8008 Exam Dumps - PRM Certification - Exam III: Risk Management Frameworks, Operational Risk, Credit Risk, Counterparty Risk, Market Risk, ALM, FTP - 2015 Edition

Netting is the ability to set just the net balances when amounts are both owed and due. Netting can take many forms. Payment netting is netting between counterparties that owe moneys to each other in the same currency under the same transaction (or master agreement). Closeout netting is when parties settle a net amount for the value of all outstanding transactions upon the occurrence of an event of default such as bankruptcy. Multiateral netting involves a third party that sets off exposures across counterparties that owe moneys to each other.

Closeout netting under the ISDA master agreement enables a party to terminate transactions early if an Event of Default or Termination Event occurs in respect of the other party. It involves the calculation and netting of the termination values of all transactions to produce a single amount payable between the parties. Closeout netting is therefore the correct answer.

If annual variance is 0.0256, then annual volatility (ie standard deviation) is √0.0256. Therefore the daily volatility will be √0.0256/√250 = 1.01%. The other choices are not correct.

In the Standardized Approach, banks’ activities are divided into eight business lines: corporate finance, trading & sales, retail banking, commercial banking, payment & settlement, agency services, asset management, and retail brokerage. Therefore Choice 'c' is the correct answer.

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. To intuitively 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.

The square root of time rule can be used to convert, say a 1-day VaR to a 10-day VaR, by multiplying the known number by the square root of time to get the VaR over a different time horizon. However, there are key assumptions that underlie the application of this rule, and statements I to IV correctly state those assumptions.

Statements V and VI are not correct, because the application of the square root of time rule requires the absence of serial correlations, and also the absence of volatility clustering (ie independence). Therefore Choice 'c' is the correct answer.

The square root of time rule is also applied to convert volatility or standard deviation for one period to the volatility for a different time period. Remember that VaR is just a multiple of volatility, and therefore the assumptions that apply to the square root of time rule for VaR also apply to the same rule when used in the context of volatilities or standard deviation.