UPDATED PRMIA 8011 EXAM QUESTIONS–KEY TO YOUR CAREER GROWTH

Updated PRMIA 8011 Exam Questions–Key to Your Career Growth

Updated PRMIA 8011 Exam Questions–Key to Your Career Growth

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Tags: 8011 Valid Exam Simulator, 8011 Examinations Actual Questions, New 8011 Test Tips, 8011 Reliable Test Prep, New 8011 Exam Review

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Free PDF Quiz High Hit-Rate PRMIA - 8011 - Credit and Counterparty Manager (CCRM) Certificate Exam Valid Exam Simulator

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PRMIA 8011: Credit and Counterparty Manager (CCRM) Certificate Exam is designed to test the knowledge and skills of professionals in credit and counterparty risk management. The test assesses the comprehension of the concepts and technical analysis involved in credit risk management and the application of that knowledge to real-life scenarios. 8011 Exam covers topics such as credit analysis techniques, credit risk models, risk quantification and measurement, asset quality assessment, portfolio optimization, and counterparty risk management.

PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
The difference between true severity and the best approximation of the true severity is called:

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

Answer: D

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 # 53
Ex-ante VaR estimates may differ from realized P&L due to:
I. the effect of intra day trading
II. timing differences in the accounting systems
III. incorrect estimation of VaR parameters
IV. security returns exhibiting mean reversion

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

Answer: A

Explanation:
Ex-ante VaR calculations can differ from actual realized P&L due to a large number of reasons. I, II and III represent some of them. Mean reversion however has nothing to do with VaR estimates differing from actual P&L. Therefore Choice 'c' is the correct answer.


NEW QUESTION # 54
Under the KMV Moody's approach to credit risk measurement, how is the distance to default converted to expected default frequencies?

  • A. Using Monte Carlo simulations
  • B. Using migration matrices
  • C. Using a proprietary database based on historical information
  • D. Using a normal distribution

Answer: C

Explanation:
KMV Moody's uses a proprietary database to convert the distance to default to expected default probabilities.


NEW QUESTION # 55
Calculate the 1-year 99% credit VaR of a portfolio of two bonds, each with a value of $1m, and the probability of default of 1% each over the next year. Assume the recovery rate to be zero, and the defaults of the two bonds to be uncorrelated to each other.

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: B

Explanation:
This question requires the calculation of the credit VaR of the bonds - note that in the real exam the question may not refer to 'credit' VaR, but that can be inferred from the context, ie because the probability of default is provided, it can only be asking for the credit VaR. (Note the difference from the market risk VaR which is driven by interest rate changes affecting the value of the bonds - there are other questions addressing that calculation).
Credit VaR = Expected Value - Worst case portfolio value at the selected percentile (ie the confidence level) Thus if we know the distribution of the portfolio value in the future, we can find out the value at the required percentile (in this case 99%), and the VaR will be the difference between this value and the expected value of the portfolio.
An important piece of information provided is that the defaults are independent, ie they are not correlated.
This means joint probabilities of default or survival can be easily found by multiplying the relevant probabilities. The following outcomes are possible:
1. Both bonds default: Probability = 1% * 1% = 0.01%. Portfolio value = $0 (because both bonds have defaulted & there is zero recovery)
2. One bond defaults and the other survives: Probability = 2 * 1% * 99% = 1.98%. Portfolio value = $1m (because one bond survives with a value of $1m and the defaulted bond has a value of $0). (Note that because there are two ways in which this can happen, ie bond 1 defaults, bond 2 survives; and bond 1 survives, bond 2 defaults, we need to multiply the probability by 2).
3. Both bonds survive: Probability = 99% * 99% = 98.01%. Portfolio value = $2m.
Expected value is therefore $1.98m (which is equal to 2 * $1m * (1 - 1%), or alternatively can also be obtained by multiplying the probabilities in the above three outcomes with the value associated with each).
The future distribution of the value of the portfolio can be constructed from the three outcomes outlined above:
a. Upto the 98.01th percentile the value of the portfolio is $2m, and the VaR is zero (being greater than the expected value, so there is nothing to lose) b. From the 98.01th percentile to the 99.99th percentile (98.01+ the next 1.98%), the value of the portfolio is
$1m. VaR in this range is $0.98m (=$1.98m - $1m)
c. From the 99.99th to the 100th percentile the value of the portfolio is $0, and the VaR is $1.98m.
Since the question is asking for VaR at the 99% confidence level, it lies in the range in 'b' above, and therefore the VaR is $0.98m.
Therefore Choice 'c' is the correct answer and the rest are incorrect.


NEW QUESTION # 56
What does a middle office do for a trading desk?

  • A. Reconciliations
  • B. Operations
  • C. Transaction data entry
  • D. Risk analysis

Answer: D

Explanation:
The 'middle office' is a term used for the risk management function, therefore Choice 'd' is the correct answers. The other functions describe what the 'back office' does (IT, accounting). The 'front office' includes the traders.


NEW QUESTION # 57
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