Portfolio Credit Risk (FRM Part 2 2023 - Book 2 - Chapter 7)

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After completing this reading you should be able to:
- Define and calculate default correlation for credit portfolios.
- Identify drawbacks in using the correlation-based credit portfolio framework.
- Assess the impact of correlation on a credit portfolio and its Credit VaR.
- Describe the use of a single factor model to measure portfolio credit risk, including the impact of correlation.
- Define and calculate Credit VaR.
- Describe how Credit VaR can be calculated using a simulation of joint defaults.
- Assess the effect of granularity on Credit VaR.

[@jackychamp]

hey you happen to have all notes on a page or as a list? they're in video description box i see but i'll have to re-open all videos to be directed to notes. just wondering thanks

[@analystprep]

No, sorry. But you can find everything (notes, video lessons, question bank, mock exams, etc.) included in our premium packages at app.analystprep.com. I hope this helps!

[@LifelongStudentBelgium]

I did not get at all the example of default correlation = 1, so I asked GPT:
Since the default correlation is 1, the credits in the portfolio will either all default or none of them will default. With a 2% default probability, there is a 98% chance that none of the credits will default, and a 2% chance that all of them will default.
At the 95% confidence level, you're looking at the potential losses in the worst 5% of cases. In this specific scenario, the 5% worst cases do not include the event where the entire portfolio defaults, as the default probability is only 2%. Therefore, the credit loss at the 95% confidence level is 0.
What a weird and special case though!

[@praneshkn]

How is pi(12), Joint probability of default computed in this example. We need rho(12) to compute pi(12) or pi(12) for rho(12). Could somebody explain how pi(12) is computed?