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PRMIA 8010 Exam Questions
- Topic 1: Classic Credit Products: This section of the exam covers traditional lending instruments like loans and bonds used by banks and financial institutions.
- Topic 2: Classic Credit Life Cycle: This section covers the stages a credit product goes through, from origination to maturity or default.
- Topic 3: Classic Credit Risk Methodology: This section covers conventional approaches to assessing and quantifying the risk of borrower default.
- Topic 4: Credit Derivatives and Securitization: In this section, the topics covered include financial instruments that transfer credit risk and pool debt-based assets into tradable securities.
- Topic 5: Modern Credit Risk Modeling: This section covers advanced statistical and mathematical techniques for measuring and managing credit risk.
- Topic 6: Credit Portfolio Management: This section covers strategies for optimizing the overall risk and return of a collection of credit exposures.
- Topic 7: Basics of Counterparty Risk: This section covers fundamental concepts related to the risk of a counterparty failing to fulfill their contractual obligations.
- Topic 8: Risk Mitigation: This section covers techniques and tools used to reduce or transfer various types of financial risks.
- Topic 9: Credit Valuation Adjustment (CVA): This section covers an adjustment to the fair value of derivatives to account for counterparty credit risk.
- Topic 10: CVA-related Aspects: This section covers additional considerations and implications associated with Credit Valuation Adjustment.
- Topic 11: Managing Counterparty Risk and CVA: This section covers strategies and practices for controlling exposure to counterparty default and optimizing CVA.
Free PRMIA 8010 Exam Actual Questions
Note: Premium Questions for 8010 were last updated On Jan. 25, 2025 (see below)
Financial institutions need to take volatility clustering into account:
1. To avoid taking on an undesirable level of risk
2. To know the right level of capital they need to hold
3. To meet regulatory requirements
4. To account for mean reversion in returns
Volatility clustering leads to levels of current volatility that can be significantly different from long run averages. When volatility is running high, institutions need to shed risk, and when it is running low, they can afford to increase returns by taking on more risk for a given amount of capital. An institution's response to changes in volatility can be either to adjust risk, or capital, or both. Accounting for volatility clustering helps institutions manage their risk and capital and therefore statements I and II are correct.
Regulatory requirements do not require volatility clustering to be taken into account (at least not yet). Therefore statement III is not correct, and neither is IV which is completely unrelated to volatility clustering.
Which of the following are valid approaches for extreme value analysis given a dataset:
1. The Block Maxima approach
2. Least squares approach
3. Maximum likelihood approach
4. Peak-over-thresholds approach
For EVT, we use the block maxima or the peaks-over-threshold methods. These provide us the data points that can be fitted to a GEV distribution.
Least squares and maximum likelihood are methods that are used for curve fitting, and they have a variety of applications across risk management.
Financial institutions need to take volatility clustering into account:
1. To avoid taking on an undesirable level of risk
2. To know the right level of capital they need to hold
3. To meet regulatory requirements
4. To account for mean reversion in returns
Volatility clustering leads to levels of current volatility that can be significantly different from long run averages. When volatility is running high, institutions need to shed risk, and when it is running low, they can afford to increase returns by taking on more risk for a given amount of capital. An institution's response to changes in volatility can be either to adjust risk, or capital, or both. Accounting for volatility clustering helps institutions manage their risk and capital and therefore statements I and II are correct.
Regulatory requirements do not require volatility clustering to be taken into account (at least not yet). Therefore statement III is not correct, and neither is IV which is completely unrelated to volatility clustering.
Concentration risk in a credit portfolio arises due to:
Concentration risk in a credit portfolio arises due to a high degree of correlation between the default probabilities of the issuers of securities in the portfolio. For example, the fortunes of the issuers in the same industry may be highly correlated, and an investor exposed to multiple such borrowers may face 'concentration risk'.
A low degree of correlation, or independence of individual defaults in the portfolio actually reduces or even eliminates concentration risk.
The fact that issuers are from the same country may not necessarily give rise to concentration risk - for example, a bank with all US based borrowers in different industries or with different retail exposure types may not face practically any concentration risk. What really matters is the default correlations between the borrowers, for example a lender exposed to cement producers across the globe may face a high degree of concentration risk.
Which of the following are valid approaches for extreme value analysis given a dataset:
1. The Block Maxima approach
2. Least squares approach
3. Maximum likelihood approach
4. Peak-over-thresholds approach
For EVT, we use the block maxima or the peaks-over-threshold methods. These provide us the data points that can be fitted to a GEV distribution.
Least squares and maximum likelihood are methods that are used for curve fitting, and they have a variety of applications across risk management.
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