mathbabe

The financial crisis has given rise to a series of catastrophes related to mathematical modeling.

Time after time you hear people speaking in baffled terms about mathematical models that somehow didn’t warn us in time, that were too complicated to understand, and so on. If you have somehow missed such public displays of throwing the model (and quants) under the bus, stay tuned below for examples.

A common response to these problems is to call for those models to be revamped, to add features that will cover previously unforeseen issues, and generally speaking, to make them more complex.

For a person like myself, who gets paid to “fix the model,” it’s tempting to do just that, to assume the role of the hero who is going to set everything right with a few brilliant ideas and some excellent training data.

Unfortunately, reality is staring me in the face, and it’s telling me that we don’t need more complicated models.

If I go to the trouble of fixing up a model, say by adding counterparty risk considerations, then I’m implicitly assuming the problem with the existing models is that they’re being used honestly but aren’t mathematically up to the task.

But this is far from the case – most of the really enormous failures of models are explained by people lying. Before I give three examples of “big models failing because someone is lying” phenomenon, let me add one more important thing.

Namely, if we replace okay models with more complicated models, as many people are suggesting we do, without first addressing the lying problem, it will only allow people to lie even more. This is because the complexity of a model itself is an obstacle to understanding its results, and more complex models allow more manipulation.

Example 1: Municipal Debt Models

Many municipalities are in shit tons of problems with their muni debt. This is in part because of the big banks taking advantage of them, but it’s also in part because they often lie with models.

Specifically, they know what their obligations for pensions and school systems will be in the next few years, and in order to pay for all that, they use a model which estimates how well their savings will pay off in the market, or however they’ve invested their money. But they use vastly over-exaggerated numbers in these models, because that way they can minimize the amount of money to put into the pool each year. The result is that pension pools are being systematically and vastly under-funded.

Example 2: Wealth Management

I used to work at Riskmetrics, where I saw first-hand how people lie with risk models. But that’s not the only thing I worked on. I also helped out building an analytical wealth management product. This software was sold to banks, and was used by professional “wealth managers” to help people (usually rich people, but not mega-rich people) plan for retirement.

We had a bunch of bells and whistles in the software to impress the clients – Monte Carlo simulations, fancy optimization tools, and more. But in the end, the banks and their wealth managers put in their own market assumptions when they used it. Specifically, they put in the forecast market growth for stocks, bonds, alternative investing, etc., as well as the assumed volatility of those categories and indeed the entire covariance matrix representing how correlated the market constituents are to each other.

The result is this: no matter how honest I would try to be with my modeling, I had no way of preventing the model from being misused and misleading to the clients. And it was indeed misused: wealth managers put in absolutely ridiculous assumptions of fantastic returns with vanishingly small risk.

Example 3: JP Morgan’s Whale Trade

I saved the best for last. JP Morgan’s actions around their $6.2 billion trading loss, the so-called “Whale Loss” was investigated recently by a Senate Subcommittee. This is an excerpt (page 14) from the resulting report, which is well worth reading in full:

While the bank claimed that the whale trade losses were due, in part, to a failure to have the right risk limits in place, the Subcommittee investigation showed that the five risk limits already in effect were all breached for sustained periods of time during the first quarter of 2012. Bank managers knew about the breaches, but allowed them to continue, lifted the limits, or altered the risk measures after being told that the risk results were “too conservative,” not “sensible,” or “garbage.” Previously undisclosed evidence also showed that CIO personnel deliberately tried to lower the CIO’s risk results and, as a result, lower its capital requirements, not by reducing its risky assets, but by manipulating the mathematical models used to calculate its VaR, CRM, and RWA results. Equally disturbing is evidence that the OCC was regularly informed of the risk limit breaches and was notified in advance of the CIO VaR model change projected to drop the CIO’s VaR results by 44%, yet raised no concerns at the time.

I don’t think there could be a better argument explaining why new risk limits and better VaR models won’t help JPM or any other large bank. The manipulation of existing models is what’s really going on.

Just to be clear on the models and modelers as scapegoats, even in the face of the above report, please take a look at minute 1:35:00 of the C-SPAN coverage of  former CIO head Ina Drew’s testimony when she’s being grilled by Senator Carl Levin (hat tip Alan Lawhon, who also wrote about this issue here).

Ina Drew firmly shoves the quants under the bus, pretending to be surprised by the failures of the models even though, considering she’d been at JP Morgan for 30 years, she might know just a thing or two about how VaR can be manipulated. Why hasn’t Sarbanes-Oxley been used to put that woman in jail? She’s not even at JP Morgan anymore.

Stick around for a few minutes in the testimony after Levin’s done with Drew, because he’s on a roll and it’s awesome to watch.