Support vector regression for loss given default modelling

Acharya, 2007, Does industry-wide distress affect defaulted firms? Evidences from creditor recoveries, Journal of Financial Economics, 85, 787, 10.1016/j.jfineco.2006.05.011 Banasik, 1996, Does scoring a subpopulation make a difference, International Review of Retail Distribution and Consumer Research, 6, 180, 10.1080/09593969600000019 Bastos, 2010, Forecasting bank loans loss-given-default, Journal of Banking and Finance, 34, 2510, 10.1016/j.jbankfin.2010.04.011 Bellotti, 2012, Loss given default models incorporating macroeconomic variables for credit cards, International Journal of Forecasting, 28, 171, 10.1016/j.ijforecast.2010.08.005 Basel Committee on Banking Supervision (2005a). Guidance on paragraph 468 of the framework document. Basel Committee on Banking Supervision (2005b). An explanatory note on the Basel II IRB risk weight functions. Basel Committee on Banking Supervision (2011). Basel III counterparty credit risk frequently asked questions. Calabrese, R. (2010). Predicting bank loan recovery rates in a mixed continuous-discrete model. Working paper. Dermine, 2006, Bank loan losses-given-default: A case study, Journal of Banking and Finance, 30, 1219, 10.1016/j.jbankfin.2005.05.005 Gupton, 2002, LossCalc™: Model for predicting loss given default (LGD), Moody’s KMV Jacobs, 2011, Modelling ultimate loss-given-default on corporate debt, Journal of Fixed Income, 21, 6, 10.3905/jfi.2011.21.1.006 Khieu, 2012, The determinants of bank loan recovery rates, Journal of Banking and Finance, 36, 923, 10.1016/j.jbankfin.2011.10.005 Leow, 2011, Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data, International Journal of Forecasting, 28, 183, 10.1016/j.ijforecast.2011.01.010 Leow, 2013, The economy and loss given default: evidence form two UK retail lending data sets, Journal of Operational Research Society, 1 Loterman, 2011, Benchmarking regression algorithms for loss given default modelling, International Journal of Forecasting, 28, 161, 10.1016/j.ijforecast.2011.01.006 Moody’s Analytics (2012). Default & recovery database DRD technical specifications. Papke, 1996, Econometric method for fractional response variables with an application to the 401(K) plan participation rates, Journal of Applied Econometrics, 11, 619, 10.1002/(SICI)1099-1255(199611)11:6<619::AID-JAE418>3.0.CO;2-1 Qi, 2009, Loss given default of high loan-to value residential mortgages, Journal of Banking and Finance, 33, 788, 10.1016/j.jbankfin.2008.09.010 Qi, 2011, Comparison of modelling methods for loss given default, Journal of Banking and Finance, 35, 2842, 10.1016/j.jbankfin.2011.03.011 Resti, 2007 SAS Institute Inc. (2009). SAS 9.2 user’s guide. Cary, NC. Staelin, C. (2003). Parameter selection for support vector machines. Technical Report HPL-2002-354. HP Laboratories Israel. Suykens, 2002 Suykens, 1999, Least squares support vector machine classifiers, Neural Processing Letters, 9, 293, 10.1023/A:1018628609742 Tong, 2013, A zero-adjusted gamma model for mortgage loan loss given default, International Journal of Forecasting, 29, 548, 10.1016/j.ijforecast.2013.03.003 Vapnik, 1995 Vapnik, 1998 Zhang, 2010, Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD, International Journal of Forecasting, 28, 204, 10.1016/j.ijforecast.2010.06.002