The Rise of the (Financial) Machines
BartlebyScrivener writes "A New York Times Op-Ed quoting Freeman and George Dyson wonders if Wall Street geeks and 'quants' outsmarted themselves with computer algorithms to create the current financial debacle: 'Somehow the genius quants — the best and brightest geeks Wall Street firms could buy — fed $1 trillion in subprime mortgage debt into their supercomputers, added some derivatives, massaged the arrangements with computer algorithms and — poof! — created $62 trillion in imaginary wealth. It's not much of a stretch to imagine that all of that imaginary wealth is locked up somewhere inside the computers, and that we humans, led by the silverback males of the financial world, Ben Bernanke and Henry Paulson, are frantically beseeching the monolith for answers.'"
The quoted essay from George Dyson is available at Edge.
Check out this 47 minute video for a very easy to understand and clear explanation.
http://video.google.com/videoplay?docid=-9050474362583451279
Unless you've been through university on some Economics degree - you were probably unaware of this.
Now, there are several ways you can do this. One is to run a massive monte-carlo simulation on all the input data, something that all financial software supports. There is a problem with this though, and that is that it requires some massive CPU power. Even a smaller bank would require many hundreds if not thousands of CPU's chugging away for 12 hours or more to come up with the proper numbers.
The solution they've come up with is called "historical VaR". What they do is to use the historical market data for the last year instead of random data which would be used otherwise.
The obvious problem with historical VaR is of course that it doesn't take unexpected market movements into account. If a drop such as the ones that we have seen recently haven't happened in the last year, VaR won't take such a scenario into account.
So, in short, VaR only really works in "normal" market conditions. It doesn't take extreme movements into account.
I'm confused, and wondering what you're talking about.
The original leverage ratios were set by Basel, which pegged them at 8% (or 12.5:1). In 2004, this was updated. It's still 8%, but now assets are risk-weighted.
Claims on depository banks were were given the following risk weights:
AA- 0%
A- 20%
BBB- 50%
B- 100%
(worse) 150%
Unrated 100%
And to make matters worse, claims on securities firms were defined to be the same as claims on banks.
And the kicker, claims secured by residential mortgages were weighted at 35%.
As such, though the leverage ratio was officially 12.5, somebody who held nothing but mortgages could be levered up 35:1. And if you owned some bank issues, you could get nearly infinite.
But I'm wondering... what makes you think that these limits were going to be further increased?
Only most of the loans that were rolled into the mortgage-backed securities were made by institutions that were not governed by the CRA.
Nice try though.
That's just the point. They didn't do the sociology of the people taking the NINA variable rate mortgages. A lot of them were speculators who were buying properties with no money down and then flipping them. This had two effects: driving the price up because they could; and, making it looks like NINA variable rate mortgages were getting paid off with only slightly higher risk than normal loans. Once the house-flippers got out and interest rates went up, the people with no assets and no income left holding the variable rate mortgages couldn't pay and the house of cards collapsed. The model was based on speculators flipping houses, not on real owners.
If the quant people had done the sociology instead of just running the numbers, they would know who was buying and why the data appeared the way it did.
The Rise and Fall of Online Community
the CRA did not compel lending to bad credit risks.
what it did do was compel banks to actually assess people based on credit rather than arbitrary geographic area.
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