Vce 8011 File, 8011 Exam Tutorials

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PRMIA 8011 Certification Exam is a rigorous exam that requires a significant amount of preparation and study. 8011 exam consists of 100 multiple-choice questions and is three hours in length. To pass the exam, individuals must achieve a score of at least 70%. 8011 exam is administered at Pearson VUE testing centers around the world, making it accessible to individuals in many different locations.

PRMIA 8011 exam covers a wide range of topics related to credit risk and counterparty risk management, including credit analysis, credit risk modeling, credit derivatives, counterparty risk management, and regulatory frameworks. 8011 Exam is divided into two parts, with the first part consisting of multiple-choice questions and the second part consisting of case studies and essay questions. 8011 exam is designed to be challenging, and candidates are expected to have a solid understanding of the principles and practices of credit risk and counterparty risk management in order to pass.

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PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
Which of the following are valid methods for selecting an appropriate model from the model space for severity estimation:
I. Cross-validation method
II. Bootstrap method
III. Complexity penalty method
IV. Maximum likelihood estimation method

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

Answer: D

Explanation:
Once we have a number of distributions in the model space, the task is to select the "best" distribution that is likely to be a good estimate of true severity. We have a number of distributions to pick from, an empirical dataset (from internal or external losses), and we can estimate the parameters for the different distributions.
We then have to decide which distribution to pick, and that generally requires considering both approximation and fitting errors.
There are three methods that are generally used for selecting a model:
1. The cross-validation method: This method divides the available data into two parts - the training set, and the validation set (the validation set is also called the 'testing set'). Parameter estimation for each distribution is done using the training set, and differences are then calculated based on the validation set. Though the temptation may be to use the entire data set to estimate the parameters, that is likely to result in what may appear to be an excellent fit to the data on which it is based, but without any validation. So we estimate the parameters based on one part of the data (the training set), and check the differences we get from the remaining data (the validation set).
2. Complexity penalty method: This is similar to the cross-validation method, but with an additional consideration of the complexity of the model. This is because more complex models are likely to produce a more exact fit than simpler models, this may be a spurious thing - and therefore a 'penalty' is added to the more complex models as to favor simplicity over complexity. The 'complexity' of a model may be measured by the number of parameters it has, for example, a log-normal distribution has only two parameters while a body-tail distribution combining two different distributions may have many more.
3. The bootstrap method: The bootstrap method estimates fitting error by drawing samples from the empirical loss dataset, or the fit already obtained, and then estimating parameters for each draw which are compared using some statistical technique. If the samples are drawn from the loss dataset, the technique is called a non- parametric bootstrap, and if the sample is drawn from an estimated model distribution, it is called a parametric bootstrap.
4. Using goodness of fit statistics: The candidate fits can be compared using MLE based on the KS distance, for example, and the best one selected. Maximum likelihood estimation is a technique that attempts to maximize the likelihood of the estimate to be as close to the true value of the parameter. It is a general purpose statistical technique that can be used for parameter estimation technique, as well as for deciding which distribution to use from the model space.
All the choices listed are the correct answer.

NEW QUESTION # 66
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. II and III
C. I and II
D. I, II and IV

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 # 67
Which of the following risks were not covered in detail in most stress tests prior to the current crisis:
I. The behavior of complex structured products under stressed liquidity conditions II. Pipeline or securitization risk III. Basis risk in relation to hedging strategies IV. Counterparty credit risk
V. Contingent risks
VI. Funding liquidity risk

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

Answer: D

Explanation:
The BCBS publication 'Principles for sound stress testing practices and supervision' (May 2009) identifies all of the above as risks that were covered in insufficient detail in most stress tests prior to the current crisis.
Therefore Choice 'd' is the correct answer.
For the PRM exam, you should have read this document. You should also be familiar with all the above risk types as being contributors to the crisis, and know what each of these mean.

NEW QUESTION # 68
Which of the following statements are true:
I. Stress tests should consider simultaneous pressures in funding and asset markets, and the impact of a reduction in liquidity II. Judging the effectiveness of risk mitigation techniques is not a part of stress testing III. A reverse stress test is useful for discovering hidden vulnerabilities and inconsistencies in hedging strategies IV. Reputational risk, which is explicitly excluded from the definition of operational risk under Basel II, should still be considered as part of stress tests.

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

Answer: B

Explanation:
All the statements in this question are directly based on the principles for effective stress testing as laid down in the BCBS document on stress testing issued in May 2009. Statement 1 is correct and is an almost verbatim reproduction of principle 10 as laid down in that document. Statement II is incorrect as it is contrary to principle 11 laid down in the same document. Statement III is correct as discovering hidden vulnerabilities and inconsistencies in hedging strategies is one of the objectives of reverse stress tests. Similarly, even though reputational risk is not really covered under any risk category under Basel II (as it is not a part of either market, credit or operational risk), principle 14of this paper requires the mitigation of spill-over effects on market confidence of reputational risk when thinking about stress tests.
Thus statements I, III and IV are correct and statement II is incorrect.

NEW QUESTION # 69
A bank expects the error rate in transaction data entry for a particular business process to be 0.005%. What is the range of expected errors in a day within +/- 2 standard deviations if there are 2,000,000 such transactions each day?

A. 60 to 80 errors in a day
B. 80 to 120 errors in a day
C. 90 to 110 errors in a day
D. 0 to 200 errors in a day

Answer: B

Explanation:
Error rates are generally modeled using the Poisson distribution. Recall that the Poisson distribution has only one parameter -#- which is its mean and also its variance.
In the given case, the mean number of errors is 2,000,000 x 0.005% = 100. Since this is the variance as well, the standard deviation is #100 = 10. Therefore the range of outcomes within 2 standard deviations of the mean is 100 +/- (2*10) = 80 to 120 errors in a day.

NEW QUESTION # 70
......

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Vce 8011 File, 8011 Exam Tutorials

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PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q65-Q70):

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