Software of the subprime crisis | FT Alphaville

Software of the subprime crisis

Here’s a novel idea about the CDO component of the subprime and financial crisis.

As late as 2003 CDOs were — believe it or not — still being described with words like “Toxic. Explosive. Opaque.” What helped the securities overcome that reputation — to become the ubiquitous and ‘safe’ triple-A assets that many financial players seemed to think they were in the run-up to the crisis — was a number of specially-tailored computer programmes.

Intex. Wall Street Analytics. Risk Metrics — and so on.

These things could analyse the hundreds — often thousands — of mortgages that formed the underlying basis of a single CDO. Like the rating agencies’ gifting of triple-As, they could make investors think they understood the risks and rewards of each and every moving part in a deal.

Of course, we know now that the underlying assumptions weren’t adequate. They were vulnerable to statistical stationality, tail events weren’t being posited, correlation wasn’t being totally encompassed, and so on.

Nevertheless, it’s worth wondering whether these computer programmes themselves played a role in boosting CDOs into the mainstream. And that’s just what University of Edinburgh professor Donald MacKenzie did in Thursday’s FT:

Although few outsiders have heard of it, the single most important language of mortgage-backed securities and similar products is a system called Intex. It includes a computer language for defining deals’ intricate cash flow rules, a graphics-based tool for designing deals, and a truly remarkable computerised “library” of the parameters of the underlying asset pools and the cash flow rules of more than 20,000 deals. Intex is not cheap – one user told me his bank pays about $1.5m a year for it – and it has competitors such as Bloomberg, but it is essential for all serious participants in structured securities.

In July a friendly banker showed me Intex in action. He chose a particular mortgage-backed security, entered its price and a figure for each of prepayment speed, default rate, and loss severity. In less than 30 seconds, back came not just the yield of the security, but the month-by-month future interest payments and principal repayments, including whether and when shortfalls and losses would be incurred. The psychological effect was striking: for the first time, I felt I could understand mortgage-backed securities.

Of course, my new-found confidence was spurious. The reliability of Intex’s output depends entirely on the validity of the user’s assumptions about prepayment, default and severity. Nevertheless, it is interesting to speculate whether some of the pre-crisis vogue for mortgage-backed securities resulted from having a system that enabled neophytes such as myself to feel they understood them. Certainly, like any language, Intex aided communication. If you were planning a mortgage-backed deal, you could construct an Intex file, make it available to potential investors, and use it to discuss the deal’s features, modify those features, and gauge investors’ interest.

And when it came to CDOs, this was one of the system’s many selling points. But even Intex found it hard to cut through the many machinations of a single deal. The programme became much slower to run, which meant the people who were using it started taking some short cuts — and not the keyboard kind either:

The limits of the language came when mortgage-backed securities were repackaged into collateralised debt obligations (CDOs), complex debt securities based on pools of other assets. You could still run Intex, first for each of the securities and then for the CDO, but it could be a slow process. Often, CDOs included not just mortgage-backed securities, but tranches of other CDOs, each maybe incorporating further CDOs. This multiplied enormously the number of underlying mortgage pools, causing a single valuation run to take hours. (On occasion, each of a pair of CDOs would buy a tranche of the other, creating a “loop” that slowed analysis). Sometimes, users did little more than one run using the prepayment, default and severity rates judged most likely. Those (such as the rating agencies) that needed to do more nearly all took a fatal shortcut. Instead of analysing CDOs from the bottom (the underlying pools of mortgages) up, they shifted to a different mathematical language, which treated a CDO’s components (mortgage-backed securities and tranches of other CDOs), in effect, as if they were corporate bonds, with their properties inferred from their ratings. This often led to serious underestimation, especially by rating agencies, of correlation among these components.

Triple-A ratings that assured.

The formulas that had risk figured out.

The software systems that made complex structures comprehensible.

All part of the structured finance security blanket that helped boost the CDO market to prominence — and future infamy.

Related links:
CDOs move into the mainstream – Risk, 2004
The formula that felled Wall Street – Sam Jones, FT
My Manhatten Project: How I helped build the bomb that blew up Wall Street – NY Mag
“What happens To MBS and CDOs and CDS when subprime defaults rise?” – Naked Capitalism, 2007