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New 8011 Trustworthy Pdf | Efficient Test 8011 Cram Pdf: Credit and Counterparty Manager (CCRM) Certificate Exam 100% Pass

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PRMIA 8011 Exam covers a wide range of topics related to credit risk management, including credit analysis, credit policies, credit risk measurement, counterparty credit risk, and regulatory requirements. 8011 course aims to equip professionals with the analytical tools and techniques required to manage credit risk effectively. Credit and Counterparty Manager (CCRM) Certificate Exam certification is suitable for professionals in the banking, investment, and finance industries, as well as for those who are interested in pursuing a career in credit risk management.

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PRMIA 8011 (Credit and Counterparty Manager (CCRM) Certificate) Exam is a globally recognized certification that demonstrates a professional's expertise in managing credit and counterparty risk. 8011 Exam evaluates a candidate's knowledge of principles, practices, and regulations in credit risk management, counterparty risk management, and credit derivatives.

PRMIA Credit and Counterparty Manager (CCRM) Certificate Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
Loss provisioning is intended to cover:

A. Both expected and unexpected losses
B. Expected losses
C. Unexpected losses
D. Losses in excess of unexpected losses

Answer: B

Explanation:
Loss provisioning is intended to cover expected losses. Economic capital is expected to cover unexpected losses. No capital or provisions are set aside for losses in excess of unexpected losses, which will ultimately be borne by equity.
Choice 'd' is the correct answer.

NEW QUESTION # 153
For identical mean and variance, which of the following distribution assumptions will provide a higher estimate of VaR at a high level of confidence?

A. A distribution with kurtosis = 2
B. A distribution with kurtosis = 3
C. A distribution with kurtosis = 0
D. A distribution with kurtosis = 8

Answer: D

Explanation:
A fat tailed distribution has more weight in the tails, and therefore at a high level of confidence the VaR estimate will be higher for a distribution with heavier tails. At relatively lower levels of confidence however, the situation is reversed as the heavier tailed distribution will have a VaR estimate lower than a thinner tailed distribution.
A higher level of kurtosis implies a 'peaked' distribution with fatter tails. Among the given choices, a distribution with kurtosis equal to 8 will have the heaviest tails, and therefore a higher VaRestimate. Choice 'a' is therefore the correct answer. Also refer to the tutorial about VaR and fat tails.

NEW QUESTION # 154
Which of the following formulae describes CVA (Credit Valuation Adjustment)? All acronyms have their usual meanings (LGD=Loss Given Default, ENE=Expected Negative Exposure, EE=Expected Exposure, PD=Probability of Default, EPE=Expected Positive Exposure, PFE=Potential Future Exposure)

A. LGD * PFE * PD
B. LGD * EPE * PD
C. LGD * ENE * PD
D. LGD * EE * PD

Answer: B

Explanation:
The correct definition of CVA is LGD * EPE * PD. All other answers are incorrect.
CVA reflects the adjustment for counterparty default on derivative and other trading book transactions. This reflects the credit charge, that neeeds to be reduced from the expected value of the transaction to determine its true value. It is calculated as a product of the loss given default, the probability of default and the average weighted exposure of future EPEs across the time horizon for the transaction.
The future exposures need to be discounted to the present, and occasionally the equations for CVA will state that explicitly. Similarly, in some more advanced dynamic models the correlation between EPE and PD is also accounted for. The conceptual ideal though remains the same: CVA=LGD*EPE*PD.

NEW QUESTION # 155
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 # 156
Which of the following statements are true in relation to Historical Simulation VaR?
I. Historical Simulation VaR assumes returns are normally distributed but have fat tails II. It uses full revaluation, as opposed to delta or delta-gamma approximations III. A correlation matrix is constructed using historical scenarios IV. It particularly suits new products that may not have a long time series of historical data available

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

Answer: C

Explanation:
Historical Simulation VaR is conceptually very straightforward: actual prices as seen during the observation period (1 year, 2 years, or other) become the 'scenarios' forming the basis of the valuation of the portfolio. For each scenario, full revaluation is performed, and a P&L data set becomes available from which the desired loss quantile can be extracted.
Historical simulation is based upon actually seen prices over a selected historical period, therefore no distributional assumptions are required. The data is what the data is, and is the distribution. Statement I is therefore not correct.
It uses full revaluation for each historical scenario, therefore statement II is correct.
Since the prices are taken from actual historical observations, a correlation matrix is not required at all.
Statement III is therefore incorrect (it would be true for Monte Carlo and parametric Var).
Historical simulation VaR suffers from the limitation that if enough representative data points are no available during the historical observation period from which the scenarios are drawn, the results would be inaccurate.
This is likely to be the case for new products. Therefore Statement IV is incorrect.

NEW QUESTION # 157
......

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Hal Gray Hal Gray

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