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  3. 8010 Exam
  4. PRMIA.8010.v2022-03-04.q88 Dumps
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Question 21

A corporate bond has a cumulative probability of default equal to 20% in the first year, and 45% in the second year. What is the monthly marginal probability of default for the bond in the second year, conditional on there beingno default in the first year?

Correct Answer: A
Explanation
Note that marginal probabilities of default are the probabilities for default for a given period, conditional on survival till the end of the previous period. Cumulative probabilities of default are probabilities of default by a point in time, regardless of when the default occurs. If the marginal probabilities of default for periods 1, 2... n are p1, p2...pn, then cumulative probability of default can be calculated as Cn = 1 - (1 - p1)(1-p2)...(1-pn).
For this question, we can calculate the marginal probability of default for year 2 by solving the equation [1 - (1
- 20%)(1 - P2) = 45%] for P2. Solving, we get the marginal probability of default during year 2 as 31.25%.
Since this is the annual marginal probability of default, we will need to convert it to a monthly number, which we can do by solving the following equation where M1 is the monthly marginal probability of default.
1 - 31.25% = (1 - M1)^12, implying M1 = 3.07%
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Question 22

Which of the following statements are correct:
I. A training set is a set of data used to create a model, while a control set is a set of data is used to prove that the model actually works II. Cleansing, aggregating or ensuring data integrity is a task for the IT department, and is not a risk manager's responsibility III. Lack of information on the quality of underlying securities and assets was a major cause of the collapse in the CDO markets during the credit crisis that started in 2007 IV. The problem of lack of historical data can be addressed reasonably satisfactorily by using analytical approaches

Correct Answer: C
Explanation
Statement I is correct. Data is often divided into two sets - a 'training set' that is used to create and fine-tune the model while the 'control set' is used to prove that the model works on sample data. Back testing is then perfomed using actual data that becomes available over time, or may already be available as historical data.
Statement II is incorrect. A risk manager often spends a great deal of time in managing data,and ensuring that the data being used is accurate enough for the purpose it is being used for. A risk manager can expect to spend a good part of his or her team's time in cleansing data. While he or she can try to get the IT processes and systems to produce correct data in the first place so it requires minimal subsequent cleansing or validation, this task is likely to remain a key part of a risk manager's role for quite some time in the future given the challenges nearly all organizations face in managingrisk data.
Statement III is correct. There was not enough granular data available on the underlying components of some of the derivative debt securities whose markets dried up during the crisis that began in 2007. This was because investors became increasingly unsure of what the value of these securities, such as CDOs was, leading to market seizure and firesale prices.
Statement IV is not correct. There is no easy solution to the lack of enough historical data, which is used to create as well as test models, and construct stress scenarios. Analytical approaches are not a good enough substitute for real market data. During the recent crisis, many instruments had rather short histories and there was not enough data available, and risk managers and portfolio managers relied upon analytical approaches to value and price them. Many of the assumptions that underpinned these approaches were untested in the real world and turned out to be incorrect.
Therefore Choice 'c' is the correct answer and the rest are incorrect.
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Question 23

Which of the following is not a credit event under ISDA definitions?

Correct Answer: C
Explanation
According to ISDA, a credit event is an event linked to the deteriorating credit worthiness of an underlying reference entity in a credit derivative. The occurrence of a credit eventusually triggers full or partial termination of the transaction and a payment from protection seller to protection buyer. Credit events include
- bankruptcy,
- failure to pay,
- restructuring,
- obligation acceleration,
- obligation default and
-repudiation/moratorium.
A rating downgrade is not a credit event.
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Question 24

The frequency distribution for operational risk loss events can be modeled by which of the following distributions:
I. The binomial distribution
II. The Poisson distribution
III. The negative binomial distribution
IV. The omega distribution

Correct Answer: A
Explanation
The binomial, Poisson and the negative binomialdistributions can all be used to model the loss event frequency distribution. The omega distribution is not used for this purpose, therefore Choice 'a' is the correct answer.
Also note that the negative binomial distribution provides the best model fit because it has more parameters than the binomial or the Poisson. However, in practice the Poisson distribution is most often used due to reasons of practicality and the fact that the key model risk in such situations does not arise from the choice of an incorrect underlying distribution.
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Question 25

Which of the following belong to the family of generalized extreme value distributions:
I. Frechet
II. Gumbel
III. Weibull
IV. Exponential

Correct Answer: B
Explanation
Extreme value theory focuses on the extreme and rare events, and in the case of VaR calculations, it is focused on the right tail of the lossdistribution. In very simple and non-technical terms, EVT says the following:
1. Pull a number of large iid random samples from the population,
2. For each sample, find the maximum,
3. Then the distribution of these maximum values will follow a GeneralizedExtreme Value distribution.
(In some ways, it is parallel to the central limit theorem which says that the the mean of a large number of random samples pulled from any population follows a normal distribution, regardless of the distribution of the underlying population.) Generalized Extreme Value (GEV) distributions have three parameters: (shape parameter), (location parameter) and (scale parameter). Based upon the value of , a GEV distribution may either be a Frechet, Weibull or a Gumbel. These arethe only three types of extreme value distributions.
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