Compute the 99% and 99.5% alpha quantiles using the basic simulation (and code) discussed in Lecture 8 (Löffler and Posch macro simVBA) and 50,000 trials.

FNCE 5341 – Financial Risk Management III (Credit Risk)

1. 1)  Using the data provided in the Data Assignment 5 file:
   1. Compute the 99% and 99.5% alpha quantiles using the basic simulation (and code) discussed in Lecture 8 (Löffler and Posch macro simVBA) and 50,000 trials.
   2. Repeat the same exercise but allowing for uncertainty about the factor sensitivity As in Löffler and Posch’s example assume estimation error is perfectly correlated across portfolio components. Model this by drawing w from a normal distribution with mean w (in Data Assignment 6) and standard deviation equal to 0.06. Present the results and relevant changes to VBA code discussed in class.
   3. Repeat the same exercise once more allowing for uncertainty about the factor sensitivity w but this time assume estimation error is uncorrelated across portfolio components. Model this again by drawing w from a normal distribution with mean w (in Data Assignment 6) and standard deviation equal to 0.06. Present the results and relevant changes to VBA code discussed in class.
2. 2)  For a borrower rated B, you have estimated the following transition probabilities:

A B C Default 10% 80% 5% 5%

Determine the critical thresholds for these categories for a Monte Carlo Simulation with multi-state modelling.

3) (Mini case) Voyager is a major player in fixed income investments. With total assets of more than $ 200 billion, its portfolio consists of 75 percent in Public Corporate Securities, 15 percent in Private Placements, and 10 percent in structured securities (such as CDOs). As is common with many fixed income investors, the company has a per-issuer exposure limit based on issuer rating, ranging from $375mm to AAA and AA, $325 for A, $250 for BBB, $100 for BB and $50 for lower grades. The risk management unit has been hearing concerns from the portfolio managers that the issuer limits have become too restrictive and they are finding it harder to put new money to work because many of the creditworthy names are approaching the corporate issuer limit. A possible solution to this problem is increasing the per-issuer limits.

Submit a recommendation for a set of new limits.

1. Summarize your recommendation and explain your rationale to support your

recommendation.

2. Indicate (in the recommendation), what other information you need to

complete the analysis and what assumptions (if any) you employed.

3. Explain (in the recommendation) what analytical modelling (if any) you

conducted to establish the new limit?

(The evaluation of this question will be based on your reasoning and analytical process, and how you present the recommendation.)