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PRMIA 8011 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Classic Credit Products, Life Cycle and Credit Risk Methodology | 30% | - Introduction to Credit Risk - Classic Credit Products - Classic Credit Risk Methodology |
| Topic 2: Credit Derivatives and Securitization | 13% | - Securitization Processes and Instruments - Credit Derivatives |
| Topic 3: Credit Valuation Adjustment (CVA) and Related Topics | 17% | - Managing Counterparty Risk and CVA - Credit Valuation Adjustment (CVA) - XVA and SA-CCR |
| Topic 4: Case Studies | 7% | |
| Topic 5: Counterparty Risk and Risk Mitigation | 13% | - Risk Mitigation Techniques - Basics of Counterparty Risk |
| Topic 6: Credit Risk Modeling and Portfolio Management | 20% | - Modern Credit Risk Modeling - Credit Portfolio Management |
PRMIA Credit and Counterparty Manager (CCRM) Certificate Sample Questions:
1. In setting confidence levels for VaR estimates for internal limit setting, it is generally desirable:
A) that actual losses exceed the VaR estimates with some reasonably observable frequency that is neither too high nor too low
B) that actual losses exceed the VaR estimates on only the rarest of occasions
C) that actual losses very frequently exceed the VaR estimates
D) that actual losses never exceed the VaR estimates
2. The 10-day VaR of a diversified portfolio is $100m. What is the 20-day VaR of the same portfolio assuming the market shows a trend and the autocorrelation between consecutive periods is 0.2?
A) 200
B) 141.42
C) 100
D) 154.92
3. The degree distribution of the nodes of the financial network is:
A) best approximated by a beta distribution
B) normally distributed
C) non-linear
D) long tailed
4. Which of the following cannot be used to address the issue of heavy tails when modeling market returns
A) Student's t-distribution
B) Normal mixtures
C) EVT
D) EWMA
5. Which of the following statements are true:
I. Heavy tailed parametric distributions are a good choice for severity modeling in operational risk.
II. Heavy tailed body-tail distributions are a good choice for severity modeling in operational risk.
III. Log-likelihood is a means to estimate parameters for a distribution.
IV. Body-tail distributions allow modeling small losses differently from large ones.
A) II and III
B) All of the above
C) II, III and IV
D) I and IV
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |
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