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University of Southampton – School of Management, in collaboration with SAS UK: PHD OPPORTUNITY IN THE AREA OF CREDIT SCORING, CREDIT RISK MODELLING, AND BASEL II Applications are invited for an EPSRC Industrial CASE studentship to support doctoral study in the area of credit scoring, credit risk modelling, and Basel II. The three and a half-year studentship covers tuition fees and includes payment of a maintenance stipend of not less than £14,600 per year and research support. Applicants must meet EPSRC eligibility requirements and have (at least) an upper second class honours degree (or equivalent) in a suitable subject. The student will have the opportunity to work in an established credit risk research team within the school (supervised by Prof Lyn Thomas, Dr Christophe Mues and Dr Bart Baesens), which is part of an EPSRC-sponsored research consortium, the Quantitative Financial Risk Management Centre (QFRMC). He/she will collaborate closely with the industrial sponsor, SAS, on problems that are of direct interest to their work in the credit risk domain, and that currently attract much attention from the financial sector. Project Description: Basel II-Compliant Credit Risk Modelling for Low- Default Portfolios Credit scoring involves the use by the finance industry of operational research and statistical models to assess and manage credit risk (which includes among other things the probability of borrowers defaulting on their credit obligations). Originally introduced as a handy tool for deciding whether to accept or reject individual applications for retail credit, credit scoring techniques are nowadays used to administer and follow-up default risk across a whole range of credit portfolios. In particular the introduction of the new Basel II regulatory capital framework has created an imminent need to develop new or further refine existing models in this area. However, a common problem for building statistically reliable models is that, for several of the bank's portfolios, the available data only contain a very low number of defaulted exposures or loss events. Typical examples of these so-called low default portfolios (LDPs) include some forms of corporate exposures, bank and sovereign exposures, or even prime mortgage loans. The aim of this project is to develop and/or further investigate several potential statistical or machine learning approaches for dealing with credit risk estimation in LDPs, and to empirically validate their effectiveness on real-life data. We are looking for motivated candidates with a good quantitative/analytical background in one or more of the following areas: management science, OR, statistical analysis, mathematics for finance, data mining & machine learning. For application details and further information please contact Dr Christophe Mues (email: [log in to unmask]). More background information on the School of Management and the Quantitative Financial Risk Management Centre (QFRMC) can be found on www.management.soton.ac.uk and www3.imperial.ac.uk/mathsinstitute/programmes/research/bankfin/qfrmc, respectively. Please submit applications by 18th June 2007.