The measurement of credit risk through structural models: An application to the Colombian market
PDF (Spanish)

Keywords

Probability of default
structural models
Merton model
asset volatility

How to Cite

Caicedo Cerezo, E., Claramunt Bielsa, M., & Casanovas Ramón, M. (2011). The measurement of credit risk through structural models: An application to the Colombian market. Cuadernos De Administración, 24(42). https://doi.org/10.11144/Javeriana.cao24-42.mrcm
Almetrics
 
Dimensions
 

Google Scholar
 
Search GoogleScholar

Abstract

This article presents the results of the study on credit risk management in shares included in the Colombian stock exchange index (IGBC), between 2005 and 2007. The probability of default and rates of recovery given default are estimated using the Merton structural approach, and its extensions. The assumptions of constant volatility and heteroskedacity of the companies´ assets are used to provide estimates by individual company and by economic sector. The results indicate, with a statistical significance of 1%, and using non-parametric tests, that in that period there were significant differences in the probability of default at the level of the sector, and that the patterns of heteroskedacity considered in the volatility of assets have no significant influence on the estimates of the probability of default.

PDF (Spanish)

Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609.

Altman, E. (2000). Predicting financial distress of companies: Revisiting the Z-Score and Zeta Models. Recuperado el 29 de septiembre de 2008, de http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.1884

Altman, E.; Haldeman, R. and Narayanan P. (1977). Zeta analysis. A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1, 589-609.

Altman, E.; Marco, M. and Varetto, F. (1994). Corporate distress diagnostics: Comparison using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18, 505-529.

Altman, E. and Sabato, G. (2005). Modeling credit risk for SMEs: Evidence from the US market. Recuperado el 29 de septiembre de 2008, de http://ssrn.com/abstract=872336

Arora, N.; Jeffrey R. and Fanlin, Z. (2005). Reduced form vs. structural models of credit risk: A case study of three models. Journal of Investment Management, 3 (4), 43-67.

Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12, 929-935.

Basilea (1988). Convenio de capital. Comité de Supervisión Bancaria de Basilea, Convenio de Capital. Banco de Pagos Internacionales. Basilea, Suiza.

Basilea (2001). Convenio de capital. Proceedings of the 11th. International Conference of Banking Supervisors, 20-22 september, Basilea, Suiza.

Basilea (2004). Convergencia internacional de medidas y normas de capital. Comité de Supervisión Bancaria de Basilea, Marco revisado junio. Banco de Pagos Internacionales. Basilea, Suiza.

Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal Political Economy, 81 (3), 637-654.

Carreras, P. M. (2006). Credit risk modeling using insurance methods. Barcelona: Tesis doctoral Universidad de Barcelona.

Chen, C. J. and Panjer, H. (2003). Unifying discrete structural models and reduced-form models in credit risk using a jump-diffusion process. Insurance: Mathematics and Economics, 33 (2), 357-380.

Chen, C. J. and Panjer, H. (2009). A Bridge from Ruin Theory to Credit Risk. Review of Quantitative Finance and Accounting, 32 (4), 373-403.

Chen, Z. F.; Liv, Y. Y. and Wang, X. F. (2008). Comparative study on credit risk models. Proceedings of 2008 International Conference on Risk and Reliability Management, I-II, 189-191.

Coyle, B. (2000). Corporate credit analysis. Chicago: Amacom.

Crosbie, P. and Bohn, J. R. (2003). Modeling Default Risk. KMV Corporation.

Crouhy, M.; Galai, D. and Mark, R. (2000). A comparative analysis of current credit risk models. Journal of Banking and Finance, 24, 59-117.

DeLara, H.A. (2003). Medición y control de riesgos financieros. México: Limusa Noriega Editores.

Deng, Z. W. (2005). Moody's KMV model and its apply in credit risk evaluation of corporate. Proceedings of International Conference on Construction and Real Estate Management, 1-2, 773-775.

Duan, J. C. and Andras, F. (2009). Estimating the structural credit risk model when equity prices are contaminated by trading noises. Journal of Econometrics, 150 (2), 288-296.

Dunkel, J. and Weber, S. (2007). Efficient Monte Carlo methods for convex risk measures in portfolio credit risk models. Proceedings of the 2007 Winter Simulation Conference, 1-5, 937-945.

Elizalde, A. (2005). Credit Risk Models II: Structural Models. Madrid: CEMFI and UPNA.

Ericsson, J. and Reneby, J. (2005). Estimating structural bond pricing models. Journal of Business, 78 (2), 707-735.

Geske, R. (1977). The valuation of corporate liabilities as compound options. Journal of Financial and Quantitative Analysis, 12 (4), 541-552.

Greene, W. H. (2000). Econometric Analysis, 2nd . ed., New York: Prentice Hall Internacional Editions.

International Actuarial Association (2004). A global Framework for Insurer Solvency Assessment. Research Report of the Insurer Solvency Assessment Working Party.

Jackson, P.; Nickell, P. and Perraudin, W. (1999). Credit risk modeling. Financial Stability Review, 94-121.

Jarrow, R. A.; Protter, P. and Sezer, A. D. (2007). Information reduction via level crossings in a credit risk model. Finance and Stochastics, 11, 195-212.

Jarrow, R.A. and Protter, P.(2004). Structural versus reduced form models: A new information based perspective. Journal of Investment Management, 2 (2), 1-10.

Jones, P.; Mason, P. S. and Rosenfeld, E. (1984). Contingent claims analysis of corporate capital structures: An empirical investigation. Journal of Finance, 39 (3), 611-625.

JP Morgan and Company (1997). CreditMetrics. Documento Técnico. Nueva York: JP Morgan.

Loffler, G. and Posch, P. (2007). Credit Risk Modeling. England: John Wiley and Sons.

Márquez, J. (2006). Una nueva visión del riesgo de crédito. México: Limusa - Noriega Editores.

Merton, R. (1974). On the pricing of the corporate debt: the risk structure of interest rates. Journal of Finance, 29 (2), 449-470.

Pederson, G. D. and Zech, L. (2009). Assessing credit risk in an agricultural loan portfolio. Canadian Journal of Agricultural Economics, 57, 169-185.

Ramaswamy, S. (2005). Simulated credit loss distribution. Journal of Portfolio Management, 31, 91-99.

Saavedra, G. M. L. and Saavedra, G. M. J. (2010). Modelos para medir riesgo de crédito de la banca. Cuadernos de Administración, 23 (40), 295-319.

Samaniego, R.; Trujillo, A. y Martin, J. L. (2007). Un análisis de los modelos contables y de mercado en la evaluación del riesgo de crédito: aplicación al mercado bursátil español. Revista europea de dirección y economía de la empresa, 16 (2), 93-110.

Seidler, J. and Petr, J. (2009). Implied market loss given defaultin the Czech Republic: Structural-model approach. Finance a Uver/Czech Journal of Economics and Finance, 59 (1), 20-40.

Soros, G.; Simons, J.; Paulson, J.; Griffin, K. and Falcone, P. (2008). Statement before the U.S House of Representatives Committee on oversight and Government Reform. U.S. Congress. Recuperado el 16 de noviembre de 2008, de http://republicans.oversight.house.gov

Vassalou, M. and Xong, Y. (2004). Default risk in equity returns. Journal of Finance, LIX (2), 831-868.

Wei, R. (2008). Development of credit risk model based on fuzzy theory and its application for credit risk management of commercial banks in China. 4th. International conference on wireless communications, networking and mobile computing, 1-31, 10339-10342.

Zech, L. and Pederson, G. (2004). Application of credit risk models to agricultural lending. Agricultural Finance Review, 64, 91-106.

Zhu, Y. and Chiu, W. H. (2007). Credit risk assessment using the RBF neural network. Information. Management and Algorithms, II, 125-128.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.