February 21, 2009
Points for discussion
An interesting debate about the approaches that banks have taken to quantify operational risk for Basel II. What have been the problems and what are some of the solutions, is openly discussed in this article. Really does Basel II work at all?
Incognito // Obviously, the biggest challenge for op risk modelling is the tail of the loss distribution
MD // That would be true and this is no secret here. If this wasn`t a limiting function (the curve had a tendency to move towards the X-Axis and become more narrow as you move out) then there would be plenty of data and modelling exercises would be really straight forward however, most banks would be broke as it would be more common losing a substantial amount of money.
So we are going to have less data because as the probability drops off so do the number of events.
Incognito // My understanding is that there are basically two solutions to the lack of data - you either try to get more insight out of the data you do have, OR you try to add qualitative information to the data. Is it right to say that these are two separate schools of thought and that those separate schools still exist today?
MD // When it comes to modelling, operational risk and Basel II there are really three distinct schools of thought, they are:
(1) sbAMA The Scenario Based Approach is based on the assessment of forward-looking “what-if” scenarios. The output of the scenarios are entered into an operational risk model where regressive techniques such as Monte Carlo are used to compute regulatory capital.
(2) RDCA Risk Drivers and Controls Approach (formerly known as the scorecard approach) uses a series of weighted questions (some of which can be interpreted as scenarios) whose answers yield a score that can be aggregated to allow the calculation of capital between business units.
(3) LDA Loss Data Approach puts emphasis to the computation of capital on historic loss data. Standard statistical techniques such as those that have been used by the insurance industry for years as well as some of the more complex derived functions including extreme value theory are used to compute regulatory capital. Estimation of exposure is usually performed on the frequency and magnitude of event distributions separately within the Basel event classifications, the results are then aggregated for a clear dimension of Value at Risk.
Incognito // Is it right to say that there are these two separate schools of thought and that those separate schools still exist today?
MD // In theory yes but life is not such a dichotomy. When you ask people whether they politically believe in right wing or left wing, what they like and dislike and extend that to risk you will find that most risk analysts are no different. That is they land somewhere between LDA and sbAMA, a hybrid approach.
There are other reasons that drive the hybrid existence which are less obvious. Firstly lack of data features but more so is that the Loss Data Approach is backward looking. If an analyst Includes scenarios in their tail estimates, their model or the hypothetical probability distribution function will take on a forward looking perspective, something the regulators are keen on driving home.
Incognito // Is it also accurate to say that EVT is basically, the-get-more-out-of-the-data school?
MD // One thing I say about extreme value theory, is that there is nothing extreme about it. Take this statement from Jack King`s book Operational Risk Measurement and Modelling; he puts it well.
Extreme Value Theory offers a parametric statistical approach for the extreme values of data. Its roots are in the physical sciences and it has recently been applied to insurance.
Of course we are now applying it to banking but it is nothing more than a set of parametric distributions for the largest (or smallest) values (GEV) and excess values over a threshold (GDP) from a set of underlying losses.
He likens it to building a wall to keep the sea off a path. How tall should the wall be considering the highest and lowest tide over a year? How about ten years of high and low tides, how about 100 years without a breach of the wall; how tall would you build the wall?
In this way EVT can answer that question by using curve estimates (method of moments or maximum likelihood estimators) in the lower data end of the curve at the threshold.
We still need data, not in the extreme so much but also in the mid range of the curve to dimension the extreme. Most banks have been so poor at classifying losses correctly and attributing loss events to business units that they don`t have very much data in either the extreme or normal position.
Incognito // While Bayesian techniques are advocated by the mix-data-with-scenarios school?
MD // Bayesian techniques are a completely different mathematical technique which looks at the contribution of data to forward points. A Bayesian approach is more causal and draws conclusions on how one variable infers or contributes to another.
As written by Martin Neil from Agena
A Bayesian Network is a way of describing the relationships between causes and effects and is made up of nodes and arcs
More on Agena
We call this Bayes Theorem of Propagation So if we go back to our three key approaches LDA, sbAMA or RDCA. RDCA is the Bayes end of the approaches
Incognito // One of my contacts claimed that the future of operational risk modelling will be based around Bayesian Methods, Do you agree?
MD // Personally I am a big fan of Bayesian Methods because they show what drives failure and that then supports management decision on what events are creating the worst hazards. Curve fitting (LDA) does not provide this type of information, it may show the maximum potential loss given a set of data but how does that assist management reduce the value.
I believe Bayesian Methods offer great relief to the operational risk capital problem however they are not without their difficulties. Firstly, Bayesian approaches are a lot of work but also I fair operational risk departments in banks have a tenacity to lack virulence and to go on common consensus; Bayesian approaches can have political concerns at implementation because they capture data which can be manipulated by the staff before such variables are connected to the causal model. There is a moral dilemma at the assessment process. People in short lie, for whatever reason; often not in the way you would believe and often they don`t know they have myopic vision.
For example years ago I worked with a risk department that exaggerated its control positions downwards to grab budget for improvements and then another department that refused to see error in their ways, how dare any loss be attributed to them. Bayesian models when mixed with people generally have stacks of error (myopic, Type 1, Type 2 and criminal).
Incognito // If so (Bayesian is the future), why has it taken so long for Bayesian methods to win converts, given that researchers first started trying to apply them to op risk back in 2000/01?
MD // Oh Bayes has been around a little longer than that --- Thomas Bayes (c. 1702 to 17 April 1761) was a British mathematician and Presbyterian minister, known for having formulated a specific case of the theorem that bears his name: Bayes` theorem, which was published posthumously.
More on Bayes
To answer your question in short:
>> Expensive to capture relevant variables (time consuming)
>> Difficult to define which variables to capture (requires operation insight)
>> Hard to remove human error or manipulation of data out of the variable capture (the integrity of the system can be easily compromised)
Incognito // One or two of my contacts were very pessimistic about the possibility of ever being able to model op risk to the kind of confidence level that regulators require. Can it be done, in your view?
MD // In this sense I would agree and I can side with him in many respects however, not to do something worthy in its intentions because it is hard to achieve is not an excuse. If we are ever to improve the standard delivery of banking, we are going to need diligent measures of what can go wrong so that we target our limited resources where they can best be applied.
Can it be done?: Yes but be aware, as we smooth the kernel we introduce error. It can be done, but the result will have error, nothing is a perfect measure of anything. There is always going to be propagated error in our model that we will fail to capture or be able to explain.
Can it be done, depends on how we define what has to be done to begin with. At present in the world of banking this is a lacking. Amazingly there are still plenty of operational risk analysts out there who do not have the inclination, interest or perseverance in finding a way forwards and thus the confusion continues. The regulators themselves contribute massively to this failing and the responsibility for being a difficult program does not lie directly on the heads of the banks themselves.
This is not so easy to print or write of course as it may be misinterpreted as being utterly irreverent but none the less it is blatantly obvious that what is lacking is the insight from some of those that are responsible for directing the discipline in their organizations.
Is this discovery something out of this world?
Not really! Like all measures of statistics there are going to be some banks that have it sorted which are run by outstanding analysts and then other institutions that press forwards with less time, presence and interest. The secret of Basel II is not can it be done but is to narrow the standard deviation between poor banks and good banks when it comes to risk awareness and control diligence. Basel II aims to bring all banks up to a specific benchmark or standard and cut out the left end of the tail in this case and in that respect, it is working.
So we ask ourselves what happened to the US banking community?
Well they actually didn`t prescribe to Basel II until it was too late, but that is another discussion all unto itself.
Posted by CausalEvents at 02:45 PM
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February 12, 2009
The US compensation battle
The latest musings on the credit crisis or perhaps the outcome of the event (one single occasion is usually driven by many causal factors), is corporate compensation.
This week president Barack Obama called the bonus payouts for banks receiving rescue funds as "shameful" and that the government will require financial companies on the aid trade to cap compensation for top officials at USD 500,000 a year.
Obama stated that he was responding to a public outcry "in bad taste" over bonuses paid to bankers and wanted to enforce greater transparency of expenses and restrict severance pay when executives leave the company.
Read with charts
The Irony
He has a point of course, why should the tax payer fund the compensation of corporate individuals who have been partially responsible for destroying rather than created value in their firm. Ironically there is a sniff of pharisaicalness in all of this considering that his cabinet has been implicated with their own inability to manage tax liabilities. Not even one month in, Tom Dasche the former senator selected by Obama for Health and Human services was scrutinized for lodging tax errors and secretary Nancy Killefer appointed last month by the president to probe government spending, has also fallen through the moral floor of the inland revenue system.
This is all partially jocular but it is not the feature of this journal. More importantly we want to ask ourselves:
(1) What is the purpose of this blanket policy over its decorous gesture?
(2) Is corporate banking compensation policy skewed or failing in the first place?
(3) Could such caps drive some kind of different dysfunction?
If a broker wants to propagate ever increasing returns against this baseline, they have little or no choice but to go deeper on a position even though that might have negative marginal utilities of return. The foundation of this very system needs to be addressed, not the size bucket but the downside; the risk-reward-appetite of each individual or team in the bank needs perhaps a different approach. More concerning is that most incentive programs are short term and aggregate the upside but do not weight the downside equally.
Then of course as employees climb the corporate ladder their compensation accelerates and their ability to influence the executive team also becomes more prevalent.
In most cases or measures there is going to be a left or right zone and it is argued that Merrill Lynch sits squarely in that flange. Bloomberg Jan 29 ~ ``New York Attorney General Andrew Cuomo may demand the return of $4 billion in bonuses paid by Merrill Lynch & Co. just before it was acquired by Bank of America Corp. Cuomo also wants to know whether [ALT] Bank of America Chief Executive Officer Kenneth Lewis knew about the accelerated bonuses and about Merrill`s surprise $15 billion net loss in the fourth quarter`` ~~~ That is probably pushing the boundaries however a blanket policy might not be the right approach either as it paints all banks with the same brush.
On Consideration
Within hours of the Obama blanket policy, there was also a response from the industry. Goldman Sachs Group stated that it “wants to repay the 10 billion it received from the US Treasury last year to signal the firm is healthy and escape any imposed limitations on the funds” and is going to raise additional funds in the equity markets to balance this when the time is appropriate. What value is going to be derived from transferring one obligation to another is yet to be determined.
One of the main problems for blanket incentives is that they can drive institutions to become baron places of innovation especially when the floor is adjusted to some arbitrary mean as it is in the current process. In such a situation an executive might only work till they reach the incentive barrier and then take an attitude of languor in their work, others might simply leave the institution.
Some argue that the incentive scheme for large banks should actually be focussed more on share options than cash incentives; in this way if the firm actually performs bonuses should be reflected appropriately in the share price.
Last year when the Troubled Asset Auction Program was launched by the federal reserve, it also inserted the following ruling “Any financial institution participating in the Capital Purchase Program will be subject to more stringent executive compensation rules comprising of three key criteria:
(1) Incentive compensation for executives does not encourage excessive risk taking that may threaten the value of the financial institution.
(2) Clawback of any bonus or incentive compensation paid to a executives based on statements of earnings, gains, or other criteria that are later proven to be materially inaccurate.
(3) Prohibition on the financial institution from making any golden parachute payment to a executives.
So what went wrong?
Well firstly there are no limits on pay or linking of pay to performance. In addition, definitions around what is risk taking and when are such risks to be booked to the balance sheet is also omitted. The definition of what is excessive pay is actually totally lacking and there was no criteria on the clawback of bonuses. While firms that sold troubled assets to the government were not allowed to deduct pay that exceeds USD 500k from their corporate income taxes, such a ruling was only applied to these firms in the program and not to the broader industry that might have received support.
The web site common dreams puts it this way
The current U.S. tax code places a $1 million cap on tax deductibility for executive compensation, but this provision has been meaningless in practice because it allows exceptions for "performance-based" pay. Most companies simply limit top executive salaries to around $1 million and then add on to that total various assortments of "performance-based" bonuses, stock awards, and other long-term compensation. The bailout legislation was designed to close this loophole by eliminating that exception for executives of bailed-out firms.
Where does that brings us up to today?
Going forwards I would say this is going to be a contentious area that will require more carefully thought through policy. A policy that perhaps balances talent retention with performance based incentives that are linked to risk-adjusted reward and the downside outcome.
Posted by CausalEvents at 06:10 PM
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