QuickTime version 7.6.2

June 2, 2009

Apple has released a new version of its QuickTime multi-media software, version 7.6.2.  According to Apple, this update improves compatibility with Apple PreRes media, and with iTunes.  It also fixes several security vulnerabilities that would allow your system to be compromised by  maliciously-crafter media files.  Updated versions were released for both Mac OS-X and Windows; the download links are here:

(One note especially for sysadmin folks:  Apple’s Web site tries to auto-detect the operatung system you are using, and offer just the correct download.  This falls in the category of too-clever-by-half, and causes problems if the machine you are using to do the download is not the target machine for the update.  This will definitely be the case if you use Linux.  For Firefox users, the “User Agent Switcher” extension, which makes the browser lie about the system it’s running on, can be quite handy in this sort of situation.)

Because this update contains significant security fixes, I suggest installing it as soon as it’s convenient.

Formulas for Disaster, Part 2

June 2, 2009

A former colleague of mine recently wrote to me about another article on the Wall Street meltdown, “My Manhattan Project”, by Michael Osinsky, in New York magazine.  Mr. Osinsky started working as a software developer on Wall Street in 1985.  His early assignments were highly technical projects: computer systems’ plumbing, in essence. But he soon grew interested in the nascent market for mortgage-backed securities [MBSs] or Collateralized Mortgage Obligations [CMOs], the first mainstream examples of Collateralized Debt Obligations [CDOs].  Although his boss described them as instruments “designed to keep programmers employed”, those early securities were considerably more straightforward than some of their descendants.  The mortgages involved were standardized, and originated by GNMA (the Government National Mortgage Association), Fannie Mae, or Freddie Mac.  They had standard terms, and the mortgages that were bundled were all considered of good quality.  Osinsky says, and I agree, that there was nothing wrong with this basic concept:

But in my view, and of course I’m inescapably biased, there’s nothing inherently flawed about securitization. Done correctly and conservatively, it increases the efficiency with which banks can loan money and tailor risks to the needs of investors. Once upon a time, this seemed like a very good idea, and it might well again, provided banks don’t resume writing mortgages to people who can’t afford them. Here’s one thing that’s definitely true: The software proved to be more sophisticated than the people who used it, and that has caused the whole world a lot of problems.

In the mid-1980s, I did some work on MBSs.  One of the modeling challenges was to estimate the rate at which the underlying mortgages would be pre-paid: some because the mortgagee had to move, and sell the property, some because the mortgagee got a windfall, or the opportunity to refinance at a more attractive rate.  Data was kept on the prepayment rates of various mortgage pools,  and sifted through by age of the loan, geographic location, and other factors.  It was a bit complex, but a more-or-less tractable problem in statistical estimation.

As the 1980s drew to a close, interest rates were tending to drop, and more people were entering the house-buying (= mortgage) market.  Mr. Osinsky and his colleagues worked on making the software that assembled the CDOs faster and easier to use.

Working with another programmer, I wrote a new mortgage-backed system that enabled investors to choose the specific combinations of yield and risk that they wanted by slicing and dicing bonds to create new bonds.

The structuring of these securities got more imaginative, and more complicated.  The new, easy-to-use software made it possible for people on the trading desk — not just quantitative specialists — to structure deals to order:

Traders loved it. What had taken days before now took minutes. They could design bonds out of bonds, to provide the precise rate of return that an investor wanted.

This was also music to the ears of investment bank managers: if all the traders could work on structuring new securities, then the volume of new issues and the volume of trading would go up, which tended to have a pleasant effect on their bonuses.

However, as I’ve suggested before (see the post “In the Beginning …”), making complex tools more accessible and easy to use does not always produce the intended effect:

The drive to simplify the user’s contact with the machine has an inherent side effect of disguising the complexity of a given task. Over time, the users of any software are inured to the intricate nature of what they are doing. Also, as the software does more of the “thinking,” the user does less.

The menu of assets available for securitization was expanded considerably, too, with more generalized CDOs:

I asked my colleagues, what was a CDO exactly? Like CMOs, they were structured products, but the underlying collateral was not limited to home mortgages. They could be anything—corporate bonds, subprime-mortgage bonds, swaps, or simply air, like the synthetic CDOs: They could be CDOs underwritten by the bonds of other CDOs, CDOs squared.

These securities were, obviously, considerably more complicated and less transparent than the original MBSs / CMOs.  But it has always been a motto in at least some quarters on Wall Street that “nothing succeeds like excess.”  The new securities were pitched, and sometimes rated, on the basis that the underlying collateral was diversified:

“Diversity of collateral” was the pitch. Some things could go bad, but not everything at once. It never has, except during the Depression, and we’re so much smarter now. That could never happen again.

In the case of the original mortgage-backed securities, the complexities were in the structure of the security itself, and in estimating the pre-payment rates.  With the new CDOs, which included all kinds of debt, some of it distinctly less than investment grade, the big problem became how to estimate the rate of defaults by the borrowers.  I wrote in the first post in this series about  how well that’s worked for us.  I have a friend who describes it this way: “Wall Street figured out how to make a silk turd out of a sow’s ear.”

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