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8011 Valid Exam Practice Exam | 8011: Credit and Counterparty Manager (CCRM) Certificate Exam–100% free

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PRMIA 8011 Credit and Counterparty Manager (CCRM) Certificate Exam is a highly respected certification that is valued by employers and recognized globally as a mark of excellence in the field of credit and counterparty risk management. 8011 exam is designed to test an individual's knowledge and understanding of the key principles and practices of credit risk management and counterparty credit risk management, and is open to professionals who meet the eligibility requirements. With its comprehensive syllabus, rigorous testing, and global recognition, the PRMIA 8011 CCRM certification is an essential credential for anyone looking to advance their career in risk management.

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PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q291-Q296):

NEW QUESTION # 291
An investor holds a bond portfolio with three bonds with a modified duration of 5, 10 and 12 years respectively. The bonds are currently valued at $100, $120 and $150. If the daily volatility of interest rates is
2%, what is the 1-day VaR of the portfolio at a 95% confidence level?

A. 163.11
B. 0
C. 115.51
D. 1

Answer: C

Explanation:
The total value of the portfolio is $370 (=$100 + $120 + $150). The modified duration of the portfolio is the weighted average of the MDs of the different bonds, ie =(5 * 100/370) + (10 * 120/370) + (12 * 150/370) =
9.46.
This means that for every 1% change in interest rates, the value of the portfolio changes by 9.46%. Since the daily volatility of interest rates is 2%, the 95% confidence level move will be 1.65 * 2% = 3.30%. Thus, the VaR of the portfolio at the 95% confidence level will be 3.3 * 9.46% * $370 = $115.51.
All other answers are incorrect.

NEW QUESTION # 292
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. All of the above
B. I and IV
C. II and III
D. I, II and III

Answer: A

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 # 293
In the case of historical volatility weighted VaR, a higher current volatility when compared to historical volatility:

A. will decrease the VaR estimate
B. will increase the confidence interval
C. will not affect the VaR estimate
D. will increase the VaR estimate

Answer: D

Explanation:
When calculating volatility weighted VaR, returns are adjusted by a factor equal to the current volatility divided by the historical volatility, ie the volatility that existed during the time period the returns were earned.
If the current volatility is greater than the historical volatility (also called contemporary volatility), then it has the effect of increasing the magnitude of any past returns (whether positive or negative). This in turn increases the VaR.
Consider an example: if the current volatility is 2%, and a return of -5% was earned at a time when the volatility was 0.8%, then the volatility weighted return would be 12.5% (=-5% x 2%/0.8%). Clearly, this has the effect of increasing the VaR.
Choice 'd' is therefore the correct answer.

NEW QUESTION # 294
Which of the following statements are true:
I. A high score according to Altman's Z-Score methodology indicates a lower default risk II. A high score according to the Probit or Logit models indicates a higher default risk III. A high score according to Altman's Z-Score methodology indicates a higher default risk IV. A high score according to the Probit or Logit models indicates a lower default risk

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

Answer: C

Explanation:
A high score under the probit and logit models indicates a higher default risk, while under Altman's methodology it indicates a lower default risk. Therefore Choice 'd' is the correct answer.

NEW QUESTION # 295
Which of the following statements is correct?

A. Market liquidity risk is idiosyncratic while funding liquidity risk is not
B. Dynamic simulations of liquidity needs require an assumption of counterparty risk remaining constant
C. Market liquidity risks present themselves in the form of higher bid offer spreads
D. Funding liquidity risks present themselves in the form of an adverse market impact on prices from a trade

Answer: C

Explanation:
Simulations of liquidity needs can be of various types: historical simulations, where the current positions are subjected to the kind of liquidity shocks experienced in the past; static simulations, where a static view of current positions, counterparty credit position, and the business is considered; and dynamic simulations where all factors are dynamically changed including counterparty credit standing, changes to the current portfolio and behavioural aspects of the business. Choice 'b' is incorrect as dynamic simulations require no such assumptions.
Liquidity risk is often thought of in terms of market liquidity risk and funding liquidity risk. Market liquidity risk relates to the the liquidity for a particular type of asset drying up. For example, during the 2007-2009 crisis a large number of corporate bonds and structured products became extremely illiquid. Market liquidity risk manifests itself in the form of higher bid offer spreads, higher pricde impact, and a reduction in the normal market size (ie, the 'normal' size of a trade for which a dealer quote is valid for). Therefore Choice 'd' is correct. Similarly, Choice 'a' is incorrect as adverse price impact results from market liquidity risk and not funding liquidity risk.
Market liquidity risk applies to the entire market and all its participants. It is not idiosyncratic. Therefore Choice 'c' is incorrect too. Funding liquidity risk on the other hand applies to an individual institution that is under liquidity stress in the sense of not being able to meet its obligations such as margin or collateral calls because of a lack of liquid assets. Thus it is funding liquidity that is idiosyncratic. Market liquidity risk often leads to funding liquidity risks materializing as firms are unable to get to the funds they were relying upon due to assets becoming illiquid.

NEW QUESTION # 296
......

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