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8011 Certification - 8011 Latest Test Labs

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PRMIA 8011 CCRM Certificate exam is an essential certification for professionals who desire to work in the highly competitive field of risk management. The certificate offers a competitive advantage by providing candidates with a deep understanding of the latest techniques in credit and counterparty risk management. 8011 Exam equips candidates with the skills necessary to identify and evaluate risks associated with credit and counterparty transactions, enabling them to make informed decisions and contribute to the success of their organization.

PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
Which of the following is not a possible early warning indicator in relation to the health of a counterparty?

A. Negative publicity
B. Falling stock price
C. A decline in the counterparty's corporate debt yield
D. Credit rating downgrade

Answer: C

Explanation:
Negative publicity, a downgrade in the credit rating, a falling stock price are all pointers to potential credit problems, and the counterparty credit monitoring group of a bank should be using these as possible early indicators of an upcoming credit health problem. A decline in the yield of the debt issued by a counterparty means its spread is declining and the health of the credit is actually improving. Therefore a decline in the counterparty's corporate debt yield cannot be used as an indicator of potential credit problems.
Choice 'c' is therefore the correct answer.

NEW QUESTION # 33
If the odds of default are 1:5, what is the probability of default?

A. 20.00%
B. 16.67%
C. 50.00%
D. 12.00%

Answer: B

Explanation:
Odds are the ratio between the probability of the occurence of an event to the probability that the event does not occur.
If odds are H, then p = H/(1 + H) and H = p/(1-p). In this case the odds are 1:5, or 1/5, therefore the correct answer is Choice 'a', equal to (1/5)/(1 + 1/5) = 1/6 = 16.67%. All other choices are incorrect.

NEW QUESTION # 34
Regulatory arbitrage refers to:

A. the practice of investing and financing decisions being driven by associated regulatory capital requirements as opposed to the true underlying economics of these decisions
B. the practice of structuring a financial institution's business as a bank holding company to arbitrage the differing capital and credit rating requirements for different business lines
C. the practice of transferring business and profits to jurisdictions (such as those in other countries) to avoid or reduce capital adequacy requirements
D. All of the above

Answer: A

Explanation:
The correct answer is Choice 'c'. The other choices do not refer to 'regulatory arbitrage'.

NEW QUESTION # 35
When pricing credit risk for an exposure, which of the following is a better measure than the others:

A. Mark-to-market
B. Expected Exposure (EE)
C. Notional amount
D. Potential Future Exposure (PFE)

Answer: B

Explanation:
Exposure for derivative instruments can vary significantly over the lifetime of the instrument, depending upon how the market moves. The potential future exposure represents the extremes, notthe most likely outcome.
The expected exposure is the most suitable measure for pricing the credit risk. Over time, as multiple transactions are entered into, the expectation (or the mean) will be realized - though individual transactions may have more or less by way of exposure.
The notional amount may not be relevant, though for loans it may be the most important contributor to the expected exposure. Mark-to-market will represent the exposure at a given point in time, but cannot be predicted nor be used to price the credit risk.

NEW QUESTION # 36
The difference between true severity and the best approximation of the true severity is called:

A. Fitting error
B. Estimation error
C. Approximation error
D. Total error

Answer: C

Explanation:
This question relates to fitting a distribution to the true severity of the operational risk loss we are trying to model. The quality of the fit, or the precision of the fit, has two elements to the difference between the severity as represented by our model and the true severity. To understand this, consider the three data points below:
a. The true severity,
b. The best approximation of the true severity in the model space, and
c. The fit based on the dataset.
- True severity is what we are trying to model.
- The model space refers to the collection of analytical distributions (log-normal, burr etc) that we are considering to arrive at the estimate of the severity.
- The 'best approximation of the true severity in the model space' is reached by estimating the parameters of the distribution that optimizes the risk functional.
- The 'fit' is the actual parameter estimates we settle for with the distribution we have determined best fits the true estimate of our severity. When estimating parameters, we have various methods available for estimation - the least squares method, the maximum likelihood method, for example, and we can get different estimates depending upon the method we choose to use.
Our severity model will be different from the true severity, and the total difference can be split into two types of errors:
1. Fitting error, represented by 'c - b' above: The difference between the fit based on the dataset and the best approximation of the true severity is called 'fitting error', ie, a measure of the extent to which we could have estimated the parameters better.
2. Approximation error, represented by 'b - a' above: Approximation error is the difference between the true severity, and the best approximation of the true severity that can be achieved within the model space is called
'approximation error'.
One can reduce the approximation error by expanding the model space by adding more distributions. This will reduce the approximation error, but generally has the effect of increasing the fitting error because the complexity of the model space increases, and there are more ways to fit to the true severity.

NEW QUESTION # 37
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