Orth, Walter (2011): Multi-period credit default prediction with time-varying covariates.
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In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock market variables. If the prediction horizon covers multiple periods, this leads to the problem that the future evolution of these covariates is unknown. Consequently, some authors have proposed a framework that augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi-period prediction that avoid the burden to specify and estimate a model for the covariate processes. In an application to North American public firms, we show that the proposed models deliver high out-of-sample predictive accuracy.
|Item Type:||MPRA Paper|
|Original Title:||Multi-period credit default prediction with time-varying covariates.|
|Keywords:||Credit default; multi-period predictions; hazard models; panel data; out-of-sample tests|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods
C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C41 - Duration Analysis ; Optimal Timing Strategies
G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill
|Depositing User:||Walter Orth|
|Date Deposited:||22 Nov 2011 13:08|
|Last Modified:||24 Jan 2016 16:37|
Agarwal, V. & Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 32, 1541–1551.
Basel Committee on Banking Supervision (2005). Studies on the Validation of Internal Rating Systems. Basel Committee on Banking Supervision working paper No. 14.
Basel Committee on Banking Supervision (2006). International Convergence of Capital Measurement and Capital Standards: A Revised Framework.
Basel Committee on Banking Supervision (2009). Guiding Principles for the Replacement of IAS 39. Available at http://www.bis.org/publ/bcbs161.htm.
Bennett, S. (1983). Analysis of survival data by the proportional odds model. Statistics in Medicine, 2, 273–277.
Breslow, N. E. (1974). Covariance analysis of censored survival data. Biometrics, 30, 89–100.
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2011). Robust inference with multiway clustering. Journal of Business and Economic Statistics, 29 (2), 238–249.
Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. Journal of Finance, 63, 6, 2899–2939.
Carling, K., Jacobson, T., Linde, J., & Roszbach, K. (2007). Corporate credit risk modeling and the macroeconomy. Journal of Banking & Finance, 31, 845–868.
Chava, S. & Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8, 537–569.
Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society Series B, 34, 2, 187–220.
Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge.
Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83, 635–665.
Ebnoether, S. & Vanini, P. (2007). Credit portfolios: What defines risk horizons and risk measurement? Journal of Banking & Finance, 31, 3663–3679.
Field, C. A. & Welsh, A. H. (2007). Bootstrapping clustered data. Journal of the Royal Statistical Society Series B, 69, 369–390.
Figlewski, S., Frydman, H., & Liang, W. (2006). Modeling the Effect of Macroeconomic Factors on Corporate Default and Credit Rating Transitions. NYU Stern Finance Working Paper No. FIN-06-007.
Fons, J. S. (1994). Using default rates to model the term structure of credit risk. Financial Analysts Journal, 50 (5), 25–32.
Hamerle, A., Jobst, R., Liebig, T., & Roesch, D. (2007). Multiyear risk of credit losses in SME portfolios. Journal of Financial Forecasting, 1(2), 1–29.
Hamerle, A., Liebig, T., & Scheule, H. (2006). Forecasting credit event frequency – empirical evidence for West German firms. Journal of Risk, 9 (1), 75–98.
Hanson, S. & Schuermann, T. (2006). Confidence intervals for probabilities of default. Journal of Banking & Finance, 30, 2281–2301.
Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lundstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 9, 5–34.
Jacobson, T., Kindell, R., Linde, J., & Roszbach, K. (2008). Firm Default and Aggregate Fluctuations. Sveriges Riksbank Working Paper Series No. 226.
Lawless, J. F. (2003). Statistical Models and Methods for Lifetime Data. Wiley.
Lin, D. Y. (1994). Cox regression analysis of multivariate failure time data: The marginal approach. Statistics in Medicine, 13, 2233–2247.
Maennasoo, K. & Mayes, D. G. (2009). Explaining bank distress in Eastern European transition economies. Journal of Banking & Finance, 33 (2), 244–253.
Orth, W. (2011). The predictive accuracy of credit ratings: Measurement and statistical inference. International Journal of Forecasting. Forthcoming. doi:10.1016/j.ijforecast.2011.07.004.
Royston, P. & Parmar, M. K. B. (2002). Flexible parametric-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine, 21, 2175–2197.
Roesch, D. & Scheule, H. (2007). Multi-year dynamics for forecasting economic and regulatory capital in banking. Journal of Credit Risk, 3 (4), 113–134.
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74, 101–124.
Somers, R. H. (1962). A new asymmetric measure of association for ordinal variables. American Sociological Review, 27(6), 799–811.
Standard & Poor’s (2010). The Time Dimension of Standard & Poor’s Credit Ratings. Global Credit Portal, September 22, 2010.
Stein, R. M. (2004). Benchmarking Default Prediction Models. Moody’s KMV technical report No. 030124. Available at http://www.moodyskmv.com/research/.
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Multi-period credit default prediction with time-varying covariates. (deposited 28 Apr 2011 17:53)
Multi-period credit default prediction with time-varying covariates. (deposited 31 May 2011 13:05)
- Multi-period credit default prediction with time-varying covariates. (deposited 22 Nov 2011 13:08) [Currently Displayed]
- Multi-period credit default prediction with time-varying covariates. (deposited 31 May 2011 13:05)