I’m a bit perplexed at a Gillian Tett piece in the Financial Times in which she is shocked, shocked that managers didn’t heed warnings from subordinates that risk models weren’t all that they were cracked up to be. Her article wouldn’t seem odd, except it focuses on a really basic shortcoming, namely that many models (Black Scholes, Value at Risk being some of the best known) assume a normal distribution of risk (also known as Gaussian or a bell curve). Anyone who knows the basics is aware that markets deviate from a normal distribution: they exhibit skewness (results are not symmetrically distributed around the mean) and “fat tails” or kurtosis risk (extreme events are far more probable than in a normal distribution).
Yet Tett reports that telling, or more accurately, reminding management of these failings is a career-limiting move:
A few years ago, Ron den Braber, an outspoken Dutch mathematics geek, was working in the risk department at Royal Bank of Scotland when he became alarmed about the models being used to price collateralised debt obligations.Most notably, he concluded that the so-called Gaussian Copula approach then in use at RBS (and many other banks) significantly underplayed risks attached to the most senior pieces of debt – creating a danger of future, large losses.
So he duly tried to raise the alarm. But, as he tells the tale, he faced hostility. “I started saying things gently – in banks you don’t use the word ‘error’, but the problem is that in banks . . . people just don’t want to listen to bad news,” Mr den Braber recalls.
Now, every corporate tale has many sides – and RBS, for its part, vehemently denies that it ever ignores challenges or stifles debate. It says it could not find any record of strong warnings about the Gaussian Copula model, is aware of its shortcomings, and, while it has recently suffered CDO losses, these relate to products acquired after Mr den Braber’s time…
Or as one senior risk manager writes (anonymously since he remains employed): “[My] institution has now taken multibillion writedowns – job losses result and significant share price erosion – and I wonder how this can have happened? Upfront we did express to senior management that we lacked the analytical skills . . . and highlighted deep concerns about the approach colleagues in the market risk area had taken . . . I feel responsible for not doing more, but I really did push my views, risking my immediate career.”
Yves here. The second example, although less specific, is more troubling. Misplaced faith in analytical models is more understandable than handing risk management responsibility to a team that tells management is it not up to the task.
Back to Tett:
But, if nothing else, this saga shows the great blind spot that still haunts many banks. This decade, financiers have invented so many brilliantly clever mathematical tools to repackage risk that the industry has slipped, almost unthinkingly, into an assumption that “credit” is a collection of abstract equations, stripped from any human context.Thus banks have become so dazzled with their powers that they have ignored how they interact with the rest of society – or how the tribal aspects of their own institutions can create dangerous traps.
Meanwhile, the cult of models has become so extreme that banks have believed them even when this collides with common sense. Yet, as any Latin scholar knows, the word “credit” hails from credere: “to trust”. It is, in other words, also a social construct.
And bankers forget this human dimension to their cost – no matter how impressive the abstract numbers might seem. Or as the same risk officer says: “The billions involved were so hard to contemplate that we almost certainly lost sight of the possible consequences [of our credit business] until it was too late.”
So, as the banks nurse their credit losses, they certainly do need to review why some of their clever mathematical models failed. That geeky Gaussian Copula stuff, in other words, matters hugely.
But, most important of all, they need to work out why the human processes around the models failed, too.But, if nothing else, this saga shows the great blind spot that still haunts many banks. This decade, financiers have invented so many brilliantly clever mathematical tools to repackage risk that the industry has slipped, almost unthinkingly, into an assumption that “credit” is a collection of abstract equations, stripped from any human context.
Thus banks have become so dazzled with their powers that they have ignored how they interact with the rest of society – or how the tribal aspects of their own institutions can create dangerous traps.
Meanwhile, the cult of models has become so extreme that banks have believed them even when this collides with common sense. Yet, as any Latin scholar knows, the word “credit” hails from credere: “to trust”. It is, in other words, also a social construct.
And bankers forget this human dimension to their cost – no matter how impressive the abstract numbers might seem. Or as the same risk officer says: “The billions involved were so hard to contemplate that we almost certainly lost sight of the possible consequences [of our credit business] until it was too late.”
So, as the banks nurse their credit losses, they certainly do need to review why some of their clever mathematical models failed. That geeky Gaussian Copula stuff, in other words, matters hugely.
But, most important of all, they need to work out why the human processes around the models failed, too.
Tett is on to something that a lot of professionals in banking no longer want to hear: credit worthiness depends on character as well as ability to pay. But assessment of character is subjective, and somehow institutions are not only reluctant to make assessments on a case-by-case basis, but distrust qualitative analysis.
One of the oddities of the banking industry is that despite all the talk of economies of scale, it’s utter rubbish. In the US, banks above a certain threshold (different studies draw the line in different places, but all come to the same conclusion) banks show a slightly increasing cost curve, meaning big banks are more costly to operate per dollar of assets, despite considerable cost efficiencies in certain areas (transaction processing, access to interbank funding). My pet, unproven view is that smaller banks know their communities better (is the hardware store a good business?) and make greater use of old-fashioned credit processes and that in the end, they are no more costly than quantitative, multi-level credit review processes (but if a big bank tried to revert to old-style lending, it might impose more costs for the same procedure than a small bank because it would have more portfolio/supervisory reviews).
Another FT writer, John Dizard, had a more cynical take on why financial firms continue to rely on demonstrably flawed Gaussian models:
As is customary, the risk managers were well-prepared for the previous war. For 20 years numerate investors have been complaining about measurements of portfolio risk that use the Gaussian distribution, or bell curve. Every four or five years, they are told, their portfolios suffer from a once-in-50-years event. Something is off here.Models based on the Gaussian distribution are a pretty good way of managing day-to-day trading positions since, from one day to the next, risks will tend to be normally distributed. Also, they give a simple, one-number measure of risk, which makes it easier for the traders’ managers to make decisions.
The “tails risk” ….becomes significant over longer periods of time. Traders who maintain good liquidity and fast reaction times can handle tails risk….Everyone has known, or should have known, this for a long time. There are terabytes of professional journal articles on how to measure and deal with tails risk….
A once-in-10-years-comet- wiping-out-the-dinosaurs disaster is a problem for the investor, not the manager-mammal who collects his compensation annually, in cash, thank you. He has what they call a “résumé put”, not a term you will find in offering memoranda, and nine years of bonuses.
Anonymous 
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Two points:
First, with regard to Tett, it would be interesting to look at the financial press over the past decade to see what coverage was given to these warnings as a percent of overall ink.
Second, Yves’ point about the costs associated with evaluating character is essential to ponder. Where we find higher cost, we also find limits to volume and speed. Once the larger financial firms chose to compete on volume and speed, attention to character was doomed. As we know, with regard to residential lending, the attention to character got outsourced — but unfortunately to the wrong players: mortgage brokers who fell in line with attention to volume and speed.
From an industrial market structure standpoint, any future effort to retain the advantages of volume and speed while not falling victim to inattention to slower, higher cost character determination will turn on finding a different set of players who have the skills, know how and dedication to character based evaluation.
They exist and are called non-profit affordable housing groups (the best ones anyway).
And, not only do the best ones have the skills and track record by the way (their delinquency and foreclosure rates are far smaller because they DO pay attention to character). But, in addition, they have a built-in set of motivations that sustain their competitive advantage: namely, they actually care about whether the borrower stays in the home. (Again, this refers to the best in class and certainly does not refer to ersatz firms who call themselves nonprofits but are close to being scams).
So, there’s a viable industrial approach out there. But, we’re unlikely to seize it because of cultural misunderstanding of non-profits ranging from naivete to arrogance.