Highly recommended: FT journalist Gillian Tett, a PhD in social anthropology, discusses her book on the financial crisis: Fool's Gold, at an LSE public lecture.
I haven't read the book yet, but it's on my list :-) Here are two nice excerpts that appeared in the FT. She does a great job of covering the birth and development of credit derivatives, CDOs, etc.
Genesis of the debt crisis
How panic gripped the world's biggest banks
Below is a discussion of correlation from the first excerpt.
The problem with correlation
Demchak was acutely aware that modelling the risks involved in credit derivatives deals had its limits. One of the trickiest problems revolved around the issue of “correlation”, or the degree to which defaults in any given pool of loans might be interconnected. Trying to predict correlation is a little like working out how many apples in a bag might go rotten. If you watch what happens to hundreds of different disconnected apples over several weeks, you might guess the chance that one apple might go rotten – or not. But what if they are sitting in a bag together? If one apple goes mouldy, will that make the others rot too? If so, how many and how fast?
Similar doubts dogged the corporate world. JP Morgan statisticians knew that company debt defaults are connected. If a car company goes into default, its suppliers may go bust, too. Conversely, if a big retailer collapses, other retail groups may benefit. Correlations could go both ways, and working out how they might develop among any basket of companies is fiendishly complex. So what the statisticians did, essentially, was to study past correlations in corporate default and equity prices and program their models to assume the same pattern in the present. This assumption wasn’t deemed particularly risky, as corporate defaults were rare, at least in the pool of companies that JP Morgan was dealing with. When Moody’s had done its own modelling of the basket of companies in the first Bistro deal, for example, it had predicted that just 0.82 per cent of the companies would default each year. If those defaults were uncorrelated, or just slightly correlated, then the chance of defaults occurring on 10 per cent of the pool – the amount that might eat up the $700m of capital raised to cover losses – was tiny. That was why JP Morgan could declare super-senior risk so safe, and why Moody’s had rated so many of these securities triple-A.
The fact was, however, that the assumption about correlation was just that: guesswork. And Demchak and his colleagues knew perfectly well that if the correlation rate ever turned out to be appreciably higher than the statisticians had assumed, serious losses might result. What if a situation transpired in which, when a few companies defaulted, numerous others followed? The number of defaults required to set off such a chain reaction was a vexing unknown. Demchak had never seen it happen, and the odds seemed extremely long, but even if there was just a minute chance of such a scenario, he didn’t want to find himself sitting on $100bn of assets that could conceivably go bust. So he decided to play it safe, and told his team to look for ways to cut their super-senior liabilities again, irrespective of what the regulators were saying.
That stance cost JP Morgan a fair amount of money, because it had to pay AIG and others to insure the super-senior risk, and those fees rose steadily as the decade wore on. In the first such deals with AIG, the fee had been just 0.02 cents for every dollar of risk insured each year. By 1999, the price was nearer 0.11 cents per dollar. But Demchak was determined that the team must be prudent.
