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

According to ISDA, a credit event is an event linked to the deteriorating credit worthiness of an underlying reference entity in a credit derivative. The occurrence of a credit event usually triggers full or partial termination of the transaction and a payment from protection seller to protection buyer. Credit events include

- bankruptcy,

- failure to pay,

- restructuring,

- obligation acceleration,

- obligation default and

- repudiation/moratorium.

A rating downgrade is not a credit event.

A bank's stress testing programme in relation to firm wide stress tests should document the type, frequency and the purpose of the programme, as well as methodologies for defining scenarios and the remedial actions envisaged. Choice 'b' is therefore a necessary attribute of a robust stress testing programme.

The programme should be supported by a robust systems infrastructure that allows the execution of periodic as well as ad-hoc stress tests at the right level (business unit, as well as firm-wide) at the right level of detail or granularity. Choice 'c' also therefore is a valid choice.

A related element is data quality - without which no stress tests can be be credible.

Therefore all the choices listed are correct and Choice 'd' is the correct answer.

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:

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

- 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.

A standard model for representing asset returns in finance is the Geometric Brownian Motion process, and returns according to this model can be estimated by the expression given in Choice 'b'. Note that prices according to this model are log-normally distributed, and returns are normally distributed.

Once the frequency distribution has been determined (for example, using the binomial, Poisson or the negative binomial distributions) and the severity distribution has also been determined (for example, using the lognormal, gamma or other functions), the loss distribution can be produced by a Monte Carlo simulation using successive drawings from each of these two distributions. It is assumed that the severity and frequency are independent of each other. The resulting distribution gives a distribution showing the losses for operational risk, from which there Op Risk VaR can be determined using the appropriate percentile.Therefore Choice 'b' is the correct answer.

The recovery rate, the default hazard rate (also called the average default intensity) and the spread on debt are linked by the equation Hazard Rate = Spread/(1 - Recovery Rate). Therefore, the recovery rate implicit in the given data is = 1 - 8%/10% = 20%.

The credit worthiness of the retail customer is certainly a factor for the bank to consider as it will need to price the loan to cover the expectation of default. At the same time, it will need to look at other loans in its portfolio as to avoid unacceptable concentration risk. A corollary of the same theme is that the bank will need to take a portfolio view of the loan request and consider its contribution to total portfolio risk. Therefore all the choices are appropriate considerations for the bank and Choice 'd' is the correct answer.

Choice 'd' represents a risk concentration due to excessive exposure to a single country, even though spread across different industries as the risk factors (economy, exchange rate, interest rate, political risk etc) are the same for all companies in the country.

Choice 'a' represents a risk concentration because even though the risk factors are different, they are highly correlated and therefore effectively behave as one. These undetected correlations proved to be fatal to many financial institutions during the credit crisis.

Choice 'b' represents a risk concentration as was borne out by the recent credit crisis. Large banks had to take over the obligations of SIVs they had created, even though the SIVs were separate legal entities with no legally enforceable recourse to the originating bank. This had to be done for moral and reputational reasons, and banks had to absorb the losses of these supposedly separate vehicles.

Choice 'c' does not represent a risk concentration, in fact it is not a risk at all because it refers to collateral held, even though the collateral may have been provided by the same counterparty. In this case the risk is to the party providing the collateral (in case the party holding the collateral rehypothecates or sells the collateral and is unable to return it).

Therefore Choice 'c' is the correct answer.

The BCBS document on stress testing provides a very nice articulation of risk concentration, and the relevant text from that document is produced verbatim here: [Risk concentration] may arise along different dimensions: single name concentrations; concentrations in regions or industries; concentrations in single risk factors; concentrations that are based on correlated risk factors that reflect subtler or more situation-specific factors, such as previously undetected correlations between market and credit risks, as well as between those risks and liquidity risk; concentrations in indirect exposures via posted collateral or hedge positions; concentrations in off-balance sheet exposure, contingent exposure, non-contractual obligations due to reputational reasons.