Whenever I write about banks’ rising Value-at-Risk, a bunch of commenters tells me that duh of course VaR is rising, because VaR is a function of volatility, and volatility has gone up. So here’s my question: can someone come up with a baseline VaR chart, for a hypothetical bank which had, say, a fixed $1 million investment in the S&P 500. What would its quarterly Value-at-Risk have looked like over the past couple of years?

Would the decrease in volatility this year have shown up as decreased VaR in say the second quarter? Or do the volatility calculations go back so far that only now are we losing the Great Moderation datapoints and using volatility numbers only from the era of increased volatility?

Armed with that kind of baseline chart, we’ll be able to tell much more easily, for any given bank, whether it’s actually increasing the size of its bets, or whether increased VaR is simply a statistical necessity given the recent history of volatility. Does such a chart exist?

Update: Phorgy comes through. All banks calculate VaR differently, but this is a really useful resource. Basically Var increased enormously in 2008, and after that slowed down sharply in 2009, possibly even dropping significantly. Which means, I think, that any significant increases in VaR in 2009 can’t be blamed on increased volatility.

Philip Delves Broughton notes that Robert McNamara, one of Harvard Business School’s most notorious graduates, basically did in the field of war what Wall Street quants did in the field of finance:

The journalist David Halberstam wrote that McNamara mistrusted people who did not speak his language of statistics and hard data. If it ever came down to one person saying something “just didn’t feel right” or that it “smelled wrong”, he would always go with his facts over their feeling. Fatally, in the case of Vietnam, the data he received was not accurate.

When Wall Street quants fail to account for model risk, they can end up losing hundreds of billions of dollars. But that’s an improvement over what happened when McNamara failed to account for model risk: those losses were much worse.

Paul Wilmott has words of wisdom for anybody in the financial-services industry who’s putting a model together:

At every stage of valuation and model development you must be asking questions about risk and robustness. It is dangerous to come up with some fancy model and only afterwards start asking questions about model error. Anyone who has ever calibrated a model knows that the methods used to mitigate model risk almost come as an afterthought, and are totally inconsistent with the original model. This need not be the case.

The problem is that developing a model is the sort of thing which (a) quants are trained to do, and (b) can, eventually, make money. While mitigating model risk is a very recondite field which very few people have any expertise with; what’s more, it doesn’t really make money in and of itself. Where will all the model-risk modellers come from?

Binyamin Appelbaum has details of the new regulations surrounding securitization, and it all looks incredibly unworkable to me, especially the central plank:

Lenders would be required to retain at least 5 percent of the risk of losses on each package of loan pieces, known as an asset-backed security…

The plan also would prohibit firms from hedging that risk, meaning that they could not make an offsetting investment.

What on earth is a bank’s chief risk officer supposed to do with an edict that she cannot hedge a certain chunk of the bank’s balance sheet? I can see that she might not be able to explicitly create a synthetic CDO to bring that idiosyncratic risk down to zero. But if a bank has exposure from securitizing credit-card receivables, say, or commercial real estate leases, or student loans, or residential mortgages, then similar risk will reside elsewhere on the balance sheet, and the bank will — and should — just reduce the amount of that risk instead. It has the same result, from an all-over risk perspective, and the new regulation is rendered utterly toothless.

As for the idea that the ratings agencies should make it clear that ABS aren’t corporate bonds, well, as Agnes points out, that’s pretty silly: I think everybody knows that by now. The problem is that if there’s a separate second scale for ABS (and maybe even a third scale for munis), no one will have a clue what the new ratings are supposed to mean. It would be a bit like being given two apples, and told that one costs 15 foos while the other costs 17 bars.

I would rather encourage the ratings agencies to try and make credit ratings as laterally comparable as possible, and to try to set ratings so that you don’t have the enormous default-rate discrepancies that exist right now between different asset classes. Investors could then judge for themselves the degree to which the ratings agencies had succeeded.

My fear is that ratings agencies might start issuing separate credit ratings for munis, which will be considered much safer than the equivalent credit ratings on corporate bonds, just as a wave of muni defaults is about to hit. In general, the key here is to decrease the importance of the ratings agencies, rather than trying to regulate them on the grounds of how important they are.

But a couple of the proposals make sense: paying originators of securitized loans gradually, over time, for instance, rather than up-front when the loans are securitized; or standardizing contract language in the ABS market to make such securities easier to compare to each other. They just won’t make an enormous amount of difference.

Back in March, Rick Bookstaber published a blog entry entitled “The Fat-Tailed Straw Man”, which seems to imply that Nassim Taleb attacks Wall Street practices on the grounds that its practitioners believe in normal distributions.

The result of that blog entry, and of a few other criticisms of Taleb in various places, is that Nassim has now published an extremely useful technical appendix to The Black Swan. It includes substantially all his relevant scientific and technical papers, and essentially comprises a gauntlet being thrown down to those who would criticize him. If you want to attack my ideas, he’s saying, that’s fine, but please first do me the favor of looking at where they’re laid out in detail, as opposed to where they’re laid out in newspaper quotes or in a literary book.

There is certainly a lot of confusion in the public mind when it comes to philosophical arguments about fat tails and risk management, and it’s easy for journalists to distill things into easy soundbites about normal distributions and the like. I, for one, would like at some point to write about Taleb’s important paper on Errors, Robustness, and the Fourth Quadrant, which looks at the history of thousands of data series going back decades — but I haven’t found a very clear way of trying to explain its details in English.

The Black Swan has proved to be a very popular book largely because it doesn’t even attempt to do that: instead it talks philosophically about the conclusions one draws after looking at the data. But if you want to attack Taleb for drawing the wrong conclusions, I think he’s right: you shouldn’t attack the book, but rather the empirical research which underlies it.