PRMIA 8008 Online Practice Questions and Exam Preparation

PRMIA 8008 Online Questions & Answers

• Question 21:

  Which of the following distributions is generally not used for frequency modeling for operational risk

  A. Binomial
  B. Poisson
  C. Gamma
  D. Negative binomial
  C. Gamma

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  Explanation

  Frequency modeling is performed using discrete distributions that have a positive integer as a resultant - this allows for the number of events per period of time to be modeled. Of the distributions listed above, Poisson, negative binomial and

  binomial can be used for modeling frequency distributions. The Poisson and negative binomial distributions are encountered the most in practice.

  The gamma distribution is a continuous distribution and cannot be used for frequency modeling.

• Question 22:

  Which of the below are a way to classify risk governance structures:

  A. Reactive, Preventative and Active
  B. Committee based, regulation based and board mandated
  C. Top-down and Bottom-up
  D. Active and Passive
  A. Reactive, Preventative and Active

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  Explanation

  This is a tricky question in the sense no risk management professional can be expected to know the answer to this one unless they have read Chapter 2 of the PRMIA handbook. So this question appears purely for the sake of something you would need to know purely for the sake of the exam. PRMIA's handbook classifies governance sructures as reactive, preventative and active. Reactive structures involve monitoring signals after the event leading to corrective actions. Preventative structures are forward looking and anticipate issues before they arise. Active structures include considerations of operational efficiency and not just governance. All other answers are made up phrases and are incorrect. In reality, corporations employ all structures together without worrying about the boundary between the three, and these distinctions do not exist except in textbooks.

• Question 23:

  Which of the following statements are correct in relation to the financial system just prior to the current financial crisis:

  I - The system was robust against small random shocks, but not against large scale disturbances to key hubs in the network II - Financial innovation helped reduce the complexity of the financial network III - Knightian uncertainty refers to risk that can be quantified and measured IV - Feedback effects under stress accentuated liquidity problems

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

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  Explanation

  Statement I is correct. The financial system proved to be stable against small shocks and disturbances, or shocks of a particular type (eg, the dotcom crash, the wars in the Persian Gulf); but rather fragile against other types of shocks, including disturbances to key market participants caused by a worsening of mortgage defaults.Statement II is incorrect. Financial innovation, in particular the slicing and dicing of 'risk' through securitization, significantly increased interrelationships, dependence on the same risk factors, and the complexity of the system as a whole.Statement III is incorrect. A distinction is sometimes made between risk that is knowable, measureable, and quantifiable through parameters; and uncertainty, where the parameters are not known at all. The latter is called 'Knightian uncertainty' after the name of the scholar who came up with the distinction between the two.Statement IV is correct. Feedback effects had the greatest impact on liquidity which was tended to be hoarded, and on asset prices that tumbled as market participants tried to sell assets to become more liquid.Thus, choice is a the correct answer.

• Question 24:

  Which of the following statements is true:

  I - If the sum of its parameters is less than one, GARCH is a mean reverting model of volatility, while EWMA is never mean reverting II - Standardized returns under both EWMA and GARCH show less non-normality than non standardized returns III - Steady state variance under GARCH is affected only by the persistence coefficient IV - Good risk measures are always sub-additive

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

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  Explanation

  GARCH is a mean reverting model of volatility, with a steady state mean that the model reverts to in the absence of market shocks. EWMA is not mean reverting and volatility under EWMA stays constant in the absence of shocks. Therefore statement I is correct. Both EWMA and GARCH models are designed to address volatility clustering, which explains much of the non-normality of returns. When returns are standardized to the volatility calculations under either of these methods, the returns appear far closer to the normal distribution than non-standardized returns. (If it were not to, then there would have been no point in using these techniques.) Statement II is correct. Steady state variance under GARCH is defined as / (1 - - ), where and are the two parameters (called the reaction and persistence coefficient respectively). Clearly, it is affected by more than just the persistence coefficient, therefore statement III is not correct. Sub-additivity is a very desired property in risk measures. It implies the sum of the parts is greater than the whole. In the case of risk measures, the whole is smaller than the sum of the parts due to diversification. Statement IV is true. Therefore the correct answer is Choice 'c'

• Question 25:

  A bank evaluates the impact of large and severe changes in certain risk factors on its risk using a quantitative valuation model. Which of the following best describes this exercise?

  A. Stress testing
  B. Simulation
  C. Scenario analysis
  D. Sensitivity analysis
  C. Scenario analysis

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  Explanation

  It is important to note the difference between sensitivity analysis and stress testing. Sensitivity analysis applies to measuring the effect of changes on the outputs of a model by varying the inputs - generally one input at a time. In scenario

  analysis, a number of variables may be changed at the same time to see the impact on the dependent variable. For example, a bank may measure the changes in the value of its mortgage portfolio by varying its assumptions on prepayment

  expectations, interest rates and other factors, using its modeling software or application. The changes in the inputs may or may not relate to integrated real world situations that may arise. Sensitivity analysis is purely a quantitative exercise,

  much like calculating the delta of a portfolio.

  A stress test may include shocks or large changes to input parameters but it does so as part of a larger stress testing programme that generally considers the interaction of risk factors, past scenarios etc. At its simplest, a stress test may be

  no different from a sensitivity analysis exercise, but that is generally not what is considered a stress test at large financial institutions. A stress test may consider multiple scenarios, for example one scenario may include the events witnessed

  during the Asian crisis, another may include the events of the recent credit crisis. Simulation generally refers to a Monte Carlo or historical simulation, and is often a more limited exercise.

  The exercise described in the question is the closest to a scenario analysis, therefore Choice 'c' is the correct answer.

  It is important to note that all of the choices referred to in this question are related to each other, and the boundaries between them tend to be fuzzy. At what point does a complex sensitivity analysis start resembling a scenario, or a stress test

  can always be debatable, but such a debate would be more about the symantics than be of any practical use.

• Question 26:

  Which of the following statements is correct in relation to liquidity risk management?

  I - Pricing for products that do not impact the balance sheet need not reflect the cost of maintaining liquidity II - Time horizons for liquidity risk management are impacted by both regulatory requirements and the speed at which new sources of liquidity can be tapped III - Collateral management is an important aspect of liquidity risk management IV - The maturity period of various instruments in the capital structure has a significant impact on liquidity needs

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

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  Explanation

  All product pricing should reflect the cost of maintaining the liquidity required to support a product. This is regardless of the accounting treatment for the product, ie irrespective of whether the product is on or off balance sheet. Therefore statement I is incorrect. The time horizon to consider for liquidity risk management is determined taking into account a number of factors, which include both the speed at which new sources of liquidity can be generated and any applicable regulatory requirements. Statement II is correct. Managing collateral, both collateral received and collateral posted with counterparties, is an important aspect of liquidity risk management as liquidity problems often manifest themselves in the form of margin calls requiring collateral to be posted. Statement III is therefore correct. The maturity period of the different sources of capital funding for a bank, for example equity capital, preferred shares, long term debt etc quite clearly has a significant impact on liquidity needs. Stable sources of funds such as equity or preferred capital, or debt that is not maturing shortly help the liquidity position. Statement IV is therefore correct. Choice 'b' is the correct answer.

• Question 27:

  Which of the following statements is NOT true in relation to the recent financial crisis of 2007-08?

  A. An intention to diversify from their core activities led all market participants to the same activities, which though appearing diversified at the bank's level, created a concentration risk at the systemic level
  B. The existence of central counterparties could have limited the damage caused by the financial crisis
  C. Central banks had data on the interconnections between institutions, but poor understanding and analysis meant this data was never analyzed
  D. Counterparty risk was difficult to gauge as it was impossible to know who the counterparty's counterparties were
  C. Central banks had data on the interconnections between institutions, but poor understanding and analysis meant this data was never analyzed

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  Explanation

  Counterparty risk was difficult to gauge as it was impossible to know who the counterparty's counterparties were - this is true as the chain of financial transactions became excessively long with no central transparency of who owed who what.

  Bank A's credit depended upon the health of its counterparties, whose health in turn depended upon other counterparties. Thus Choice 'd' is a correct statement. In an attempt to diversify, banks became more like each other - chasing yield,

  they piled into securitized products, and chasing diversification, they piled into different types of securitized products. The system as a whole became susceptible to small shocks in the assets underlying this vast edifice of structured products.

  Therefore Choice 'a' represents a correct statement.

  Choice 'c' does not represent a correct statement. Central banks had little data on the interconnections between institutions. They were aware of the large volumes of OTC transactions, but had no data to figure out who was connected to who,

  and who had what kind of exposures.

  Choice 'b' represents a correct statement. Most transactions, other than exchange cleared futures trades (which were a tiny fraction of all trades) were cleared on a bilateral basis. The existence of central counterparties (CCPs) could have

  limited the impact of the crisis significantly as market participants would not have lost trust in each other, and the 'collateral damage' that was witnessed from a fall in housing prices, and thereby mortgage assets, would have been more

  contained.

• Question 28:

  Which of the following steps are required for computing the aggregate distribution for a UoM for operational risk once loss frequency and severity curves have been estimated:

  I - Simulate number of losses based on the frequency distribution II - Simulate the dollar value of the losses from the severity distribution III - Simulate random number from the copula used to model dependence between the UoMs IV - Compute dependent losses from aggregate distribution curves

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

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  Explanation

  A recap would be in order here: calculating operational risk capital is a multi-step process. First, we fit curves to estimate the parameters to our chosen distribution types for frequency (eg, Poisson), and severity (eg, lognormal). Note that these curves are fitted at the UoM level - which is the lowest level of granularity at which modeling is carried out. Since there are many UoMs, there are are many frequency and severity distributions. However what we are interested in is the loss distribution for the entire bank from which the 99.9th percentile loss can be calculated. From the multiple frequency and severity distributions we have calculated, this becomes a two step process:

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  Step 1: Calculate the aggregate loss distribution for each UoM. Each loss distribution is based upon and underlying frequency and severity distribution.

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  Step 2: Combine the multiple loss distributions after considering the dependence between the different UoMs. The 'dependence' recognizes that the various UoMs are not completely independent, ie the loss distributions are not additive, and that there is a sort of diversification benefit in the sense that not all types of losses can occur at once and the joint probabilities of the different losses make the sum less than the sum of the parts. Step 1 requires simulating a number, say n, of the number of losses that occur in a given year from a frequency distribution. Then n losses are picked from the severity distribution, and the total loss for the year is a summation of these losses. This becomes one data point. This process of simulating the number of losses and then identifying that number of losses is carried out a large number of times to get the aggregate loss distribution for a UoM. Step 2 requires taking the different loss distributions from Step 1 and combining them considering the dependence between the events. The correlations between the losses are described by a 'copula', and combined together mathematically to get a single loss distribution for the entire bank. This allows the 99.9th percentile loss to be calculated.

• Question 29:

  For the purposes of calculating VaR, an FRA can be modeled as a combination of:

  A. a zero coupon bond and an interest rate swap
  B. a fixed rate bond and a zero coupon bond
  C. two zero coupon bonds
  D. a zero coupon bond and a floating rate note
  C. two zero coupon bonds

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  Explanation

  A forward rate agreement allows one of the parties to borrow an amount at a rate for a length of time, all of which are agreed in advance. Consider a "3 x 6" FRA. This allows a fixed rate borrowing starting at 3 months till the end of 6 months. This is economically equivalent to holding a zero coupon bond till the end of 6 months, and being short another zero coupon bond till the end of 3 months (or the other way round, depending upon which end of the FRA you are on). Therefore Choice 'c' is the correct answer.

• Question 30:

  Which of the following statements is true in relation to a normal mixture distribution:

  I - Normal mixtures represent one possible solution to the problem of volatility clustering II - A normal mixture VaR will always be greater than that under the assumption of normally distributed returns III - Normal mixtures can be applied to situations where a number of different market scenarios with different probabilities can be expected

  A. II and IIIlied to situations where a number of different market scenarios with different probabilities can be expected
  B. III
  C. I and II
  D. I, II and III
  B. III

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  Explanation

  Normal mixtures address fat or heavy tails, not volatility clustering. Therefore statement I is not correct.

  Statement II is not correct. Where VaR is calculated at low levels of confidence, VaR based on normal mixtures may be lower than that under a normal assumption. This is no different than for other fat tailed distributions. Statement III is

  correct. In situations where multiple market scenarios can unfold with a given probability, and each scenario is normal, we can express the result with a normal mixture where the underlying normal distributions have the probabilities of the

  different scenarios.