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These mock tests are specially built for you to assess what you have studied. These Credit and Counterparty Manager (CCRM) Certificate Exam (8011) practice tests are customizable, which means you can change the time and questions according to your needs. Taking practice exams teaches you time management so you can pass the Credit and Counterparty Manager (CCRM) Certificate Exam (8011) exam. DumpsActual's 8011 practice exam makes an image of a real-based examination which is helpful for you to not feel much pressure when you are giving the final examination.

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Latest 8011 Practice Questions | Latest 8011 Dumps Files

To pass the 8011 exam, you must put in a lot of time studying, practicing, and working hard. You will need real PRMIA 8011 Questions and the necessary understanding of the exam's format to pass the 8011 test. Without preparing with actual Credit and Counterparty Manager (CCRM) Certificate Exam (8011) questions, applicants find it difficult to get the knowledge essential to pass the PRMIA certification exam in a short time.

PRMIA 8011 Credit and Counterparty Manager (CCRM) Certificate is a comprehensive certification program that provides professionals with the skills and knowledge necessary to manage credit and counterparty risk in financial institutions. The CCRM exam covers a range of topics related to credit and counterparty risk management, and passing the exam demonstrates a candidate's knowledge and expertise in these areas. Credit and Counterparty Manager (CCRM) Certificate Exam certification is highly regarded in the financial industry and provides professionals with a competitive edge in the job market.

PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q192-Q197):

NEW QUESTION # 192
Which of the following statements is true:
I. When averaging quantiles of two Pareto distributions, the quantiles of the averaged models are equal to the geometric average of the quantiles of the original models based upon the number of data items in each original model.
II. When modeling severity distributions, we can only use distributions which have fewer parameters than the number of datapoints we are modeling from.
III. If an internal loss data based model covers the same risks as a scenario based model, they can can be combined using the weighted average of their parameters.
IV If an internal loss model and a scenario based model address different risks, the models can be combined by taking their sums.

A. III and IV
B. I and II
C. All statements are true
D. II and III

Answer: C

Explanation:
Statement I is true, the quantiles of the averaged models are equal to the geometric average of the quantiles of the original models.
Statement II is correct, the number of data points from which model parameters are estimated must be greater than the number of parameters. So if a distribution, say Poisson, has one parameter, we need at least two data points to estimate the parameter. Other complex distributions may have multiple parameters for shape, scale and other things, and the minimum number of observations required will be greater than the number of parameters.
Statement III is true, if the ILD data and scenarios cover the same risk, they are essentially different perspectives on the same risk, and therefore should be combined as weighted averages.
But if they cover completely different risks, the models will need to be added together, not averaged - which is why Statement IV is true.

NEW QUESTION # 193
Which of the following belong in a credit risk report?

A. Largest exposures by counterparty
B. Exposures by country
C. Exposures by industry
D. All of the above

Answer: D

Explanation:
All the listed variables are relevant to management monitoring the credit risk profile of an institution, therefore Choice 'd' is the correct answer.

NEW QUESTION # 194
Under the KMV Moody's approach to credit risk measurement, how is the distance to default converted to expected default frequencies?

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

Answer: B

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

NEW QUESTION # 195
Which of the following are attributes of a robust stress testing programme at a bank?

A. Data of appropriate quality and granularity
B. Robust systems infrastructure
C. Written policies and procedures
D. All of the above

Answer: D

Explanation:
A bank's stress testing programme in relation to firm wide stress tests should document the type, frequency and the purpose of the programme, as well as methodologies for defining scenarios and the remedial actions envisaged. Choice 'b' is therefore a necessary attribute of a robust stress testing programme.
The programme should be supported by a robust systems infrastructure that allows the execution of periodic as well as ad-hoc stress tests at the right level (business unit, as well as firm-wide) at the right level of detail or granularity. Choice 'c' also therefore is a valid choice.
A related element is data quality - without which no stress tests can be be credible.
Therefore all the choices listed are correct and Choice 'd' is the correct answer.

NEW QUESTION # 196
Which of the following situations are not suitable for applying parametric VaR:
I. Where the portfolio's valuation is linearly dependent upon risk factors II. Where the portfolio consists of non-linear products such as options and large moves are involved III. Where the returns of risk factors are known to be not normally distributed

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

Answer: B

Explanation:
Parametric VaR relies upon reducing a portfolio's positions to risk factors, and estimating the first order changes in portfolio values from each of the risk factors. This is called the delta approximation approach. Risk factors include stock index values, or the PV01 for interest rate products, or volatility for options. This approach can be quite accurate and computationally efficient if the portfolio comprises products whose value behaves linearly to changes in risk factors. This includes long and short positions in equities, commodities and the like.
However, where non-linear products such as options are involved and large moves in the risk factors are anticipated, a delta approximation based valuation may not give accurate results, and the VaR may be misstated. Therefore in such situations parametric VaR is not advised (unless it is extended to include second and third level sensitivities which can bring its own share of problems).
Parametric VaR also assumes that the returns of risk factors are normally distributed - an assumption that is violated in times of market stress. So if it is known that the risk factor returns are not normally distributed, it is not advisable to use parametric VaR.

NEW QUESTION # 197
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