JPMorgan Holds A $3 Billion Reserve For Quant Screw Ups

Tyler Durden's picture

Much has been said on these pages and elsewhere about the dangers embedded within quant groupthink, in which an ever increasing prevalence of fewer performing factors means that more and more speculators (note: not investors) line up on the same side of the trade pushing up offers, only to experience a regime change based on some heretofore unexpected exogenous event which renders existing signal translation models useless, and causes all former buyers to join the sellers. Whether that would result in a bidless market remains to be seen. If October 1987 is any indication, all signs point to yes. Yet in a sign that at least the bigger bankers may be anticipating just such an outcome, the Economist has disclosed that JP Morgan, in addition to reserving for general loan loss provisions on its balance sheet, has now taken a $3 billion reserve against quant error (yes, quants can be wrong... and for a lot of money at that). Just how many other investment banks demonstrate this kind of prudence? Without any specific regulatory guidelines for quant capital provisioning, we have no idea. While the bulge brackets may have joined JPM in a comparable form of "insurance" it is a certainty that the thousands of newly cropped up quant trading firms not only have no such reserves, but should a dramatic market reversal transpire, it is inevitable that wholesale asset dumping will have to take place to cover losses. And this assumes no leverage. Is the market prepared for such a contingency?

The ever increasing role of mathematics in financial modeling is no secret. The Economist writes:

By 2007 finance was attracting a quarter of all graduates from the California Institute of Technology. These eggheads are now in the dock, along with their probabilistic
models. In America a congressional panel is investigating the models’
role in the crash. Wired, a publication that can hardly be
accused of technophobia, has described default-probability models as
“the formula that killed Wall Street”. Long-standing critics of
risk-modelling, such as Nassim Nicholas Taleb, author of “The Black
Swan”, and Paul Wilmott, a mathematician turned financial educator, are
now hailed as seers. Models “increased risk exposure instead of
limiting it”, says Mr Taleb. “They can be worse than nothing, the
equivalent of a dangerous operation on a patient who would stand a
better chance if left untreated.”

The Economist focuses initially on modelling as pertains to complex structured finance models: a venture which imploded spectacularly after the housing bubble burst.

The models went particularly awry when clusters of mortgage-backed
securities were further packaged into collateralised debt obligations
(CDOs). In traditional products such as corporate debt, rating agencies
employ basic credit analysis and judgment. CDOs were so complex that
they had to be assessed using specially designed models, which had
various faults. Each CDO is a unique mix of assets, but the assumptions
about future defaults and mortgage rates were not closely tailored to
that mix, nor did they factor in the tendency of assets to move
together in a crisis.

The problem was exacerbated by the credit raters’ incentive to
accommodate the issuers who paid them. Most financial firms happily
relied on the models, even though the expected return on AAA-rated
tranches was suspiciously high for such apparently safe securities. At
some banks, risk managers who questioned the rating agencies’ models
were given short shrift. Moody’s and Standard & Poor’s were assumed
to know best. For people paid according to that year’s revenue, this
was understandable. “A lifetime of wealth was only one model away,”
sneers an American regulator.

Moreover, heavy use of models may have changed the markets they were
supposed to map, thus undermining the validity of their own
predictions, says Donald MacKenzie, an economic sociologist at the
University of Edinburgh. This feedback process is known as
counter-performativity and had been noted before, for instance with
Black-Scholes. With CDOs the models’ popularity boosted demand, which
lowered the quality of the asset-backed securities that formed the
pools’ raw material and widened the gap between expected and actual
defaults (see chart 3).

Yet while the failure in structured finance has been well documented, and the abnormal reliance on such theoretical constructs as the Copula function, for some reason the same thinking has never shifted to other concepts which have a direct intervention in day-to-day, liquid markets such as those involving plain vanilla stocks. Chief among these is the artificial construct of VaR, or Value at Risk.

For some, the crisis has shattered faith in the precision of models and
their inputs. They failed Keynes’s test that it is better to be roughly
right than exactly wrong. One number coming under renewed scrutiny is
“value-at-risk” (VAR), used by banks to measure the risk of loss in a
portfolio of financial assets, and by regulators to calculate banks’
capital buffers. Invented by eggheads at JPMorgan in the late 1980s,
VAR has grown steadily in popularity. It is the subject of more than
200 books. What makes it so appealing is that its complex formulae
distil the range of potential daily profits or losses into a single
dollar figure.

Frustratingly, banks introduce their own quirks into VAR
calculations, making comparison difficult. For example, Morgan
Stanley’s VAR for the first quarter of 2009 by its own reckoning was
$115m, but using Goldman Sachs’s method it would have been $158m. The
bigger problem, though, is that VAR works only for liquid securities
over short periods in “normal” markets, and it does not cover
catastrophic outcomes. If you have $30m of two-week 1% VAR, for
instance, that means there is a 99% chance that you will not lose more
than that amount over the next fortnight. But there may be a huge and
unacknowledged threat lurking in that 1% tail.

So chief executives would be foolish to rely solely, or even
primarily, on VAR to manage risk. Yet many managers and boards continue
to pay close attention to it without fully understanding the
caveats—the equivalent of someone who cannot swim feeling confident of
crossing a river having been told that it is, on average, four feet
deep, says Jaidev Iyer of the Global Association of Risk Professionals.

Regulators are encouraging banks to look beyond VAR. One way is to
use CoVAR (Conditional VAR), a measure that aims to capture spillover
effects in troubled markets, such as losses due to the distress of
others. This greatly increases some banks’ value at risk. Banks are
developing their own enhancements. Morgan Stanley, for instance, uses
“stress” VAR, which factors in very tight liquidity constraints.

Like its peers, Morgan Stanley is also reviewing its stress testing,
which is used to consider extreme situations. The worst scenario
envisaged by the firm turned out to be less than half as bad as what
actually happened in the markets. JPMorgan Chase’s debt-market stress
tests foresaw a 40% increase in corporate spreads, but high-yield
spreads in 2007-09 increased many times over. Others fell similarly
short. Most banks’ tests were based on historical crises, but this
assumes that the future will be similar to the past. “A repeat of any
specific market event, such as 1987 or 1998, is unlikely to be the way
that a future crisis will unfold,” says Ken deRegt, Morgan Stanley’s
chief risk officer.

Of course quants will be the first to ridicule stress scenarios, simple due to the way they approach the future which is always, in every situation, merely a mean reversion phenomenon. That the mean itself may have strayed over the past 20-30 years is irrelevant. And until the actual crash occurs, nobody will ever admit that blind faith in regression based models is folly.

For VAR, it may be hopeless at signalling rare severe losses, but the
process by which it is produced adds enormously to the understanding of
everyday risk, which can be just as deadly as tail risk, says Aaron
Brown, a risk manager at AQR. Craig Broderick, chief risk officer at
Goldman Sachs, sees it as one of several measures which, although of
limited use individually, together can provide a helpful picture. Like
a slice of Swiss cheese, each number has holes, but put several of them
together and you get something solid.

Tail risk is indeed nothing new, with some economists such as Nassim Taleb having made a career out of it. Yet it is precisely the nature of statistics, as Taleb will point out, that makes modeling for fat tails so complex. This complexity may come from the very nature of the modelling systems, the clash of various corporate cultures, the infrastructural inability to process enough risk factors, or simply human error.

A report by bank supervisors last October pointed to poor risk
“aggregation”: many large banks simply do not have the systems to
present an up-to-date picture of their firm-wide links to borrowers and
trading partners. Two-thirds of the banks surveyed said they were only
“partially” able (in other words, unable) to aggregate their credit
risks. The Federal Reserve, leading stress tests on American banks last
spring, was shocked to find that some of them needed days to calculate
their exposure to derivatives counterparties.

The banks with the most dysfunctional systems are generally those,
such as Citigroup, that have been through multiple marriages and ended
up with dozens of “legacy” systems that cannot easily communicate with
each other. That may explain why some Citi units continued to pile into
subprime mortgages even as others pulled back.

In the depths of the crisis some banks were unaware that different
business units were marking the same assets at different prices. The
industry is working to sort this out. Banks are coming under pressure
to appoint chief data officers who can police the integrity of the
numbers, separate from chief information officers who concentrate on
system design and output.

Quant Paul Wilmott turned against the prevailing grain a year ago, and in a scathing Op-Ed denounced the self-perceived infallibility of quants. And as always the case, he is right: in their intellectual sophistry the math Ph.D. would be the last to admit that their worldview is wrong (about as bad as economist in that regard). The only issue is economist are generally irrelevant to actual market activities, whereas Fields' Medal winners tend to have an ever greater impact in the market.

The way forward is not to reject high-tech finance but to be honest
about its limitations, says Emanuel Derman, a professor at New York’s
Columbia University and a former quant at Goldman Sachs. Models should
be seen as metaphors that can enlighten but do not describe the world
perfectly. Messrs Derman and Wilmott have drawn up a modeller’s
Hippocratic oath which pledges, among other things: “I will remember
that I didn’t make the world, and it doesn’t satisfy my equations,” and
“I will never sacrifice reality for elegance without explaining why I
have done so.” Often the problem is not complex finance but the people
who practise it, says Mr Wilmott. Because of their love of puzzles,
quants lean towards technically brilliant rather than sensible
solutions and tend to over-engineer: “You may need a plumber but you
get a professor of fluid dynamics.”

Yet the key take home message appears at the very end of the Economist article:

One way to deal with that problem is to self-insure. JPMorgan Chase
holds $3 billion of “model-uncertainty reserves” to cover mishaps
caused by quants who have been too clever by half.
If you can make
provisions for bad loans, why not bad maths too?

A terrific rhetorical question, and one which the regulators should certainly take to heart. Because if JP Morgan is wise (or foolish) enough to insure in this manner (even though there is no guarantee that a systemic crash would be covered by an arbitrary $3 billion number) it does provide a perspective on the scale of the problem. Multiply JPM's exposure by several hundred to account for all existing quant participants, most of whom have absolutely no reserve provision, and you get a sense of scale of the problem should a systemic event appear.

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AnonymousMonetarist's picture

'The distribution of price changes in a financial market scales.
Given that event X has happened, what are the odds that Y will happen next?
With financial prices, scaling means that the odds of a massive price movement given a large one are akin to those of a large movement given a merely sizable one. Such is the confusion of scaling. It makes decisions difficult, prediction perilous, and bubbles a certainty
-Benoit Mandelbrot 

'This subtle but extremely consequential property of scalable randomness is unusually counter intuitive. It is often said that "is wise he who can see things coming." Perhaps the wise one is the one who knows he cannot see things far away. Newspapers are excellent at predicting movie and theater schedules.'
-Nassim Taleb

Pamela Anderson's picture

If you have the time, read:   "How I Caused the Credit Crunch" by Tetsuya Ishikawa.

If you don't have the time to read it, then buy the Audible version at

It was great to be able to review everything from an insider perspective. The description of the life style of the criminals that cause this was entertaining.


Orly's picture

"...only to experience a regime change based on some heretofore unexpected exogenous event which renders existing signal translation models useless..."  (Such as blatant market manipulations...)

Hey!  That's the same experience I have had!

Welcome to the club, fellas.

Not A Pundit's picture

By holding the reserves are they not validating the Volcker rule?  Hmmm... let's hold more money back in case our prop trading goes bust.

Going Down's picture

Satyajit Das


Regulators need to heed the warning of the 17th-century French author Francois de La Rochefoucauld: “We are so accustomed to disguise ourselves to others that in the end we become disguised to ourselves.”

Satyajit Das is the author of the recently released “Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives”

hedgeless_horseman's picture

Isn't this type of reserve a normal practice for this industry?  Do not LVS, WYNN, MGM, WMS, MPEL, ASCA, and the rest of this sector's major players do the same?

seventree's picture

To summarize the above in 4 letters: GIGO

RowdyRoddyPiper's picture

I bet those sneaky bankers at JPM have bought a CFD on CMC...

Anonymous's picture

In a case of a bidless market crash, what would happen to the 3x inverse ETF's?

Anonymous's picture

counterparty default

truont's picture

$3B is not enough.  The real backstop is another cash-infusion from the FED when derivatives implode.

MarketTruth's picture

JP Morgan is an owning/member of the Federal Reserve. Thus they have unlimited dollar backing.

bugs_'s picture

Where they get the $3B for their reserve against a possible loss?

deadhead's picture

Where they get the $3B for their reserve against a possible loss

Selling lots of shitty paper to the Fed for 100 cents on the dollar.

peterpeter's picture

That smells like managing earnings.

A deep pool of funds that has as logical reason to exist, but the size of which is completely arbitrary can be a wonderful piggy bank to park extra earnings or remove from on a rainy day.

And for all the quant bashing - not all quants are on the same sides of trades.  Many run strategies that not only make money consistently, but which Nassim Taleb and Paul Wilmott would both give their blessings to.

Frumundacheeze's picture

Hey Tyler,

Since there is no way for us low-lifes to contact you via email on this site (or at least not easily located), here's an article I think you should post here. Well worht reading and discussing.


First portion:

The War on Consumers and Labor Heats Up Wall Street Moves in for the Kill


Former Treasury Secretary Hank Paulson wrote an op-ed in The New York Times yesterday, February 16  outlining how to put the U.S. economy on rations. Not in those words, of course. Just the opposite: If the government hadn’t bailed out Wall Street’s bad loans, he claims, “unemployment could have exceeded the 25 per cent level of the Great Depression.” Without wealth at the top, there would be nothing to trickle down.

The reality, of course, is that bailing out casino capitalist speculators on the winning side of A.I.G.’s debt swaps and CDO derivatives didn’t save a single job. It certainly hasn’t lowered the economy’s debt overhead. But matters will soon improve, if Congress will dispel the present cloud of “uncertainty” as to whether any agency less friendly than the Federal Reserve might regulate the banks.

Paulson spelled out in step-by-step detail the strategy of “doing God’s work,” as his Goldman Sachs colleague Larry Blankfein sanctimoniously explained Adam Smith’s invisible hand. Now that pro-financial free-market doctrine is achieving the status of religion, I wonder whether this proposal violates the separation of church and state. Neoliberal economics may be a travesty of religion, but it is the closest thing to a Church that Americans have these days, replete with its Inquisition operating out of the universities of Chicago, Harvard and Columbia.

If the salvation is to give Wall Street a free hand, anathema is the proposed Consumer Financial Protection Agency intended to deter predatory behavior by mortgage lenders and credit-card issuers. The same day that  Paulson’s op-ed appeared, the Financial Times published a report explaining that “Republicans say they are unconvinced that any regulator can even define systemic risk. … the whole concept is too vague for an immediate introduction of sweeping powers. …” Republican Senator Bob Corker from Tennessee was willing to join with the Democrats “to ensure ‘there is not some new roaming regulator out there … putting companies unbeknownst to them under its regime.”


deadhead's picture

Hey Tyler,

Since there is no way for us low-lifes to contact you via email on this site (or at least not easily located), here's an article I think you should post here. Well worht reading and discussing.

tips at zerohedge dot com


deadhead's picture

Why the phuck would the irresponsible Fed and the bank owned regulators even think about requiring a reserve for quant problems?  they have already disregarded reasonable capital provisioning for basic loan losses due to the FASB 157 mark to menagerie as well as the complete and total capital requirements waiver for 1.5 yrs necessitated by the implementation of FASB 166/167.

bottom line is that banks are still woefully undercapitalized and the Fed and US regulators are playing Sgt. Shultz in Hogan's Heroes.  "I see nothing! Nothing!"


BaseLine's picture

Am I the 1st to note that making a suggestion that long-term change has occured based on 3 years worth of data is ridiculous?

Good articles:

Financial Dictatorship where Wall Street Banksters Owns Congress? read this:

Jim in MN's picture

Fuggin A, if they are fine with reserving $3B for losses what the hell is the upside potential?  $30B?  More?


Tax it 'til it don't move no more, sez I.

Anonymous's picture

Conan's Father: Fire and wind come from the sky, from the gods of the sky. But [Market] is your god, Conan, and he lives in the earth. Once, [specialists] lived in the Earth, Conan. And in the darkness of chaos, they fooled [Market], and they took from him the enigma of [binary]. [Market] was angered. And the Earth shook. Fire and wind struck down these giants, and they threw their bodies into the waters, but in their rage, the gods forgot the secret of [binary] and left it on the battlefield. We who found it are just men. Not gods. Not giants. Just men. The secret of [binary] has always carried with it a mystery. You must learn its riddle, Conan. You must learn its discipline. For no one - no one in this world can you trust. Not [tips], not [news], not [charts].
(Points to [computer])
Conan's Father: This you can trust.

* * *

Thulsa Doom: Yes! You know what it is, don't you boy? Shall I tell you? It's the least I can do. [Binary] isn't strong, boy, flesh is stronger! Look around you. There, on the rocks; a beautiful girl. Come to me, my child...
Thulsa Doom: (coaxes the girl to jump to her death)
Thulsa Doom: That is strength, boy! That is power! What is [binary] compared to the [mind] that wields it? Look at the strength in your [mind], the desire in your heart, I gave you this! Such a waste. Contemplate this on the tree of woe. Crucify him!

* * *

Original quotes:
[] Edits mine.

Anonymous's picture

"Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?"

"Garbage In, Garbage Out" - George Fuechsel

jmc8888's picture


And....we're making the same mistakes with

1. Global Warming

2. Healthcare (yes we have a death panels at every hmo, we just don't have a nationalized one run by the gov't which is what Obama's healthcare [not sane democrats version of it] healthcare - sane democrats wanted single payer without an IMBA/IMAC/IMAB/N.I.C.E.

3. Yes of course derivatives

4. Cap and Trade (new addtional derivatives)

All of these are bunk, all of these are not based on science, they're based on statistical models.  The panel of experts? Not a panel of experts but a bunch of people who treat statistics as science and with the weight of the word of god.  Except in reality, any EXPERT would realize the fact they are guesses and weight them appropriately, down towards almost nothing. 

Instead we have 'experts' working on our 'reform' that are absolutely clueless to the fact that the results from statistical models are supposed to be just a nudge helping to point the way while OTHER REAL metrics, (measurement) a venerable suite of metrics (measurements) do the legwork.

We have people who are skipping just about all the real ways to measure things, and instead relying solely on statistics, to not be 1/2 of one percent of weighting in a decision, but comprises 90-100 percent of the thought process when making a decision.  Whatever the model tells you, do....just try to forget the model is telling you what to do based off of guesses.

Plus many are funded by people who wish a particular outcome.  Hell I bet some of the formulas were written by now retired people, and those that came on, just assume everything was perfect before they arrived.  (meaning the probability for statistical modelling error is HIGHER because those in charge, didn't start it.)

Again, I'm glad ZH is addressing this issue.  Very glad.  Because personally I think that is the crux of the crux of the crux of the issue.  Yes it's derivatives, but how are they wrong? Statistical modelling.


....and it's not the ONLY place statistical modelling is screwing us up.  Succinctly what ZH and others are saying is very much true in other arenas.  The cap n trade, the IMAC/IMAB/IPAB- deathpanel for healthcare, the plain ol derivatives - just about whatever they are, and global warming.

If it doesn't work on one, people should be seeing how it doesn't work on the others.  So why are our congressman/presidents convinced that adding statistical models to control more aspects of your life is good? Because they're idiots. 

The worst is if the dems are dumb, the repubs are dumber.  But rather than place blame, we need to focus on issues, and thats removing the overreliance on statistical models to tell us things we're too lazy to figure out.  Or maybe the governance of America is no worse than possibly your 8  year old boy (if you had one) where you tell him no, but then he goes to mom to ask her.  In this case, mom, is the statistical model - guesses which if they don't meet your need, change the formula!

Great article ZH and TD


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