[2022] 8010.pdf – Questions Answers PDF Sample Questions Reliable [Q26-Q43]

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[2022] 8010.pdf – Questions Answers PDF Sample Questions Reliable

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NO.26 Which of the following need to be assumed to convert a transition probability matrix for a given time period to the transition probability matrix for another length of time:
I. Time invariance
II. Markov property
III. Normal distribution
IV. Zero skewness

I, II and IV

III and IV

I and II

II and III

Explanation
Time invariance refers to all timeintervals being similar and identical, regardless of the effects of business cycles or other external events. The Markov property is the assumption that there is no ratings momentum, and that transition probabilities are dependent only upon where the rating currently is and where it is going to.
Where it has come from, or what the past changes in ratings have been, have no effect on the transition probabilities.
Rating agencies generally provide transition probability matrices for a given period of time, say a year. The risk analyst may need to convert these into matrices for say 6 months, 2 years or whatever time horizon he or she is interested in. Simplifying assumptions that allow him to do so using simple matrix multiplication include these two assumptions – time invariance and the Markov property. Thus Choice ‘c’ is the correct answer. The other choices (normal distribution and zero skewness) are non-sensical in this context.

NO.27 A risk management function is best organized as:

integrated with the risk taking functions as risk management should be a pervasive activity carried out at all levels of theorganization.

report independently of the risk taking functions

reporting directly to the traders, as to be closest to the point at which risks are being taken

a part of the trading desks and other risk taking teams

Explanation
The point that this question is trying to emphasize is the independence of the risk management function. The risk function should be segregated from the risk taking functions as to maintain independence and objectivity.
Choice ‘d’, Choice ‘c’ and Choice ‘a’ run contrary to this requirement of independence, and are therefore not correct. The risk function should report directly to senior levels, for example directly to the audit committee, and not be a part of the risk taking functions.

NO.28 Which of the following objectives are targeted by rating agencies when assigning ratings:
I. Ratings accuracy
II. Ratings stability
III. High accuracy ratio (AR)
IV. Ranked ratings

II and III

III and IV

I and II

I, II and III

Explanation
Rating agencies target both accuracy and stability when they assign ratings. These two objectives can sometimes conflict, so a balance needs to be struck between the two. Rating agencies do not target anyparticular ‘accuracy ratio’ or rankings. Therefore Choice ‘c’ is the correct answer.

NO.29 Which of the following statements are true:
I. The set of UoMs used for frequency and severity modeling should be identical II. UoMs can be grouped together into larger combined UoMs using judgment based on the knowledge of the business III. UoMs can be grouped together into combined UoMs using statistical techniques IV. One may use separate sets of UoMs for frequency and severity modeling

I, II and III

IV only

II, III and IV

All of the above

Explanation
One may use separate UoMs for frequency and severity modeling, for example, a combined UoM may be used for estimating thefrequency of cyber attacks in a scenario, while the severity may be modeled using a more granular line-of-business UoM. Therefore statement I is false, while statement IV is true.Statement II is correct, UoMs can be grouped together into larger units based on the facts relating to the business, controls and the business environment. Similarly, UoMs can be grouped together based on statistical clustering techniques using the ‘distance’ between the units of measure and combining UoMs that are closer to eachother.
In addition, it is also possible to combine both business knowledge and statistical algorithms to combine UoMs.

NO.30 Which of the following does not affect the credit risk facing a lender institution?

The state of the economy

The applicability or otherwise of mark tomarket accounting to the institution

Credit ratings of individual borrowers

The degree of geographical or sectoral concentration in the loan book

Explanation
The state of the economy, credit quality of individual borrowers and concentration risk are all factors that affect the credit risk facing a lender. Mark to market accounting does not change the credit risk, or the underlying economic reality facing the institution. Therefore Choice ‘b’ is the correct answer.

NO.31 Which of the following credit risk models considers debt as including a put option on the firm’s assets toassess credit risk?

The actuarial approach

The CreditMetrics approach

The contingent claims approach

CreditPortfolio View

Explanation
The correct answer is Choice ‘c’. The following is a brief description of the major approaches available to model credit risk, and the analysis that underlies them:
1. CreditMetrics: based on the credit migration framework. Considers the probability of migration to other credit ratings and the impact of such migrations on portfolio value.
2. CreditPortfolio View: similar to CreditMetrics, but adds the impact of the business cycle to the evaluation.
3. The contingent claims approach: uses option theory by considering a debt as a put option on the assets of the firm.
4. KMV’s EDF (expected default frequency) based approach: relies on EDFs and distance to default as a measure of credit risk.
5. CreditRisk+: Also called the ‘actuarial approach’, considers default as a binary event that either happens or does not happen. This approach does not consider the loss of value from deterioration in credit quality (unless the deterioration implies default).

NO.32 Which of the following measures can be used to reduce settlement risks:

escrow arrangements using a central clearing house

increasing the timing differences between the two legs of the transaction

providing for physical delivery instead of netted cash settlements

all of the above

Explanation
increasing the timing differences between the two legs of the transaction will increase settlement risk and not reduce it. Using escrow arrangements, such as central clearing houses to settle transactions (eg the DTCC in the United States) reduces settlement risk. Cash settlements based on netting arrangements reduce settlement risk, while physical delivery combined with gross cash payments increase it.
Therefore Choice ‘a’ is the correct answer.

NO.33 The CDS quote for the bonds of Bank X is 200 bps. Assuming a recovery rate of 40%, calculate the default hazard rate priced in the CDS quote.

0.80%

5.00%

3.33%

2.00%

Explanation
Hazard rate x Loss given default = CDS quote. In other words, Hazard rate x (1 – recovery rate) = CDS quote.
We can therefore calculate the hazard rate for this problem as 200 bps/(1 – 40%) = 3.33%.

NO.34 Under the contingent claims approach to credit risk, risk increases when:
I. Volatility of the firm’s assets increases
II. Risk free rate increases
III. Maturity of the debt increases

II and III

I and III

I, II and III

I and II

Explanation
Under the contingent claims approach, credit risk is evaluated as the value of the put on the firm’s assets with a strike price equal to the face value of the debt and maturity equal to the maturity of the obligation. The Black Scholes model can then be used to value the put, and therefore an increase in volatility and the time to expiry (ie maturity) will increase the value of the debt. An increase in the risk free rate will actually reduce the value of the put, therefore statements I and III are correct and Choice ‘b’ is the correct answer.

NO.35 Which of the following statements are true:
I. Pre-settlement risk is the risk that one of the parties to a contract might default prior to the maturity date or expiry of the contract.
II. Pre-settlement risk can be partly mitigated by providing for early settlement in the agreements between the counterparties.
III. The current exposure from an OTC derivatives contract is equivalent to its current replacement value.
IV. Loan equivalent exposures are calculated even for exposures that are not loans as a practical matter for calculating credit risk exposure.

II and IV

III and IV

I, II, III and IV

II and III

Explanation
Pre-settlement risk is the risk that one of the counterparties defaults prior to the date for the maturity of the transaction in question. This may be an unrelated default, in fact there may have been no default on that particular contract, but the party may have defaulted on its other obligations, or filed for bankruptcy. To deal with such cases and to protect the interests of both the parties, it is common toprovide for immediate termination of positions and settlement based on the current replacement value of the contracts. Therefore statements I and II are correct.
Statement III is correct as well – the exposure from an OTC derivative contract derives fromits current replacement value, and not the notional. If the current replacement value is negative, then the credit exposure is considered equal to zero.
Statement IV is correct as it is quite common to restate all exposures – those from credit lines, OTC derivatives etc – in loan equivalent terms prior to estimating credit risk.

NO.36 Which of the following describes rating transition matrices published by credit rating firms:

Expected ex-ante frequencies of migration from one credit rating to another over a one year period

Probabilities of default for each credit rating class

Probabilities of ratings transition from one rating to another for a given set of issuers

Realized frequencies of migration from one credit rating toanother over a one year period

Explanation
Transition matrices are used for building distributions of the value of credit portfolios, and are the realized frequencies of migration from one credit rating to another over a period, generally one year. Therefore Choice
‘d’ is the correct answer.
Since they represent an actually observed set of values, they are not probabilities nor are they forward looking ex-ante estimates, though they are often used as proxies for probabilities. Choice ‘a’ and Choice ‘c’ are not correct. They include more than information on just defaults, therefore Choice ‘b’ is not correct.

NO.37 Which of the following formulae correctly describes Component VaR. (p refers to the portfolio, and i is the i-th constituent of the portfolio. MVaR means Marginal VaR, and other symbols have their usual meanings.)

III

I and II

Explanation
The first two formulae describe component VaR. The last formula is the formula for Marginal VaR. Therefore I and II is the correct answer.
Component VaR is a VaR decomposition technique that allows the total VaR for a portfolio to be broken down and attributed to the components of a portfolio. The total of the component VaR for each constituent of a portfolio is equal to the VaR for the portfolio. This property is extremely useful as opposed to the standalone VaR for each constituent taken alone as it can be used for allocating trading budgets.

NO.38 For a hypotherical UoM, the number of losses in two non-overlapping datasets is 24 and 32 respectively. The Pareto tail parameters for the two datasets calculated using the maximum likelihood estimation method are 2 and 3. What is an estimate of the tail parameter of the combined dataset?

2.57

2.23

Cannot be determined

Explanation
For a number of processes, including many in finance, while a distribution such as the normal distribution is a good approximation of the distribution near the modal value of the variable, thesame normal distribution may not be a good estimate of the tails. For this reason, the Pareto distribution is one of the distributions that is often used to model the tails of another distribution. Generally, if you have a set of observations, and you discard all observations below a threshold, you are left with what are called ‘exceedances’. The threshold needs to be reasonably far out in the tail. If from each value of the exceedances you subtract the threshold value, the resulting dataset is estimated bythe generalized Pareto distribution.

The Pareto distribution has a ‘shape parameter’. The average of two Pareto distributions with tail parameters 1 and 2 ( is a Greek character, pronounced as ‘sai’ (saa-eee)), is the weighted average of 1 and 2 with weights proportional to the number of observations in the datasets underlying the distributions.

NO.39 Which of the following are valid approaches to calculating potential future exposure (PFE) forcounterparty risk:
I. Add a percentage of the notional to the mark-to-market value
II. Monte Carlo simulation
III. Maximum Likelihood Estimation
IV. Parametric Estimation

III and IV

I, III and IV

I and II

All of the able

Explanation
When a derivative position is entered into, its mark-to-market value is generally close to zero (though the notional may be high). With the passage of time, the derivative’s value fluctuates in an unpredictable way, creating a counterparty exposure that may be difficult to estimate and risk manage. Counterparty risk in such cases is estimated based on Potential Future Exposure, which may be calculated using either:
a) Take the mark-to-market at present, and add a certain percentage of the notional, or b) Perform a Monte Carlo simulation, capturing the stochastic nature of the PFE.
Therefore I and II are valid choices. MLE and parametric estimation are not methods for calculating PFE.

NO.40 If EV be the expected value of a firm’s assets in a year, and DP be the ‘default point’ per the KMV approach to credit risk, and be the standard deviation of future asset returns, then the distance-to-default is given by:
A)

B)

C)

D)

Option A

Option B

Option C

Option D

Explanation
The distance to default is the number of standard deviations that expected asset values are away from the default point. The expression in Choice ‘d’ represents distance to default. Choice ‘d’ is the correct answer. The other choices are incorrect.

NO.41 The VaR of a portfolio at the 99% confidence level is $250,000 when mean return is assumed to be zero. If the assumption of zero returns is changed to an assumption of returns of $10,000, what is the revised VaR?

260000

240000

273260

226740

Explanation
The exact formula for VaR is = -(Z + ), where Z is the z-multiple for the desired confidence level, and is the mean return. Now Z is always a negative number, or at least will certainly be provided the desired confidence level is greater than 50%, and is often assumed to be zero because generally for the short time periods for which market risk VaR is calculated, its value is very close to zero.
Therefore in practice the formula for VaR just becomes -Z, andsince Z is always negative, we normally just multiply the Z factor without the negative sign with the standard deviation to get the VaR.
For this question, there are two ways to get the answer. If we use the formula, we know that -Z= 250,000 (as
=0), and therefore -Z – = 250,000 – 10,000 = $240,000.
The other, easier way to think about this is that if the mean changes, then the distribution’s shape stays exactly the same, and the entire distribution shifts to the right by $10,000 as the mean moves upby $10,000. Therefore the VaR cutoff, which was previously at -250,000 on the graph also moves up by 10k to -240,000, and therefore $240,000 is the correct answer.
The other choices are intended to confuse by multiplying the z-factor for the 99% confidence level with
10,000 etc.

NO.42 Altman’s Z-score does not consider which of the following ratios:

Market capitalization to debt

Sales to total assets

Net income to total assets

Working capital to totalassets

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.

NO.43 Which of the following belong to the family of generalized extreme value distributions:
I. Frechet
II. Gumbel
III. Weibull
IV. Exponential

I, II and III

II and III

All of the above

Explanation
Extreme value theory focuses on the extreme and rare events, and in the case of VaR calculations, it is focused on the right tail of the lossdistribution. In very simple and non-technical terms, EVT says the following:
1. Pull a number of large iid random samples from the population,
2. For each sample, find the maximum,
3. Then the distribution of these maximum values will follow a GeneralizedExtreme Value distribution.
(In some ways, it is parallel to the central limit theorem which says that the the mean of a large number of random samples pulled from any population follows a normal distribution, regardless of the distribution of the underlying population.) Generalized Extreme Value (GEV) distributions have three parameters: (shape parameter), (location parameter) and (scale parameter). Based upon the value of , a GEV distribution may either be a Frechet, Weibull or a Gumbel. These arethe only three types of extreme value distributions.

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