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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Manasse, P., Roubini, N., Schimmelpfennig, A.: Predicting sovereign debt crises. IMF Working Paper 03/221, International Monetary Fund (2003), ISBN: 978-1-45187-525-6
Lee G., Sung T.K., Chang N.: Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction. J. Manag. Inf. Syst. 16, 63–85 (1999)
Lee T., Chiu C., Chou Y., Lu C.: Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Comput. Stat. Data Anal. 50, 1113–1130 (2006)
Weber G.-W., Akyüz-Özögür S., Kropat E.: A review on data mining and continuous optimization applications in computational biology and medicine. Birth Defects Res (Part C)-Embryo Today 87(2), 165–181 (2009)
Detragiache E., Spilimbergo A.: Short-Term Debt and Crises, International Money Fund. European Summer Symposium in International Macroeconomics, Israel (2001)
Çavuşoğlu, Z.: Predicting Debt Crises in Emerging Markets Using Generalized Partial Linear Models. Term Project, Institute of Applied Mathematics, Middle East Technical University, Ankara (2010)
Weber, G.-W., Çavuşoğlu, Z., Özmen, A.: Predicting default probabilities in emerging markets by new conic generalized partial linear models and their optimization. Optim. Special Issue Adv. Continuous Optim. Appl. Finance. 6(4), (2012). doi:10.1080/02331934.2011.654343
Özmen, A.: Robust conic quadratic programming applied to quality improvement—a robustification of CMARS. MSc Thesis, Institute of Applied Mathematics, Middle East Technical University, Ankara (2010)
Özmen, A., Weber, G-W., Batmaz, I.: The new robust CMARS (RCMARS) method. In: ISI Proceedings of 24th MEC-EurOPT 2010—Continuous Optimization and Information-Based Technologies in the Financial Sector, İzmir, Turkey, pp. 362–368 (2010), ISBN: 978-9955-28-598-4
Özmen, A., Weber, G.-W., Batmaz, I., Kropat, E.: RCMARS: Robustification of CMARS with different scenarios under polyhedral uncertainty set. Commun. Nonlinear Sci. Num. Simul. (2011), doi:10.1016/j.cnsns.2011.04.001
Ben-Tal A., Nemirovski A.: Robust optimization—methodology and applications. Math. Program. 92(3), 453–480 (2002)
Ben-Tal A., Nemirovski A.: Robust convex optimization. Math. Oper. Res. 23, 769–805 (1998)
Ben-Tal A., El-Ghaoui L., Nemirovski A.: Robust Optimization. Princeton University Press, (2009)
Özmen, A., Weber, G.-W.: Robust conic generalized partial linear models using RCMARS method—a robustification of CGPLM. preprint at Institute of Applied Mathematics, METU, to appear in Proceedings of Fifth Global Conference on Power Control and Optimization PCO June 1–3, Dubai, (2011). ISBN: 983-44483-49
Müller M.: Estimation and testing in generalized partial linear models—a comparive study. Stat. Comput. 11, 299–309 (2001)
Taylan, P., Weber, G.-W., Lian, L., Yerlikaya-Özkurt, F.: On foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimization. Comput Math Appl (CAM-WA) 60, 1 (2010) 134–143, in the special issue at the occasion of PCO 2010, 3rd Global Conference on Power Control and Optimization, February 2–4, 2010, Gold Coast, Queensland, Australia
Çelik, G.: Parameter estimation in generalized partial linear models with conic quadratic programming. MSc Thesis, Institute of Applied Mathematics, METU, Ankara (2010)
Kayhan, B.: Parameter estimation in generalized partial linear models with Tikhonov regularization method. MSc Thesis, Institute of Applied Mathematics, METU, Ankara (2010)
Weber G.-W., Batmaz I., Köksal G., Taylan P., Yerlikaya F.: CMARS: A new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization. Tech. Rep., Institute of Applied Mathematics, METU, Ankara, Turkey (2009)
Ben-Tal A., Nemirovski A.: Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications, MPR-SIAM Series on Optimization. SIAM, Philadelphia (2001)
MARS® salford systems: software available at http://www.salfordsystems.com
Fioramanti M.: Predicting sovereign debt crises using artificial neural networks: a comparative approach. J. Financial Stab. 4(2), 149–164 (2008)
Fox J.: Bootstrapping Regression Models: An R and S-PLUS Companion to Applied Regression. Sage Publications, CA, USA (2002)
MOSEK: A very powerful commercial software for CQP, available at http://www.mosek.com
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Özmen, A., Weber, GW., Çavuşoğlu, Z. et al. The new robust conic GPLM method with an application to finance: prediction of credit default. J Glob Optim 56, 233–249 (2013). https://doi.org/10.1007/s10898-012-9902-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10898-012-9902-7