Yes, there have been lots of blunders. We've also not been looking at how environment is becoming increasingly unresponsive, turning a great many of our assumptions about it upside down. :-o http://www.synapse9.com/issues/92-08Commodities2-sm.pdf This is just one of many kinds of divergent in new complexities for development that we have no place to put in our models., and so leave them out of the analysis only because we don't know what to make of them. Things like this are signals of systemic physical system change. In this case, directly, that when the price went up more food and energy were not produced. That sort of thing could also lead to blunders, couldn't it?? :-)
Phil From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of peter Sent: Wednesday, October 15, 2008 10:50 PM To: The Friday Morning Applied Complexity Coffee Group Subject: [FRIAM] The Blunders that lead to the catastrophe - Humpty Dumpty's modeling school The main article ( : ( : pete Peter Baston IDEAS <http://www.ideapete.com/> www.ideapete.com The blunders that led to the banking crisis * 25 September 2008 * From New Scientist Print Edition. Subscribe <http://www.newscientist.com/subscribe.ns?promcode=nsarttop> and get 4 free issues. * Rob Jameson <http://www.newscientist.com/article.ns?id=mg19926754.200&print=true> Printable version WHAT'S the quickest way to kill a bank? As recent events in the financial world have shown, the answer is to deny them access to ready cash. Over the past year, a string of banking institutions have found themselves in such a "liquidity crisis": unable to convince the market they can honour their promises to pay back money they owe. The result has been a series of high-profile failures, from Northern Rock in the UK last year to Lehman Brothers last week. The crisis did not come without warning. Ten years ago this month, a giant hedge fund called Long-Term Capital Management collapsed when it too suffered a liquidity crisis. Yet banks and regulators seem not to have heeded the lessons from this wake-up call by improving the mathematical models that they use to manage their risk. That raises two key questions. How did the risk modellers get it so wrong? And what can they do to prevent similar crises in future? Banks are vulnerable to liquidity crises because they borrow money that may have to be repaid in the short term, and use it to back up more lucrative longer-term investments. If depositors withdraw their money and other lenders refuse to lend the bank the funds they need to replace it, the bank ends up in trouble because it can't easily turn its long-term assets into cash to make up the shortfall. Banks pay enormous sums to lure researchers away from other areas of science and set them to work building complex statistical models that supposedly tell the bankers about the risks they are running. So why didn't they see what was coming? The answer lies partly in the nature of liquidity crises. "By definition they are rare, extreme events, so all the models you rely on in normal times don't work any more," says Michel Crouhy head of research and development at the French investment bank Natixis, and author of a standard text on financial risk management. What's more, each liquidity crisis is inevitably different from its predecessors, not least because major crises provoke changes in the shape of markets, regulations and the behaviour of players. "Liquidity crises are rare, extreme events, so all the models you rely on in normal times don't work any more" On top of this, banks wrongly assumed that two areas of vulnerability could be treated in isolation, each with its own risk model. When the two areas began to affect each other and drive up banks' liquidity risk there was no unifying framework to predict what would happen, explains William Perraudin, director of the Risk Management Laboratory at Imperial College London. False assumption The first set of models covers the bank's day-to-day trading. These models typically assume that market prices will continue to behave much as they have in the past, and that they are reasonably predictable. Unfortunately, while this assumption may hold for straightforward financial instruments such as shares and bonds, it doesn't apply to the complicated financial instruments which bundle up different kinds of assets such as high-risk mortgages. What's more, information about the market prices of these products usually goes back only a few years, if it is available at all. "Statistical models based on short time series of data are a terrible way to understand [these kinds of] risks," says Perraudin. The models also assumed that the bank would be able to sell "problematic" assets, such as high-risk sub-prime mortgages, and this too turned out not to be true. "It's the combination of poor price risk modelling and being unable to sell out of the position that has produced the nightmare scenario," Perraudin says. The second set of risk models is intended to estimate the risk from borrowers failing to repay money they owe the bank. Because it's harder to sell off loans than bonds or stock, these models assume that the banks may have to bear the risks for longer. Such models were often regarded as the cutting edge of risk modelling, using sophisticated mathematics to predict how different debtors might be affected by economic conditions. However, Perraudin says these models mostly overlook how bad news can affect banks' ability to raise funds. "The real risk," he says, "turns out to be a cycle of drops." It plays out like this: word gets around that banks have got something on their hands that has dramatically lost value; this makes other institutions reluctant to lend them money to help them out, which in turn makes the value of their assets shrink further. The overall effect is to suck liquidity out of the market. Perraudin is working on a model for a hedge fund that takes account of this feedback, but he says it's a fiendishly difficult problem, partly because the models have to include a factor that captures the relationship between a bank's cost of borrowing and its riskiness in the eyes of other lenders. Insurance companies already use simple scenarios to capture such feedback effects, but for banks it's a new frontier in risk modelling - and they have only the haziest idea about how to go about it. Jonathan York of New York based SunGard Ambit, one of the banking industry's biggest suppliers of risk management software, says that one of the first steps for banks should be to spend more time assessing how their business strategy and portfolio will be perceived by outsiders during a crisis. He likens the behaviour of markets as they began picking off unstable institutions earlier this year to predatory animals attacking a herd: the first to go were the "outliers" in the banking herd that were perceived to be most vulnerable. The second step in updating risk management strategy is to look beyond individual banks' risks and analyse the risks to which the financial industry as a whole is exposed. It's a view supported by Markus Brunnermeier of Princeton University, New Jersey, author of a recent analysis of the sub-prime mortgage crisis. Until recently, he says, each bank had been content to use a measure called "value at risk" that predicted how much money it might lose from a given market position (see <http://www.newscientist.com/channel/being-human/mg19926754.200-the-blunders -that-led-to-the-banking-crisis.html#bx267542B1> "What do we stand to lose?"). Yet traditional measures of VaR largely ignore the degree to which the fate of a bank might be affected by other banks. "Banks need to look beyond their individual risks and analyse risks to the financial industry as a whole" Brunnermeier has developed a measure called Co-VaR which assesses the losses across a portfolio of banks under worst-case conditions. "It captures the fact that if I'm going under, it's bad for you too," says Brunnermeier. Capturing the degree to which bank fortunes are interconnected, and how this feeds market prices and liquidity, has become much more important as banks and other financial institutions have come to rely on loans from each other and from large investors rather than on customer deposits. Brunnermeier suggests that this is one of the reasons for the downturn in housing prices right across the US in recent months - an almost unprecedented event. In the past, the localised nature of US banking meant that booms and busts in housing were restricted to smaller areas. "This kind of national fall has happened before, back in the Great Depression, and it has happened in other countries," he points out. But recent risk analysis of the value of assets backed by mortgages - and therefore ultimately by the price of housing - has tended to use only more recent data and only US data. This meant they missed the really big risk of a national downturn in house prices hitting the banking system. It's a rueful comment that captures the mood of risk researchers right now. As they are having to admit, they still lack the tools to predict when the next liquidity crisis will come. >From issue 2675 of New Scientist magazine, 25 September 2008, page 8-9
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