(2025) PASS 8011 Exam Free Practice Test with 100% Accurate Answers [Q25-Q42]

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(2025) PASS 8011 Exam Free Practice Test with 100% Accurate Answers

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PRMIA 8011 Certification Exam is a professional certification program designed for professionals seeking to demonstrate their knowledge and expertise in credit and counterparty risk management. Credit and Counterparty Manager (CCRM) Certificate Exam certification program is offered by the Professional Risk Managers’ International Association (PRMIA), a non-profit organization dedicated to advancing the practice of risk management across all industries and sectors.

Q25. If A and B be two uncorrelated securities, VaR(A) and VaR(B) be their values-at-risk, then which of the following is true for a portfolio that includes A and B in any proportion. Assume the prices of A and B are log- normally distributed.

VaR(A+B) > VaR(A) + VaR(B)

VaR(A+B) = VaR(A) + VaR(B)

VaR(A+B) < VaR(A) + VaR(B)

The combined VaR cannot be predicted till the correlation is known

Q26. Under the standardized approach to calculating operational risk capital under Basel II, negative regulatory capital charges for any of the business units:

Should be ignored completely

Should be offset against positive capital charges from other business units

Should be included after ignoring the negative sign

Should be excluded from capital calculations

Q27. Which of the following cannot be used as an internal credit rating model to assess an individual borrower:

Distance to default model

Probit model

Logit model

Altman’s Z-score

Q28. Which of the following should be included when calculating the Gross Income indicator used to calculate operational risk capital under the basic indicator and standardized approaches under Basel II?

Insurance income

Operating expenses

Fees paid to outsourcing service proviers

Net non-interest income

Q29. Loss from a lawsuit from an employee due to physical harm caused while at work is categorized per Basel II as:

Employment practices and workplace safety

Execution delivery and process management

Unsafe working environment

Damage to physical assets

Q30. Conditional VaR refers to:

expected average losses conditional on the VaR estimates not being exceeded

value at risk when certain conditions are satisfied

expected average losses above a given VaR estimate

the value at risk estimate for non-normal distributions

Q31. When building a operational loss distribution by combining a loss frequency distribution and a loss severity distribution, it is assumed that:
I. The severity of losses is conditional upon the number of loss events II. The frequency of losses is independent from the severity of the losses III. Both the frequency and severity of loss events are dependent upon the state of internal controls in the bank

I, II and III

II and III

I and II

Q32. Fill in the blank in the following sentence:
Principal component analysis (PCA) is a statistical tool to decompose a ____________ matrix into its principal components and is useful in risk management to reduce dimensions.

Covariance

Correlation

Volatility

Positive semi-definite

Q33. Which of the following is not a credit event under ISDA definitions?

Restructuring

Obligation accelerations

Rating downgrade

Failure to pay

Q34. Which of the following is a valid approach to determining the magnitude of a shock for a given risk factor as part of a historical stress testing exercise?
I. Determine the maximum peak-to-trough change in the risk factor over the defined period of the historical event II. Determine the minimum peak-to-trough change in the risk factor over the defined period of the historical event III. Determine the total change in the risk factor between the start date and the finish date of the event regardless of peaks and troughs in between IV. Determine the maximum single day change in the risk factor and multiply by the number of days covered by the stress event

II and IV

I and III

IV only

I, II and IV

Q35. 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

II and III

I, II and III

I and IV

All of the above

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.

Q36. Which of the following assumptions underlie the ‘square root of time’ rule used for computing VaR estimates over different time horizons?
I. the portfolio is static from day to day
II. asset returns are independent and identically distributed (i.i.d.)
III. volatility is constant over time
IV. no serial correlation in the forward projection of volatility
V. negative serial correlations exist in the time series of returns
VI. returns data display volatility clustering

III, IV, V and VI

I, II, V and VI

I, II, III and IV

I and II

Q37. If P be the transition matrix for 1 year, how can we find the transition matrix for 4 months?

By calculating the cube root of P

By numerically calculating a matrix M such that M x M x M is equal to P

By dividing P by 3

By calculating the matrix P x P x P

Q38. According to Basel II’s definition of operational loss event types, losses due to acts by third parties intended to defraud, misappropriate property or circumvent the law are classified as:

Internal fraud

Execution delivery and system failure

External fraud

Third party fraud

Q39. For an equity portfolio valued at V whose beta is #, the value at risk at a 99% level of confidence is represented by which of the following expressions? Assume # represents the market volatility.

2.326 x # x V x #

1.64 x V x # / #

1.64 x # x V x #

2.326 x V x # / #

Q40. A risk analyst analyzing the positions for a proprietary trading desk determines that the combined annual variance of the desk’s positions is 0.16. The value of the portfolio is $240m. What is the 10-day stand alone VaR in dollars for the desk at a confidence level of 95%? Assume 250 trading days in a year.

12595200

157440000

6297600

31488000

Q41. When estimating the risk of a portfolio of equities using the portfolio’s beta, which of the following is NOT true:

relies upon the single factor CAPM model

use of the beta assumes that the portfolio is diversified enough so that the specific risks of the individual stocks offset each other

explicitly considers specific risk inherent in the portfolio for risk calculations

using the beta significantly eases the computational burden of calculating risk

Q42. For credit risk calculations, correlation between the asset values of two issuers is often proxied with:

Credit migration matrices

Transition probabilities