The ADI will be expected to establish a distribution of aggregated potential operational risk losses across the ADI or a set of operational risk loss distributions for sub-parts of the ADI`s operations.
AGN 115.2
+opVar
This goal is actually achieved by a measurement technique known as OpVar or Value-At-Risk which is one method to calculate operational risk capital provision and must cover operational risk losses over a fixed time period and at a given confidence level. OpVar can be calculated provided we know F-1(1-a) where alpha is the confidence level say 1%, which in this case is the 99th percentile of that loss distribution and thus OpVar0.99 is F-1(1-0.99) = (0.01).
Value at Risk was originally a market risk measure that describes probabilistically the market exposure of a trading portfolio and is widely used by banks, security firms and commodity traders. Unlike other measures of risk, Value-at-risk gives management the insight of exposure that is NOT a retrospective risk metric and the Basel Accord in particular has encouraged the translation of this market measure for risk to credit and operational risk so that a transparent metric of risk can be generated across the three disciplines. So back to our loss distribution or more precisely the 99th percent quartile of our complete loss distribution, APRA gives us two choices for creating this distribution:
1)The single distribution approach, representing all potential operational risk losses and the model must be clear in how it shows specific dependence assumptions across operational risk event classifications and multiple business lines.
2) The multiple distribution approach is an alternative where the operational risk measurement model comprises of several distributions that will beaggregated to show a total exposure amount. For what it`s worth one doesn`t simply add the distributions together because that action would overestimate the capital requirement.
If a bank was to take the multiple distribution approach they must also show correlations between event categories. This is where one distribution has an inherent function on another.
+ So which one should a bank choose?
Both have their advantages and disadvantages, as we shall see below.
The single distribution approach may appeal to banks with limited internal data points for a specific operational risk event classification or where their ability to confirm accurate tracking of loss events has occurred. The single approach also is ideal with measurement techniques such as Extreme Value Theory and in that respect it seems a quick way to the solution, but it does come with its conundrums:
Firstly how complete is the model, that is, are all risk events captured and if so how is capital allocation mapped between functions and business lines. APRA specifically makes mention to this for single distribution approaches. Secondly, when this type of analysis is aggregated, it may be easy to understand the total exposure for a business line but a true comprehension of causality can be concealed in the equation and that knowledge is important for tracking and controlling potential events. There are also other satellite issues, such as the use of external data and most regulators have made it mandatory that external data factors in the model. The problem with consolidated external data is in its nebulous nature, how should it be scaled for use within the organisation and how should the bank integrate these additional data points. Consolidated data stratification is not a straightforward task because the weak context classifications create debates over whether such data points are actually part of population they are being applied to. The bank also has to consider a dimension on the scale between external and internal business unit gearing and that task can be very complicated without fragmenting the model.
So let`s turn to the multiple distribution approach, many of the problems mentioned above are actually circumvented with this technique. Certainly ensuring the capital model is representative of the business activities seems to be inherent within the application itself and choosing where and what external data to use is easier to manage. Unfortunately there is no short cut and with this style, the bank will end up having several distributions representative of loss and it will fail unless it can establish and document good correlation coefficients between the inference between one distribution and the next. Such methods explain how a single loss event increases in magnitude as it gathers momentum, instigating knock on effects from one event category to the next and again the regulator has stressed this has to be modeled within the capital system.
Where the ADI`s approach assumes a dependence structure across those risk measures, by way of correlation estimates across operational risk losses or business lines, the ADI may be able to incorporate those estimates into its aggregation of individual operational risk measures.
Like most agendas in life, there isn`t a short cut and Australian banks should carefully consider the implementation tactics required in the context of their internal operations before jumping on one distribution style against the other.
