Journal of Credit Risk

Welcome to the December issue of The Journal of Credit Risk.


In the issue's first paper, ‘Modeling the current loan-to-value structure of mortgage pools without loan-specific data', Peter Palmroos presents a method for approximating the current loan-to-value (CLTV) and remaining principal structures of heterogeneous mortgage loan pools. Using empirical data collected from the Finnish banking sector, the solved structure makes it possible to analyze changes in the dynamics of a mortgage loan pool over time, as well as compare the risks of different pools. One of the most important applications of the model is that it can be used to supplement the existing mortgage loan credit risk models and, thus, improve the accuracy of risk and loss estimates.

In ‘Further investigation of parametric loss given default modeling', Phillip Li, Min Qi, Xiaofei Zhang and Xinlei Zhao conduct an interesting comprehensive study of some new parametric models that are designed to fit the unusual bounded and bi-modal distribution of loss given default. The authors claim to offer evidence that the more sophisticated methods (i.e. smearing estimator, a Monte Carlo estimator, and a global adjustment approach) need not outperform the more commonly used fractional response and standard linear regressions.

In ‘Financial and nonfinancial variables as long-horizon predictors of bankruptcy' by Edward I. Altman, Małgorzata Iwanicz-Drozdowska, Erkki K. Laitinen and Arto Suvas, the authors assess the long-term ability of financial and non-financial variables to predict business failure. The authors constructed and tested three groups of models: based on financial variables only; based on non-financial variables only; and based on both financial and non-financial variables. Their model shows that measures of solvency, turnover, environmental risk, payment behaviour and board member characteristics can be significant predictors of bankruptcies for as long as ten years.

The final paper, ‘Benchmarking the loss given default parameter for mortgage loan portfolios under stress' by Christian Greve and Lutz Hahnenstein analyzes the impact of a decline in property prices that leads to stressed recovery rates for collateral on the loss given default (LGD) parameter in portfolios of mortgage loans. The authors derive a closed-form solution to the portfolio LGD assuming that LTVs follow a truncated Beta distribution, and they present evidence that this distribution is a reasonable approximation to observed LTV distributions.

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an indvidual account here: