FNCE 5341 – Financial Risk Management III (Credit Risk) Group Assignment #4 Fall 2025

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FNCE 5341 – Financial Risk Management III (Credit Risk)

Group Assignment #4

Fall 2025

This is group work, which you will perform in groups of up to five students. If for some exceptional reason you would like to work in a larger group please talk to me first (if you have not done that already). Your submission should consist of a Word document and an Excel spreadsheet. The Word document should contain the name of the group, the names of all group participants, and the answers to the questions together with an explanation of what you have done and how to navigate the spreadsheet. The Excel spreadsheet will contain the calculations that you performed in order to answer the questions.

Your submission must be in my mailbox ([email protected]) by 11pm, October 21^st. The subject line should contain the following information: “Assignment 4 – Name of the group”. Late submissions will be penalized, so be sure that you allow yourself sufficient time to safely email your group’s files.  Pay special attention to the format of your Word document: it needs to be presented in such a way that it is clear to me how your analysis has been performed.

1) Estimate an OLS model of loss given default prediction using the first 1000 observations (shaded in light blue) in the “Data Assignment 4” file. The file contains information about defaulted personal loans. The variables included in the dataset are: Recovery rate, Loan to Value ratio, Purpose (of the loan), Characteristic (of the borrower) #1, and Characteristic (of the borrower) #2. Do the variables included in the data help predict losses given default? Notice that, unlike the corporate loss given default data we went over in class, there are no multiple debt instruments in this case. As a result, there will be no clustering and no need to use robust standard errors.

2) Repeat the analysis in 1 above but using Beta transformed losses given default as the basis of the analysis.

3) Use the remaining observations in that dataset (shaded in yellow) to backtest both models. To do this you will need to use the forecasting equations estimated in 1 and 2 above (using only the first 1000 observations) to make forecasts for the loss given default of the remaining observations. Which model seems more accurate in this case?