PRMIA 8008 Online Practice Questions and Exam Preparation

PRMIA 8008 Online Questions & Answers

• Question 301:

  Which of the following statements are true:

  I - Credit risk and counterparty risk are synonymous II - Counterparty risk is the contingent risk from a counterparty's default in derivative transactions III - Counterparty risk is the risk of a loan default or the risk from moneys lent directly IV - The exposure at default is difficult to estimate for credit risk as it depends upon market movements

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

  ---

  Explanation

  Credit risk is the risk from a borrower defaulting on moneys lent. Counterparty risk, on the other hand, is the risk that a counterparty to a derivative transaction will be unable to pay at the time the transaction is in-the-money. Credit risk therefore relates more to the banking book, counterparty risk relates more to the trading book. Credit risk and counterparty risk differ in that for counterparty risk, the amount at risk fluctuates for counterparty risk depending upon the value of the underlying derivative. Counterparty risk generally starts at zero, for most swaps and other derivatives are near zero value at inception. Over time, as the prices of the underlying instruments move, one party ends up owing money to the other. A deterioration in the financial situation of the party owing moneys may lead to a loss to the other party, resulting in counterparty risk. Counterparty risk can also arise from stock lending operations and repo trades. Credit risk on the other hand is the traditional risk of default by a borrower, or a bank's customer who has taken a loan or has an overdraft or other credit facility. Statement I is therefore incorrect as credit risk and counterparty risks are different. Statement II is correct as counterparty risk is 'contingent' in the sense it arises only if the transaction with the counterparty ends up being in-the-money, and the counterparty defaults. Statement III is incorrect. The statement describes credit risk. Statement IV is incorrect, as the exposure is known for moneys lent. Derivative exposures for the future are difficult to estimate, they can even turn from moneys owed to moneys due as the value of the underlying changes.

• Question 302:

  For a loan portfolio, expected losses are charged against:

  A. Economic capital
  B. Regulatory capital
  C. Credit reserves
  D. Economic credit capital
  C. Credit reserves

  ---

  Explanation

  Credit reserves are created in respect of expected losses, which are considered the cost of doing business. Unexpected losses are borne by economic credit capital, which is a part of economic capital. Therefore Choice 'c' is the correct answer.

• Question 303:

  Which of the following introduces model error when basing VaR on a normal distribution with a static mean and standard deviation?

  A. Heavy tails
  B. Volatility clustering
  C. Autocorrelation of squared returns
  D. All of the above
  D. All of the above

  ---

  Explanation

  When VaR is based on an assumption of normality with a static mean and volatility, it means anything that violates these assumptions will introduce model error. Volatility clustering implies a non-static volatility. Heavy tails imply non-normality of the shape of the distribution. Autocorrelation of squared returns implies that returns are not independent and identically distributed. Therefore all of these introduce model error. Choice 'd' is therefore the correct answer.

• Question 304:

  The Basel framework does not permit which of the following Units of Measure (UoM) for operational risk modeling:

  I - UoM based on legal entity II - UoM based on event type III - UoM based on geography IV - UoM based on line of business

  A. I and IV
  B. III only
  C. II only
  D. None of the above
  D. None of the above

  ---

  Explanation

  Units of Measure for operational risk are homogenous groupings of risks to allow sensible modeling decisions to be made. For example, some risks may be fat-tailed, for example the risk of regulatory fines. Other risks may have finite tails for example damage to physical assets risk (DPA) may be limited to the value of the asset in the question.

  Additionally, risk reporting may need to be done at the line of business, legal entity or regional basis, and in order to be able to do, so the right level of granularity needs to be captured in the risk modeling exercise. The level of granularity

  applied is called the 'unit of measurement' (UoM), and it is okay to adopt all of the choices listed above as the dimensions that describe the unit of measure. Note that it is entirely possible, even likely, to use legal entity, risk type, region,

  business and other dimensions simultaneously, though doing so is likely to result in an extremely large number of UoM combinations. That can be addressed by then subsequently grouping the more granular UoMs into larger UoMs, which

  may ultimately be used for frequency and severity estimation.

• Question 305:

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

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

  ---

  Explanation

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

• Question 306:

  Which of the following risks and reasons justify the use of scenario analysis in operational risk modeling:

  I - Risks for which no internal loss data is available II - Risks that are foreseeable but have no precedent, internally or externally III - Risks for which objective assessments can be made by experts IV - Risks that are known to exist, but for which no reliable external or internal losses can be analyzed V - Reducing the complexity of having to fit statistical models to internal and external loss data VI - Managing the capital estimation process as to produce estimates in line with management's desired capital buffers

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

  ---

  Explanation

  All the reasons and risks presented above are valid reasons for using scenario analysis, except V and VI - ie, the need to reduce the complexity of calculations is not a valid reason for using scenario analysis. Similarly, making operational risk capital estimates match management's desired capital allocation targets is also not a valid reason. Capital calculations are intended to provide adequate capital for managing the risk from operations, regardless of what management may desire them to be.

• Question 307:

  Altman's Z-score does not consider which of the following ratios:

  A. Market capitalization to debt
  B. Sales to total assets
  C. Net income to total assets
  D. Working capital to total assets
  C. Net income to total assets

  ---

  Explanation

  A computation of Altman's Z-score considers the following ratios:

  -Working capital to total assets

  -Retained earnings to total assets

  -EBIT to total assets

  -Market cap to debt

  -Sales to total assets

  It does not consider Net Income to total assets, therefore Choice 'c' is the correct answer. This makes sense as net income is after interest and taxes, both of which are not relevant for considering the cash flows for debt servicing.

• Question 308:

  There are three bonds in a diversified bond portfolio, whose default probabilities are independent of each other and equal to 1%, 2% and 3% respectively over a 1 year time horizon. Calculate the probability that exactly 1 of the three bonds will default.

  A. .011%
  B. 2%
  C. 5.8%
  D. 0%
  C. 5.8%

  ---

  Explanation

  The probability that only one of the three bonds will default is equal to the sum of the probabilities of the three scenarios where one bond defaults and the other two survive. This probability is given by 1%*(1 - 2%)*(1 - 3%) + (1 - 1%)*2%*(1 3%) + (1 - 1%)*(1 - 2%)*3% = 5.7818%. Choice 'c' is the correct answer.

• Question 309:

  A bank extends a loan of $1m to a home buyer to buy a house currently worth $1.5m, with the house serving as the collateral. The volatility of returns (assumed normally distributed) on house prices in that neighborhood is assessed at 10% annually. The expected probability of default of the home buyer is 5%.

  What is the probability that the bank will recover less than the principal advanced on this loan; assuming the probability of the home buyer's default is independent of the value of the house?

  A. More than 1%
  B. Less than 1%
  C. More than 5%
  B. Less than 1%

  ---

  Explanation

  The bank will not be able to recover the principal advanced on this loan if both the home buyer defaults, and the house value falls to less than $1m, ie the price moves adversely by more than $500k, which is $-500k/$150k = -3.33. (Note that

  150k is the 1 year volatility in dollars, ie $1.5m * 10%).

  The probability of both these things happening together is just the product of the two probabilities, one of which we know to be 5%. The other is also certainly a small number, and intuitively it is clear that the probability of both the things

  happening together will be less than 1%.

  For a more precise answer, we can calculate the probability of the house price falling by 3.33 standard deviations by calculating the area under the standard normal curve to the left of -3.33. This indeed is a very small number (actually equal

  to NORMSINV(- 3.33)=0.00043), which when multiplied by the probability of default of the home buyer at 5% is certainly going to be less than 1%. Therefore Choice 'b' is the correct answer.

• Question 310:

  Which of the following formulae describes Marginal VaR for a portfolio p, where V_i is the value of the i-th asset in the portfolio? (All other notation and symbols have their usual meaning.)

  

  A. Option A
  B. Option B
  C. Option C
  D. Option D
  D. Option D

  ---

  Explanation

  Marginal VaR of a component of a portfolio is the change in the portfolio VaR from a $1 change in the value of the component. It helps a risk analyst who may be trying to identify the best way to influence VaR by changing the components of the portfolio. Marginal VaR is also important for calculating component VaR (for VaR disaggregation), as component VaR is equal to the marginal VaR multiplied by the value of the component in the portfolio. Marginal VaR is by definition the derivative of the portfolio value with respect to the component i. This is reflected in Choice 'a' above. Using the definitions and relationships between correlation, covariance, beta and volatility of the portfolio and/or the component, we can show that the other two choices are also equivalent to Choice 'a'. Therefore all the choices present are correct.