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NEW QUESTION 52
The loss severity distribution for operational risk loss events is generally modeled by which of the following distributions:
I. the lognormal distribution
II. The gamma density function
III. Generalized hyperbolic distributions
IV. Lognormal mixtures
- A. I and III
- B. I, II, III and IV
- C. I, II and III
- D. II and III
Answer: B
Explanation:
Explanation
All of the distributions referred to in the question can be used to model the loss severity distribution for op risk.Therefore Choice 'c' is the correct answer.
NEW QUESTION 53
A bank's detailed portfolio data on positions held in a particular security across the bank does not agree with the aggregate total position for that security for the bank. What data quality attribute is missing in this situation?
- A. Data integrity
- B. Data extensibility
- C. Auditability
- D. Data completeness
Answer: A
Explanation:
Explanation
The term 'data quality' has multiple elements, ie, data in order to be considered of a high quality must have multiple attributes such ascompleteness, timeliness, auditability etc. Because this is not an exact science, every expert or text book will have a different view of what goes into data quality. For our purposes however, we will stick to what the PRMIA study material specifies, and according to the study material the following are the elements that can be considered attributes that make for quality data:
1. Integration
2. Integrity
3. Completeness
4. Accessibility
5. Flexibility
6. Extensibility
7. Timeliness
8. Auditability
I am notgoing to describe each of these here as that would be repetitive of the study material, but suffice it to say that the break-down of a number into its constituents should tie to the aggregate total. If that is not true, then the data lacks integrity - andtherefore Choice 'b' is the correct answer. The other choices address other aspects of data quality but not this, and therefore are not correct.
NEW QUESTION 54
Which of the following are true:
I. The total of the component VaRs for all components of a portfolio equals the portfolio VaR.
II. The total of the incremental VaRs for each position in a portfolio equals the portfolio VaR.
III. Marginal VaR and incremental VaR are identical for a $1 change in the portfolio.
IV. The VaR for individual components of a portfolio is sub-additive, ie the portfolio VaR is less than (or in extreme cases equal to) the sum of the individual VaRs.
V. The component VaR for individual components of a portfolio is sub-additive, ie the portfolio VaR is less than the sum of the individual component VaRs.
- A. II and IV
- B. II and V
- C. I and II
- D. I,III and IV
Answer: D
Explanation:
Explanation
Statement I is true - component VaR for individual assets in the portfolio add up to the total VaR for the portfolio. This property makes component VaR extremely useful for risk disaggregation and allocation.
Stateent II is incorrect, the incremental VaRs for the positions in a portfolio do not add up to the portfolio VaR, in fact their sum would be greater.
Statement III is correct. Marginal VaR for an asset or position in the portfolio is by definition the change in the VaR as a result of a $1 change in that position. Incremental VaR is the change in the VaR for a portfolio from a new position added to the portfolio - and if that position is $1, it would be identical to the marginal VaR.
Statement IV is correct, VaR is sub-additive due to the diversification effect. Adding up the VaRs for all the positions in a portfolio will add up to more than the VaR for the portfolio as a whole (unless all the positions are 100% correlated, which effectively would mean they are all identical securities which means the portfolio has only one asset).
Statement V is in incorrect. As explained for Statement I above, component VaR adds up to the VaR for the portfolio.
NEW QUESTION 55
An assumption regarding the absence of ratings momentum is referred to as:
- A. Herstatt risk
- B. Time invariance
- C. Markov property
- D. Ratings stability
Answer: C
Explanation:
Explanation
Choice 'c' is the correct answer. The Markov property is the assumption that there is no ratings momentum, and that transition probabilities are dependent only upon where the rating currently is and where it is going to.
Where it has come from, or what the past changes in ratings have been, have no effect on the transition probabilities. ('Herstatt risk' refers to settlement risk, and is irrelevant.)
NEW QUESTION 56
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