8011 Exam Dumps Free Test Engine Verified By PRMIA Certification Certified Experts [Q184-Q198]

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PRMIA 8011 exam is a challenging and rigorous certification that requires extensive preparation and study. Candidates who pass the exam will be awarded the PRMIA CCRM Certificate, which is a valuable credential that can help enhance their career prospects and demonstrate their commitment to the highest standards of risk management practices. Overall, the PRMIA 8011 exam is an excellent option for professionals who are looking to enhance their knowledge and expertise in credit and counterparty risk management.

Q184. Which of the following will be a loss not covered by operational risk as defined under Basel II?

Earthquakes

Fat finger losses

Systems failure

Strategic planning

Q185. A bullet bond and an amortizing loan are issued at the same time with the same maturity and with the same principal. Which of these would have a greater credit exposure halfway through their life?

Indeterminate with the given information

They would have identical exposure half way through their lives

The amortizing loan

The bullet bond

Q186. The definition of operational risk per Basel II includes which of the following:
I. Risk of loss resulting from inadequate or failed internal processes, people and systems or from external events II. Legal risk III. Strategic risk IV. Reputational risk

I, II, III and IV

II and III

I and III

I and II

Q187. In January, a bank buys a basket of mortgages with a view to securitize them by April. Due to an unexpected lack of investors in the securitization market, it is unable to do so and is left with the exposure to the mortgages on its books. This is an example of:

Market risk

Wrong-way risk

Basis risk

Pipeline and warehousing risk

Q188. Which of the following statements are true:
I. Top down approaches help focus management attention on the frequency and severity of loss events, while bottom up approaches do not.
II. Top down approaches rely upon high level data while bottom up approaches need firm specific risk data to estimate risk.
III. Scenario analysis can help capture both qualitative and quantitative dimensions of operational risk.

III only

II and III

I only

II only

Q189. Under the contingent claims approach to measuring credit risk, which of the following factors does NOT affect credit risk:

Cash flows of the firm

Maturity of the debt

Volatility of the firm’s asset values

Leverage in the capital structure

Q190. The daily VaR of an investor’s commodity position is $10m. The annual VaR, assuming daily returns are independent, is ~$158m (using the square root of time rule). Which of the following statements are correct?
I. If daily returns are not independent and show mean-reversion, the actual annual VaR will be higher than
$158m.
II. If daily returns are not independent and show mean-reversion, the actual annual VaR will be lower than
$158m.
III. If daily returns are not independent and exhibit trending (autocorrelation), the actual annual VaR will be higher than $158m.
III. If daily returns are not independent and exhibit trending (autocorrelation), the actual annual VaR will be lower than $158m.

I and IV

I and III

II and III

II and IV

Q191. Under the credit migration approach to assessing portfolio credit risk, which of the following are needed to generate a distribution of future portfolio values?

The forward yield curve

A specified risk horizon

A rating migration matrix

All of the above

Q192. Conditional default probabilities modeled under CreditPortfolio view use a:

Power function

Altman’s z-score

Probit function

Logit function

Q193. Under the CreditPortfolio View approach to credit risk modeling, which of the following bestdescribes the conditional transition matrix:

The conditional transition matrix is the unconditional transition matrix adjusted for the state of the economy and other macro economic factors being modeled

The conditional transition matrix is the transition matrix adjusted for the risk horizon being different from that of the transition matrix

The conditional transition matrix is the unconditional transition matrix adjusted for probabilities of defaults

The conditional transition matrix is the transition matrix adjusted for the distribution of the firms’ asset returns

Q194. The probability of default of a security during the first year after issuance is 3%, that during the second and third years is 4%, and during the fourth year is 5%. What is the probability that it would not have defaulted at the end of four years from now?

12.00%

88.53%

88.00%

84.93%

Q195. The key difference between ‘top down models’ and ‘bottom up models’ for operational risk assessment is:

Top down approaches to operational risk are based upon an analysis of key risk drivers, while bottom up approaches consider causality in risk scenarios.

Bottom up approaches to operational risk are based upon an analysis of key risk drivers, while top down approaches consider causality in risk scenarios.

Bottom up approaches to operational risk calculate the implied operational risk using available data such as income volatility, capital etc; while top down approaches use causal factors, risk drivers and other factors to get an aggregated estimate of risk.

Top down approaches to operational risk calculate the implied operational risk using available data such as income volatility, capital etc; while bottom up approaches use causal factors, risk drivers and other factors to get an aggregated estimate of risk.

Q196. Which of the following best describes Altman’s Z-score

A calculation of default probabilities

A regression of probability of survival against a given set of factors

A numerical computation based upon accounting ratios

A standardized z based upon the normal distribution

Q197. Which of the following are valid approaches for extreme value analysis given a dataset:
I. The Block Maxima approach
II. Least squares approach
III. Maximum likelihood approach
IV. Peak-over-thresholds approach

II and III

I, III and IV

I and IV

All of the above

Q198. The accuracy of a VaR estimate based on a Monte carlo simulation of portfolio prices is affected by:
I). The shape of the distribution of portfolio values
II). The number simulations carried out
III). The confidence level selected for the VaR estimate

III

I, II and III

II and III

VaR calculations look at the lower part of the distribution of future portfolio values, for example, if the desired confidence level is 95%, the cut-off for the VaR calculation will be at the bottom 5%; similarly at 1% for a 99% confidence level. The number of observations that will end up in these bottom ranges will be few and sparse, and therefore their accuracy will generally be lower than, say, the average where observations are more likely to be concentratred. If the shape of the distribution of future portfolio values is not symmetrical and has a long tail to the left, then this problem gets further exacerbated as there may be even fewer and less reliable simulated numbers at the 5% or 1% quintiles. Thus the shape of the distribution will affect the accuracy of a VaR estimate. The distribution for a short option position, for example, will have a long tail to the left, and the VaR number will be quite significantly affected by a few simulations. On the other hand, for a long option position where the long tail is to the right, and we are interested in the left tail which is better defined and ends at zero we are more likely to get a better VaR estimate. Therefore Statement I is correct.
The number of simulations carried out directly affects the standard error, which is inversely proportional to the square root of the sample size (ie the number of simulations). THe accuracy of the VaR estimate can be increased by increasing the sample size (or reduced by reducing the sample size). Therefore Statement II is correct.
The confidence level selected for the VaR estimate also affects the accuracy of the estimate. Tointuitively understand this, consider this extreme example where the desired confidence level is 99.9% and there are
1000 observations. Therefore the VaR will be determined by the last value in the sample, and will therefore be quite fickle and dependent upon what chance produces as the lowest value in the simulation. But if for the same sample the confidence level desired were to be 90%, there would be 100 observations beyond the 90% cut-off and this would be a much more stable and accurate number. Therefore the confidence level selected for the VaR estimate is also a determinant of the accuracy of the VaR estimate derived from the simulation.
Statement III is correct.
Thus all statements are correct and Choice ‘b’ is the correct answer.

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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