http://en.wikipedia.org/wiki/Goodhart’s_law

Danielsson even has a corollary to that as “A risk model breaks down when used for regulatory purposes”

See this paper
The Emperor has no Clothes: Limits to Risk Modelling
http://www.econ.ucy.ac.cy/seminars/danielsson.pdf

Two U.S. senators, citing a plunge in regulatory fines against companies and individuals, yesterday asked government watchdogs to examine whether the federal Securities and Exchange Commission isn’t being aggressive in fighting fraud.

Sen. Jack Reed said in a speech that he and Senate Banking Committee Chairman Christopher Dodd, D-Conn., asked the Government Accountability Office to look at the SEC’s enforcement division. Reed sought the review after sanctions fell about 51 percent to $1.6 billion in fiscal 2007.

The drop “raises questions about whether changes have taken place in enforcement philosophy or scope of activity,” Reed said in a statement. The GAO will examine whether the enforcement division “has sufficient staff and funds to perform its mission and whether there have been fundamental changes in operation to the way they handle cases.”

This is the second time in 18 months that a member of Congress has called for a review of the SEC’s vigilance. Since taking over in August 2005, SEC chairman Christopher Cox has given commissioners more power over enforcement penalties as the agency considers whether fining companies also hurts shareholders.

You obviously don’t hang with computer guys. “Automagically” dates back to the nineties or before. Usually refers to some hacked up initialization code.

That sounds like Minsky in a nutshell.

Two bottles of vodka can kill you if you drink them all at once. If you try, you will vomit, and so save your life. If you are a hardcore drinker and try to chug them, you’ll probably still upchuck and live to try again. If you’re hardcore and take it slow, though—knock a shot down, talk a bit, take another, repeat—you can get a dose plenty enough to kill you while you are still upright and holding (mostly) sensible conversation. Even if you ‘know your limit’ it can change on a give day; you’re more dehydrated to start, going a little too fast, etc., etc. It doesn’t have to be a big belt that embalms you.

Now, our systems was _supposed_ to be that the barkeep would cut off the hardcore drunk before the dose got toxic. But, in our situation the pourer was a pusher. If you learn to ‘manage risk’ like you can learn to ‘hold your liquor,’ you have a great chance of getting enough in you to be a dead man talking. Shake hands with anyone on Wall Street today looking for more capital and asset price support and you’ll know just what I mean.

The issue with correlation can also be seen from connectivity processes in Game of Life simulations, which not only fit the context discussed here but are doubtless employed by some of the quant boys. Where connectivity is incomplete, you can have sustainable local optima—i.e. locally exploitable profit possibilities in this case—but where connectivity is high (every damn body’s got the same data and tools), than the system as a whole locks out differential valuations. If everyone gets into something, get out, is the only real lesson.

Risk assessment in the present circumstance has been totally faulty, but I would add another issue I don’t see in Persaud’s summary (or at least didn’t see, I’m up past my bedtime). If risk assessment is performed with the goal of avoiding risk, that outcome is more likely. If risk assessment is performed with the goal of taking on ‘manageable risk,’ then _that_ outcome is the more likely. The whole risk assessment game has been to find rationales for taking on as much as folks thought they could handle, not to avoid what they thought they couldn’t handle. This may sound small, but when every player at every level is looking to tack on just a litt-tt-ttle more risk, what you have is a system biased toward accumulating risk. There is a logical outcome to such a process; we live it today. It doesn’t matter, at a deeper level, whether the risk taker is someone with $50K on plastic and $0 in savings, a homeowner taking on an ARM above their cash flow with a plan to refi before the reset, or St. Alan the Mendacious believing he can bloom bubbles to grow the plutocracy ’cause he has the public till to clean up the mess and the mess will be small enough to be clean-upable. If the bias is to take on just a lit-tt-ttllllle more risk, one is guaranteed to get loaded with more risk than can be managed in an adverse circumstance. All that’s to say, risk management has been a fool’s game because the hidden bias has been to take it not to shake it. Except for Warren B., who treats risk like a disease and so will be the last man standing tall.

To a: No, the present situation is most assuredly _not_ unprecedented. Bubbles all look the same, and they are all readily recognizable on the way up. Look at SE Asia in the 90’s, Argentina, or any situation you want to pick. All it takes is to accept totally inflated values for assets as ‘normal’ and to use leverage in substantial excess of collateral. We have 400 years of modern capital doing the same damn thing, and they always do it the same damn way with a shiny new ribbon on the package. Just like we have.

It has been my view for some time that the only really stable public financial regulatory policy would be one _actively antagonistic to fast booms_ and deeply antipathetic to bubbles. Bubbles make big profits for big profiteers on the way up; this is why no one wants to prevent them. But they do massive damage. When we re-write the rules for public financial regulation in this country, preventing bubbles should be an explicit policy mandated to the Fed and public auditors. I’m not holding my breath on this one, though. Jus’ too many folks want to get rich rather than live well. It isn’t zero sum, this ‘get rich’ thing, but it tends to sum to zero repeatedly.

Is there much else to this?

Steve

I think it is probably incorrect to say that models “failed”. The models simply did not consider the scenario that we are living through.

There are *always* scenarios which will make a bank go bust. Consider a conservatively managed bank in the scenario where housing prices overnight go down 50% and people starting mailing in their keys. It would go bust. There’s no way around it, so long as a bank is the banking business.

Or consider the case of the Nasdaq bubble. At the height of the bubble if the market had gone down 80% in one day, then at least one IB would have gone bust. At least one, maybe more.

These are extreme scenarios, and they are not considered in the analyses that IBs run. At the height of the Nasdaq bubble suppose someone thought a scenario of -50% to be possible. Now, after the fact, we can say that it didn’t happen. Was the person wrong? Or was it a possibility that needed to be considered?

So banks can’t and won’t consider all possibilites. Some extreme scenarios will sink the bank; that’s not news. The hard part is to decide, beforehand, what is *plausible*.

For the proverbial “black swan” (or, for want of a better description, End of the World) events, the only thing that proves the plausibility of the event is if it happens. That makes it very difficult to know, beforehand, exactly what degree of scenario needs to be considered. At the height of the Nasdaq bubble what was the proper extreme scenario, for which a bank needed to be prepared? -25%? -40%? -50%? – 75%? -90%?

Now I don’t have any pity for IBs, but the current scenario is unprecedented. It’s a genuine “black swan” event. So post hoc it’s very easy to say, “Hey guys, you should have considered this.” But before the event, it’s very hard – no, it’s impossible – to calibrate.

So I’d say it’s not a question of tweaking the models. Post hoc there’s obviously ways to do that, but I imagine the IBs models are actually robust enough, and what was lacking was considering the present scenario as plausible, as an extreme event which needed to be considered and to be reserved against (and here the regulators, with their allegiance to VAR, were not helpful).