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Unit information: Credit Risk in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Credit Risk
Unit code EFIMM0067
Credit points 15
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Hill
Open unit status Not open




School/department School of Accounting and Finance
Faculty Faculty of Social Sciences and Law


Understanding credit risk and the measurement of credit risk for both individual instruments and portfolios.

The unit will combine examination of leading academic papers, credit ratings based literature and a commonly employed industry based model for determining portfolio credit risk (Credit Metrics - all material has been provided by the authors of the Credit Metrics system). The unit will be supported by computer lab workshops based in SAS software and this is also an opportunity for students to learn about a leading data analysis software system.

Intended learning outcomes

On completion of the unit students will be able to

  1. Demonstrate knowledge of credit risk is and why it needs to be measured
  2. Demonstrate understanding of models for the evaluation of the credit risk of individual corporations and instruments, following leading academic papers.
  3. Critically evaluate credit ratings
  4. Demonstrate understanding of credit risk within a portfolio context employing the method of CreditMetrics.
  5. Use SAS software and/or excel to:

(a) Construct models for the evaluation of the credit risk of individual corporations and instruments, following leading academic papers;

(b) Apply the Credit Metrics method to determine portfolio credit risk.

Teaching details

Timetabled for staff

7 x 2 hour lectures in lecture theatre

5 x 1 hour workshop sessions supervised in computer lab

Staff contact = 14 + 5 = 19 hours.

Timetabled for students only

5 x 2 hour sessions unsupervised in computer lab

19 contact hours (14 hours of lectures and 5 hours of classes)

2 x 5 hours of unsupervised workshops = 10 hours

100 hours for independent learning.

Assessment Details

Formative assessment: Feedback is given on a week by week basis on exercises completed in the computer labs.

Summative assessment: The unit is assessed via a two-hour examination (70% of the final mark) and coursework (30% of the final mark). The exam will require students to demonstrate their knowledge and understanding of credit risk and the theory behind the empirical models designed to measure credit risk. The exam will contain a combination of qualitative and quantitative questions, which will require students to demonstrate that they understand the various models that are used to quantify credit risks (ILO1 to ILO4). In addition, the exam will test the students’ ability to think critically and communicate effectively in writing under exam conditions. The coursework comprises a workbook containing 5 mini assignments to be completed in 5 unsupervised workshops. Five x 2 hours of unsupervised workshops have been booked for the purpose of assignment completion. However, students are also able to work on the assignments outside of these unsupervised workshops. The workshops will allow students the opportunity to put the theory they learn into practice by constructing a range of credit scoring models for individual corporations and for portfolios (IL05(a) to (c)).

Reading and References


Allison, Paul, 1995, “Survival Analysis Using SAS: A Practical Guide”

Duffie, D., and Singleton, K.J., 2003, ‘Credit Risk: Pricing, Measurement and Management’. Princeton Series in Finance

Grinstead, C.M., Snell, J.L., 1997, Introduction to Probability.

Professional Articles:

‘The Rating Process’, FitchRatings, July 2006.

Understanding Moody’s Corporate Bond Ratings and Rating Process, Moody’s Investors Service, May 2002.

CreditWatch and Ratings Outlooks: Valuable Predictors of Ratings Behavior, Standard and Poor’s, 2005.

Technical Documents


Academic Articles

Bharath, S.T., Shumway, T., 2008. Forecasting default with the Merton distance to default model. Review of Financial Studies 21, 1339-1369.

Blume, M.E., Lim, F., MacKinlay, A.C., 1998. The declining credit quality of U.S. corporate debt: Myth or reality? Journal of Finance 53, 1389-1413.

Campbell, J.Y., Hilscher, J., Szilagyi, J., 2008. In search of distress risk. Journal of Finance 63, 2899–2939.

Chava S. and Jarrow R., 2004. Bankruptcy Prediction with Industry Effects. Review of Finance 8, 537–569

Hillegeist, S.A., Keating, E., Cram, D.P., Lunstedt, K.G., 2004. Assessing the probability of bankruptcy. Review of Accounting Studies 9, 5-34.

Purda, L.D., 2007. Stock market reaction to anticipated versus surprise rating changes. Journal of Financial Research 30, 301–320.

Shumway, T., 2001. Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business 74, 101-124.

Vassalou M. and Xing Y.H., 2004, Default risk in equity returns, Journal of Finance 59, pp. 831—868.

Chen, J., and Hill, P., 2013, ‘The Impact of Diverse Measures of Default Risk on UK Stock Returns’, Journal of Banking and Finance, Vol. 37, pp. 5118-5131.

S&P (Standard & Poor’s), 2008. Corporate criteria: analytical methodology. Available at

S&P (Standard & Poor’s), 2013a. Corporate methodology. Available at

S&P (Standard & Poor’s), 2013b. Corporate methodology: ratios and adjustments. Available at