100% Pass PRMIA - 8011 - Credit and Counterparty Manager (CCRM) Certificate Exam–Efficient Valid Exam Experience

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The Professional Risk Manager's International Association (PRMIA) is a global non-profit organisation dedicated to promoting sound risk management standards and practices throughout the financial industry. As part of their goal, the association offers various certifications designed to benefit risk professionals in pursuing their career goals. Among these is the PRMIA 8011 or Credit and Counterparty Manager (CCRM) Certificate exam.

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PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q213-Q218):

NEW QUESTION # 213
Under the standardized approach to calculating operational risk capital under Basel II, negative regulatory capital charges for any of the business units:

A. Should be excluded from capital calculations
B. Should be ignored completely
C. Should be included after ignoring the negative sign
D. Should be offset against positive capital charges from other business units

Answer: D

Explanation:
According to Basel II, in any given year, negative capital charges (resulting from negative gross income) in any business line may offset positive capital charges in other business lines without limit. Therefore Choice 'b' is the correct answer.

NEW QUESTION # 214
For a back office function processing 15,000 transactions a day with an error rate of 10 basis points, what is the annual expected loss frequency (assume 250 days in a year)

A. 0
B. 0.06
C. 1
D. 2

Answer: D

Explanation:
An error rate of 10 basis points means the number of errors expected in a day will be 15 (recall that 100 basis points = 1%). Therefore the total number of errors expected in a year will be 15 x 250 = 3750. Choice 'a' is the correct answer.

NEW QUESTION # 215
Which of the following are true:
I. Monte Carlo estimates of VaR can be expected to be identical or very close to those obtained using analytical methods if both are based on the same parameters.
II. Non-normality of returns does not pose a problem if we use Monte Carlo simulations based upon parameters and a distribution assumed to be normal.
III. Historical VaR estimates do not require any distribution assumptions.
IV. Historical simulations by definition limit VaR estimation only to the range of possibilities that have already occurred.

A. I, II and III
B. All of the above
C. III and IV
D. I, III and IV

Answer: D

Explanation:
Statement I is true. If a Monte Carlo simulation is based upon the same parameters as used for analytical VaR, and enough number of simulations are carried out, we would get the same results as with analytical VaR.
Statement II is false. We cannot use Monte Carlo simulations using parameters based upon a normal assumption when the underlying distribution is not normal. For example, if a return stream is based upon say a uniform distribution, we cannot use a simulation based upon drawings from a normal distribution even though we use the same mean and standard deviation.
Statement III is true. This is the advantage of historical simulations - no assumptions are necessary.
(Historical simulations however often suffer from the great disadvantage of the paucity of data that would cover all possibilities.) Statement IV is true. The results of historical simulations are limited to the data they are based upon.

NEW QUESTION # 216
Which of the following statements are true in relation to Principal Component Analysis (PCA) as applied to a system of term structures?
I. The factor weights on the first principal component will show whether there is common trend in the system II. The factors to be applied to principal components are obtained from eigenvectors of the correlation matrix III. PCA is a standard method for reducing dimensionality in data when considering a large number of correlated variables IV. The smallest absolute eigenvalues and their associated eigenvectors are the most useful for explaining most of the variation

A. I, II and III
B. II and IV
C. I and III
D. I and IV

Answer: B

Explanation:
If you have not studied PCA prior to the preparing for the PRMIA exams, you will find the handbook to be a bit difficult to understand this topic. However, PRMIA have been asking questions about PCA on their exams so it is important to conceptually know what PCA is (and be able to answer the exam questions which are unlikely to ever require you to do a calculation).
Using PCA, a complex correlated system (eg, the rates in a term structure which are all correlated to each other) is effectively transformed into a 'principal component representation'. A principal component representation expresses each of the independent variables in the system as a linear representation based on
'principal components'. Principal components are derived from the eigenvectors of the correlation matrix.
Remember that if you have a positive semi-definite correlation matrix (which will be an n x n square matrix), it will have 'n' eigenvectors, each with an eigenvalue. The eigenvectors will be orthogonal (ie perpendicular) to each other. The eigenvalues for each of the eigenvectors determine which are principal components - in descending order of the eignevalues. Therefore the eigenvector with the highest eigenvalue is the first principal component, the second highest value the second principal component and so on. If there are n variables in the system, then eigenvalue divided by n gives us the proportion of the variation explained by that particular principal component.
In the case of a PCA done on the term structure of interest rates, the first principal component is the 'trend' component, and under a principal component representation its coefficients are given by the first eigenvector.
(By the way, remember the next two principal components are tilt and curvature respectively). If these coefficients are the same (they are, for an interest rate term structure), this means all rates move up or down when the value of the first principal component moves. Thust factor weights on the first component show whether there is common trend in the system. Statement I is correct.
The factors to be applied to the principal components are indeed obtained from the eigenvectors of the correlation matrix (and not the covariance matrix), and therefore statement II is correct.
PCA is used to reduce dimensionality, this is true and in fact one of the main reasons why PCA is used at all.
Therefore statement III is correct.
Statement IV is false as it is the largest eigenvalues and not the smallest that determine the eigenvectors affecting most of the variation (through the principal components they represent).
Thus Choice 'b' is the correct answer.

NEW QUESTION # 217
Which of the following best describes Altman's Z-score

A. A standardized z based upon the normal distribution
B. A regression of probability of survival against a given set of factors
C. A calculation of default probabilities
D. A numerical computation based upon accounting ratios

Answer: D

Explanation:
Choice 'c' correctly describes Altman's z-score. All other choices are incorrect.

NEW QUESTION # 218
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100% Pass PRMIA - 8011 - Credit and Counterparty Manager (CCRM) Certificate Exam–Efficient Valid Exam Experience

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PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q213-Q218):

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