction of credit default

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  • Ayşe Özmen
  • Gerhard-Wilhelm Weber
  • Zehra Çavuşoğlu
  • Özlem Defterli

Abstract

This paper contributes to classification and identification in modern finance through advanced optimization. In the last few decades, financial misalignments and, thereby, financial crises have been increasing in numbers due to the rearrangement of the financial world. In this study, as one of the most remarkable of these, countries’ debt crises, which result from illiquidity, are tried to predict with some macroeconomic variables. The methodology consists of a combination of two predictive regression models, logistic regression and robust conic multivariate adaptive regression splines (RCMARS), as linear and nonlinear parts of a generalized partial linear model. RCMARS has an advantage of coping with the noise in both input and output data and of obtaining more consistent optimization results than CMARS. An advanced version of conic generalized partial linear model which includes robustification of the data set is introduced: robust conic generalized partial linear model (RCGPLM). This new model is applied on a data set that belongs to 45 emerging markets with 1,019 observations between the years 1980 and 2005. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Ayşe Özmen & Gerhard-Wilhelm Weber & Zehra Çavuşoğlu & Özlem Defterli, 2013. "The new robust conic GPLM method with an application to finance: prediction of credit default," Journal of Global Optimization, Springer, vol. 56(2), pages 233-249, June.
    • Handle: RePEc:spr:jglopt:v:56:y:2013:i:2:p:233-249
    DOI: 10.1007/s10898-012-9902-7
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    References listed on IDEAS

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    1. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.
    2. Fioramanti, Marco, 2008. "Predicting sovereign debt crises using artificial neural networks: A comparative approach," Journal of Financial Stability, Elsevier, vol. 4(2), pages 149-164, June.
    3. Mr. Axel Schimmelpfennig & Nouriel Roubini & Paolo Manasse, 2003. "Predicting Sovereign Debt Crises," IMF Working Papers 2003/221, International Monetary Fund.
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    Cited by:

    1. Magdalena Graczyk-Kucharska & Robert Olszewski & Gerhard-Wilhelm Weber, 2023. "The use of spatial data mining methods for modeling HR challenges of generation Z in greater Poland Region," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 205-237, March.
    2. Ayşe Özmen & Yuriy Zinchenko & Gerhard-Wilhelm Weber, 2023. "Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market," Annals of Operations Research, Springer, vol. 324(1), pages 1337-1367, May.
    3. Özmen, Ayşe & Yılmaz, Yavuz & Weber, Gerhard-Wilhelm, 2018. "Natural gas consumption forecast with MARS and CMARS models for residential users," Energy Economics, Elsevier, vol. 70(C), pages 357-381.
    4. Zhu, Kai & Ji, Kaiyuan & Shen, Jiayu, 2021. "A fixed charge transportation problem with damageable items under uncertain environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).

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