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The
best piece in this entire article ( Posted in full because its
subscribers only ) is "
How
could so many smart people have got it so wrong? One reason is that
their faith in their models' predictive powers led them to ignore what
was happening in the real world."
What does that say about our entire approach to modeling especially when our clients believe in the model, thinking it reflects the real world as most of these poor ( pun ) bankers and financial geniuses did Ill post the master text from the main article " The Blunders that lead to the catastrophe " - How the brightest and the best mathematical modelers fail at their task of keeping disaster at bay in the next post:::: To be fair the main quant defense is we didn't have enough related data but when the result is a financial loss to the financial world equal to all the sums made in the systems lifetime that excuse frankly sucks ( :: ( : pete Peter Baston IDEAS www.ideapete.com Editorial: When the numbers don't add up Blinded by science - Financial regulators have allowed themselves to be bamboozled ( Ably assisted by quants and techies on huge bonuses - my insert ( : ( : pete )
 ONE of the most alarming things about the crisis in the global financial system is that the warning signs have been out there for some time, yet no one heeded them. Exactly 10 years ago a hedge fund called Long-Term Capital Management failed to convince investors that it could repay its debts, thereby bringing the world to the brink of a similar "liquidity crisis" to the one we now see. Disaster was averted then only because regulators managed to put together a multibillion-dollar bailout package. LTCM's collapse was particularly notable because its founders had set great store by their use of statistical models designed to keep tabs on the risks inherent in their investments. Its fall should have been a wake-up call to banks and their regulatory supervisors that the models were not working as well as hoped - in particular that they were ignoring the risks of extreme events and the connections that send such events reverberating around the financial system. Instead, they carried on using them. Now that disaster has struck again, some financial risk modellers - the "quants" who have wielded so much influence over modern banking - are saying they know where the gaps in their knowledge are and are promising to fill them (see "How the risk models failed the world's banks"). Should we trust them? Their track record does not inspire confidence. Statistical models have proved almost useless at predicting the killer risks for individual banks, and worse than useless when it comes to risks to the financial system as a whole. The models encouraged bankers to think they were playing a high-stakes card game, when what they were actually doing was more akin to lining up a row of dominoes. How could so many smart people have got it so wrong? One reason is that their faith in their models' predictive powers led them to ignore what was happening in the real world. Finance offers enormous scope for dissembling: almost any failure can be explained away by a judicious choice of language and data. When investors don't behave like the self-interested Homo economicus that economists suppose them to be, they are described as being "irrationally exuberant" or blinded by panic. An alternative view - that investors are reacting logically in the face of uncertainty - is rarely considered. Similarly, extreme events are described as happening only "once in a century" - even though there is insufficient data on which to base such an assessment. “
” The quants' models might successfully predict the movement of markets most of the time, but the bankers who rely on them have failed to realise that the occasions on which the markets deviate from normality are much more important than those when they comply. The events of the past year have driven this home in spectacular fashion: by some estimates, the banking industry has lost more money in the current crisis than it has made in its entire history. Can modellers do better? There are alternatives to the standard approach: models based on people's real-world behaviour (New Scientist, 30 August, p 16) and on "virtual agents" (New Scientist, 19 July, p 32) have shown promise, though these are still fringe fields in economics. Most quants, while acknowledging the shortcomings of their models, tend to argue that approximations are necessary, given the difficulty of modelling extreme events, which are in any case rare. That may be true, but it is dangerous to assume that the approximations are sound. Sometimes even small modelling deficiencies can have huge consequences. Nassim Taleb, an expert on chance and co-director of the Decision Research Laboratory at the London Business School, argues that all economic activities are subject to extreme events that have complex real-world consequences - and that such events are inherently unpredictable by any conventional tool. If Taleb is right - and his argument will be hotly contested - then we cannot be sure that it is even possible to build risk models capable of making meaningful predictions when they matter most. Until we are clearer about that, it would be sensible to proceed with caution. Banks should be careful not to assume that they have it right and the rest of the world has it wrong. And regulators - who have lately allowed themselves to be blinded by science - should have no qualms about shutting down activities they do not understand. We shouldn't need another warning. |
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