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PRMIA 8011 certification exam covers a wide range of topics related to credit and counterparty risk management, including credit analysis, credit risk measurement, credit risk models, counterparty risk management, credit derivatives, and structured finance. It also covers regulatory frameworks, such as Basel III, and the role of credit rating agencies in credit risk management.

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.

PRMIA 8011 (Credit and Counterparty Manager (CCRM) Certificate) Certification Exam is a professional certification exam designed for individuals who are looking to pursue a career in credit and counterparty risk management. 8011 exam is offered by the Professional Risk Managers' International Association (PRMIA) and is recognized globally as a benchmark for excellence in credit and counterparty risk management.

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PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q150-Q155):

NEW QUESTION # 150
Which of the following need to be assumed to convert a transition probability matrix for a given time period to the transition probability matrix for another length of time:
I. Time invariance
II. Markov property
III. Normal distribution
IV. Zero skewness

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

Answer: C

Explanation:
Time invariance refers to all time intervals being similar and identical, regardless of the effects of business cycles or other external events. 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.
Rating agencies generally provide transition probability matrices for a given period of time, say a year. The risk analyst may need to convert these into matrices for say 6 months, 2 years or whatever time horizon he or she is interested in. Simplifying assumptions that allow him to do so using simple matrix multiplication include these two assumptions - time invariance and the Markov property. Thus Choice 'c' is the correct answer. The other choices (normal distribution and zero skewness) are non-sensical in this context.

NEW QUESTION # 151
The systemic manifestation of the liquidity crisis during the current credit crisis took many forms. Which of the following is not one of those forms?

A. Drying up of liquidity in the corporate bond markets
B. Drying up of liquidity in the cash market for treasury bonds
C. Stress and large withdrawals from the money markets
D. Drying up of liquidity in the wholesale money markets

Answer: B

Explanation:
The stresses on liquidity that happened as part of the credit crisis beginning 2007-08 led to drying up of trading and liquidity crisis in the corporate bond markets, the auction rate securities markets, the wholesale (interbank lending) markets, the money markets, the markets for structured products, and even the otherwise liquid futures and forwards markets (as there was no liquidity available to fund the financing of futures). The one market that was not affected was the market for treasuries, in fact the flight to quality ensured that this market was very liquid (even though stressed from a pricing perspective as yields plummetted).
Therefore Choice 'a' is the correct answer.

NEW QUESTION # 152
When modeling operational risk using separate distributions for loss frequency and loss severity, which of the following is true?

A. Loss severity and loss frequency are considered independent
B. Loss severity and loss frequency distributions are considered as a bivariate model with positive correlation
C. Loss severity and loss frequency are modeled using the same units of measurement
D. Loss severity and loss frequency are modeled as conditional probabilities

Answer: A

Explanation:
When modeling operational loss frequency distribution (which, for example, may be based upon a Poisson distribution) and a loss severity distribution (for example, based upon a lognormal distribution), it is assumed that the frequency of losses and the severity of the losses are completely independent and do not impact each other. Therefore Choice 'a' is correct, and the others are not valid assumptions underlying the operational loss modeling.
Once each of these distributions has been built, a random number is drawn from each to determine a loss scenario. The process is repeated many times as part of a Monte Carlo simulation to get a the loss distribution.

NEW QUESTION # 153
Loss from a lawsuit from an employee due to physical harm caused while at work is categorized per Basel II as:

A. Employment practices and workplace safety
B. Unsafe working environment
C. Damage to physical assets
D. Execution delivery and process management

Answer: A

Explanation:
Choice 'a' is the correct answer. Refer to the detailed loss event type classification under Basel II (see Annex 9 of the accord). You should know the exact names of all loss event types, and examples of each.

NEW QUESTION # 154
Which of the following distribution assumptions will produce the lowest probability of exceeding an extreme value, assuming identical means and variances?

A. a distribution with kurtosis = 5
B. a normal mixture distribution
C. t-distribution
D. a normal distribution

Answer: D

Explanation:
An 'extreme value' will be a value that will lie in the tails. We need to determine the distribution that will have the least weight in the tails so that the probability of exceeding this tail value is minimum across the given choices.
The t-distribution, a distribution with kurtosis > 3 and a normal mixture distribution are all distributions with tails fatter than that for a normal distribution. A normal distribution will have the 'thinnest' tails among the choices and therefore the lowest probability of exceeding a given tail event value.
A note about the t-distribution: Leptokurtic distributions (those that have kurtosis>3, ie kurtosis greater than that for a normal distribution) generally appear to have higher peaks on their PDF graphs. The t-distribution is flatter, and actually appears lower than a normal distribution, which may make one think that it has a lower kurtosis and therefore should have thinner tails than a normal distribution. But that is not so, and the "visual" inspection test fails for inferring the kurtosis from just looking a the shape of the distribution. The kurtosis of a t-distribution is given by the formula {3 + 6/(d - 4)}, where d is the degrees of freedom and d > 4. Therefore the kurtosis of a t-distribution is always greater than 3 as "6/(d-4)" will always be a positive number being added to 3. Therefore there is no conflict between a t-distribution having fatter tails than a normal distribution as it has a higher kurtosis, even though it appears 'lower' on a graph when superimposed with a normal distribution.

NEW QUESTION # 155
......

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PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q150-Q155):

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