Issue: Volume 4/Number 2, Summer 2010
Comment LETTER FROM THE EDITOR-IN-CHIEF
It is often assumed that risk model validation is solely about credit risk; that is, the risk of a loan to default or degrade in some way (by, for example, having its rating changed). That this is not true could hardly be gleaned from this issue of The Journal of Risk Model Validation, which presents four excellent papers on credit risk and zero papers on other risk categories; I shall delay describing them in detail to discuss at least one of the other key risk models that also needs validation and explain why much less research occurs in these areas.
Issue: Volume 4/Number 2, Summer 2010
Research Papers Area under the curve maximization method in credit scoring
The receiver operator characteristic curve and area under the curve (AUC) are widely used in credit risk scoring. In this field, it is common to employ the logit model with maximum likelihood estimators. The accuracy of the model is measured by AUC, but it turns out that the logit model with maximum likelihood (ML) estimators (which we refer to as the logit ML model) generally does not achieve optimality with respect to AUC. We propose a new method that uses AUC in a different manner. Our purpose is to estimate parameters and obtain a model for which AUC is maximized; we do this by using an ap
Issue: Volume 4/Number 2, Summer 2010
Research Papers Probability of default estimation and validation within the context of the credit cycle
The dependency of the individual default behavior of a firm on the state of the credit cycle is widely implemented in credit portfolio models and ultimately reflected in the Basel II one-factor model determining capital requirements. Despite this, macroeconomic variables able to represent this one factor - accounting for fluctuations in annual default rates - are not clearly identified. This paper analyzes non-parametric estimates of the credit cycle and macroeconomic variables are identified to explain systematic fluctuations in annual default rates, ie, the credit cycle. Applications of the
Issue: Volume 4/Number 2, Summer 2010
Research Papers Reconciling credit correlations
The credit crisis has resulted in a new impetus for regulators in analyzing the framework for determining regulatory capital requirements; in particular, the assessment of credit risk will be challenged. Confronted with a lack of default statistics, it is common for industry practitioners to apply a financial approach known as Merton's model of the firm, which also underpins modern solvency standards such as Basel II and Solvency II. However, while Merton's theory is an academic beauty, its implementation does not make full use of available default statistics but, instead, relies on the concep
Issue: Volume 4/Number 2, Summer 2010
Research Papers Stress testing of retail mortgages: a study based on non-stationary Markov chains and t-copula simulation
In this study, real-life retail mortgage loan data from a Chinese national commercial bank is used to generate the projected distribution over a set of predefined mortgage states or categories. Non-stationary Markov chain transition probabilities between these states are calculated using loan data from 57 consecutive months. In order to validate the model and assess the risk, we used the data gathered to simulate, by a t-copula method, the projected portfolio distribution over the states of a retail mortgage loan in different shock scenarios. The approach proposed in this paper can be readily
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