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Lisa Pollack, FT, Belly of the Whale Series, here. More worry about OTC marks in the spread. This is not likely to be just something unique to the Whale’s situation. Could be the basis for moving broker dealers to automated trading, low latency or otherwise. Kind of reduces the number of traders; takes people out of the loop for trade execution and possibly marking to market. You could get fewer situations where heads of trading desks, under oath to the US Senate, deny knowing where their OTC books are marked for months at a time. According to Boorstin, cash registers were important in dealing with these sorts of trade problems at the turn of the 20th Century in the US. Boaz Weinstein suggests the boss has to sign off on large trades in the future – just make the boss sign off on the daily MTM marks and pull the margin deltas for any counterparty collateral disputes from the boss’s historical clawback compensation pool. Everyone will straighten up and fly right pretty quickly if that were the case.
By now, it should be well understood that the credit derivatives book in JPMorgan’s chief investment office was woefully mismarked. Worryingly, the practice of marking at the extremes of the bid-offer, giving the most favourable result, was rubber-stamped as acceptable practice by both the bank’s controller and the outside auditor.
The restatement of first quarter earnings in July 2012 only happened as a result of investigators from JPMorgan’s special Task Force discovering that the traders hadn’t supplied the marks “in good faith”. We’re not sure what place “faith” has in decent account practice. The whole Street seemingly disagreed with the marks, as demonstrated by large collateral disputes. In any case, let us examine this lack of good faith by reviewing some of the things the traders said about their marks.
Lisa Pollack, Alphaville, Footnote 74: FACEPALM, here; and A tempest in a spreadsheet, here. Funny, but getting lost in the weeds. This is important because Pollack is one of the dozen or so folks who could end up writing the London Whale book that’ll get cited for decades. The 130+ pages in the JPM report dance around a lot, recounting a sequence of events without simply stating what obviously happened.
The cash register that JPM built for tracking the running value of the securities owned by the London Whale broke, probably in March or April 2012, and it could not be fixed before losing several billion dollars. Curiously , the “cash register” in this case is less euphemistic than you might have expected. The VaR, the risk managers, most of the people not directly on the CIO trading desk weave in an out of the official narrative but they are mostly irrelevant to what originally happened. They are passengers in a sad story. It really looks like the problem was either the code that read the market data to compute the inputs to the P&L calculator (the spreadsheet) or the P&L calculator itself (the supercomputer). The report doesn’t really carefully dissect this issue, not sure why. If the problem was A. the spreadsheet model for calibrating the correlations and the hazard rates for inputs, I bet the CIO desk and quants are/were more than smart and motivated enough to fix it or patch the underlying spreadsheet and analytics packages before losing much money. The CIO folks all probably remembered, all too vividly, how correlations behaved with the GM and Ford junk downgrades in May 2005 and designed their new correlation cooker to do something “better.” If the problem was B. programming the new “supercomputer,” I could see them not having enough time to fix the situation. B … final answer.
The report says there is “some evidence” that pressure was put on the reviewers to get on with approving the model in January because of the risk limit breaches being incurred with the old model around then. For example, as quoted above: “In an e-mail to Mr. Hogan on January 25, Mr. Goldman reported that the new model would be implemented by January 31 “at the latest” and that it would result in a “significant reduction” in the VaR.”
Hence the Model Review Group “may have been more willing to overlook the operational flaws apparent during the approval process.”
Back to the modeler though. He used to work at Numerix (a vendor), where a repricing model had been “developed under his supervision” that JPMorgan normally used in VaR calculations. The Numerix analytic suite had been approved by the Model Review Group. But the modeler, when developing the new VaR model, developed his own suite — called “West End”. This suite was not reviewed in advance of the new VaR model being rolled out, but rather only had a limited amount of backtesting completed on it.
David Murphy, Alphaville, The JPMorgan Whale’s regulatory motive, here. Just a wild guess for a movie plot – Whale takes leveraged position in CDX tranches that are no longer heavily traded like back in the day, say 2005. In May12 the big, and now publicly exposed, hedge is in the CDX series 9 where the Whale gets picked off by the hunch/pounce/kill boys. The new correlation/hazard rate cooker (the code that computes the inputs to the gaussian copula model) has a problem, maybe with Kodak maybe something else, and the Whale’s desk risk and the 238 second near real-time run time Credit P&L is shot – they are flying totally blind. They try to buy time marking the spreads to cover the model’s flaked out P&L and Risk while they fix it. The risk and regulatory requirements change while all this is going on so there is some JPM Risk executive who now wants someone to explain to him what this all means to his VaR model. Nobody has time to talk to him cause the VaR is just for mouth-breathers and there is a real problem here that folks need to think through. I wonder if it was helpful to debug the flaky model in the FPGA supercomputer once the CIO P&L went out, probably not, right? Bruno, Achilles, and probably even Ina got to read up on Verilog programming back in April 2012, cool thx prize winning Dataflow supercomputer implementation of the gaussian copula … Maserati, Bellagio, Bellagio, Kasparov.
If JPMorgan had just bought, say, senior tranche protection on a credit index, then while the bank’s position would indeed have been crash-hedged, it would have generated significant earnings volatility as the bonds would not have been marked-to-market but the derivatives would have been. In particular, in a tightening credit environment, such as we had earlier in the year as the ECB injected liquidity into the banking system, the derivatives would have lost money without a corresponding accounting gain on the bonds.
One way around this accounting mismatch is to restructure the derivatives position. The idea is still to be long crash protection — again, by buying protection on senior tranches, for example — but to offset this by also selling protection on the index. If done correctly this position will be indifferent to small moves in credit spreads (‘delta neutral’), but it will make money if there is a big increase in spreads.
This removes the fair value volatility from the position at the cost of introducing correlation risk: the amount of index you need to sell is a function of the correlation between the names in the index, so you have to readjust your hedge as the market price of correlation changes.
Deus Ex Macchiato, Whale Watching , the official tour, here. I think this is David Murphy again. Nice website, I should read it more frequently.
The firm’s main problem at this point was that two goals were in conflict. On one hand their position was so large (if unnoticed by regulators) that they would get crushed if they tried to leave too fast: on other other, they needed to leave to reduce capital. The solution, of course, was to try to change how capital was calculated.
the concern that an unwind of positions to reduce RWA would be in tension with “defending” the position. The executive therefore informed the trader (among other things) that CIO would have to “win on the methodology” in order to reduce RWA.
Chris Wilson, Yahoo, What would your signature look like if Jack Lew wrote it? (Interactive), here.
Now, Yahoo News exclusively brings you the Jack Lew Signature Generator. Just type in your name, hit the button, and see what your name would look like in his, er, signature style.
Matt Levine, DealBreaker, Deutsche Bank Had A Profitable Interest Rate Trading Business In 2008, here.
Again, though, I come at it the opposite way: if your business is based on manipulating rates, why are you running a matched book? There’s an intuitive plausibility to the Journal‘s basic tale of (1) bank put on big bets, (2) risk managers fretted, (3) they were reassured by traders saying “well we’ll just manipulate Libor so this bet pays off for us.” But if that was really the thinking, why not do it all the time? Why go through the effort of laying off 98% of your interest rate risk, building a mostly balanced book of long and short swaps, instead of just leaning really hard into bets on Libor going up, say, and then working the phones hard to push it up?
Frank Partnoy and Jesse Eisinger, The Atlantic, What’s Inside America’s Banks? here.
Some four years after the 2008 financial crisis, public trust in banks is as low as ever. Sophisticated investors describe big banks as “black boxes” that may still be concealing enormous risks—the sort that could again take down the economy. A close investigation of a supposedly conservative bank’s financial records uncovers the reason for these fears—and points the way toward urgent reforms.
Matt Levine, DealBreaker, Turns Out Wells Fargo Doesn’t Just Keep Your Deposits In A Stagecoach Full Of Gold Ingots, here.
There are lots of reasons for opacity in bank financial statements but surely a lot of them have to do with market making. For one thing: derivatives, a major villain in the Atlantic piece. Basically, OTC derivatives market-making doesn’t net as cleanly as does, like, buying and selling publicly traded shares of stock. In cash equities, you buy 100 shares of IBM from one customer and sell 100 shares to another customer and clip two cents and are left with zero shares. In derivatives, you buy $100mm of 7-year Libor swaps from one customer and sell $100mm of 6-year Libor swaps to another customer and sell $10mm of 8-year Libor swaps to a third; you’re left “flat” – i.e. zeroish DV01 – but have $210mm of derivatives notional for six-plus years. If you were running a directional investing business with Wells Fargo’s balance sheet – $1.4-ish trillion – and ended up with $2.8 trillion in derivatives notional you’d be … aggressive; if you were running a matched book then $2.8 or $28 or $280 trillion are all at least theoretically possible and one is not necessarily riskier than another.
Manmohan Singh and James Aitken, IMF, July 2010, The (sizable) Role of Rehypothecation in the Shadow Banking System, here. The paper Gillian Tett made famous.
The United Kingdom provides a platform for higher leveraging stemming from the use (and re-use) of customer collateral. Furthermore, there are no policy initiatives to remove or reduce the asymmetry between United Kingdom and the United States on the use of customer collateral. We show that such U.K. funding to large U.S. banks is sizable and augments the
measure of the shadow banking system. Supervisors of U.S. banks that report on a global consolidated basis need to enhance their understanding of the collateral funding that the U.S. banks receive in the United Kingdom.
Rehypothecation occurs when the collateral posted by a prime brokerage client (e.g., hedge fund) to its prime broker is used as collateral also by the prime broker for its own purposes.
Every Customer Account Agreement or Prime Brokerage Agreement with a prime brokerage client will include blanket consent to this practice unless stated otherwise. In general, hedge funds pay less for the services of the prime broker if their collateral is allowed to be rehypothecated.
Acuity Derivatives LLC, Why P&L Attribution? Or Judging Weathermen…, here. Interesting site. I don’t see many treatments of P&L attribution written down and presented like this. Let’s see how it reads.
Profit & Loss Attribution (PLA) in a bank provides a critical product control function of decomposing and analyzing actual booked Profit & Loss (P&L) and its variance, especially in the context of testing three hypotheses posed by the bank’s risk models:
I. Change in the mark-to-market value of its positions are materially determined by changes to a specified set of variables and parameters (i.e. risk factors) and the expected change is quantified by the sensitivities obtained to these risk factors from its models;
II. There is a specified % probability that the value of its positions will lose more than its VAR number over any given interval equal to the VAR holding period;
III. The cost of insuring its aggregate positions against the risk of counterpartyZ defaulting is not expected to exceed the cumulative sum of the CVA fees charged to its trading desks for originating exposure to counterparty Z.
Zerohedge, This Is What Happens When A Mega Bank Is Caught Red-Handed, here. Durden is really good at this.
Which brings up three important questions:
- Now that the “trading desk” that was responsible for up to 25% of JPM’s net income has been effectively closed, how will Jamie Dimon succeed in creating recurring profits in line with historical average and future expectations?
- What will happen to the other “VaRs” once they too are exposed, either after the loss is uncovered, or when regulators actually dare to do their job for once and truly dig through the banks’ books?
- Which other bank has a huge and heretofore undisclosed multi-billion derivative “easter egg” on its books?
For Question 3 we may have a suggestion.
Business Insider, And Now JP Morgan’s $2 Billion Trading Loss Is Already $3 Billion (And Counting), here. Ok so the reported losses are growing.
Jamie Dimon said it could get worse… and it is.
The JP Morgan trading loss that was $2 billion four days ago is now $3 billion, report Nelson Schwartz and Jessica Silver-Greenberg in the New York Times.
Because every hedge fund in the world knows JP Morgan is stuck in a position so big that it can’t unwind it… and they’re betting against it.
Zerohedge, Jamie Dimon “Invited” To Testify Before Senate, here. And there will be compelling TV on its way.
Update: JPMORGAN SAYS DIMON TO AGREE TO TESTIFY TO SENATE. Ummmm, there was an option?
As everyone (or at least Zero Hedge) long expected, JPM’s prop trading debacle just got political and senators are about to demonstrate to the world just how little they understand about modern IG9-tranche pair trades. Expect to hear much more about JPM’s “shitty” prop deal.
Zerohedge, So How Are JPM’s Prop “Counterparties” Faring? here. But Bluecrest and Blue Mountain are not reporting like they are Party B. There are reports that Boaz Weinstein/Saba is Party B but no actual P&L numbers being whispered. Odd?
Now one thing we know is that when it comes to reporting one’s results to an aggregator: when you have a profit you never under-represent it. And in this special case, since the funds are likely eager to recruit more like-minded hedge funds to their side of the trade, the best way to do it is by showing profits.
Which, for the early part of May, when the bulk of the JPM losses took place, are oddly missing for the two biggest players across from JPM…
So: where are the profits really going?
Lisa Pollack, Alphaville, Recap and tranche primer, here; and The high yield tranche piece, here. Pollack going to get the book deal for the London Whale, clearly. Once she nails down the positions maybe we will get to the Gaussian Copula substory. Chances seem to be improving that this story is about a bunch of smart guys who tried to resurrect a dead quant model. Maybe it would be better to recast the story as a Zombie Quant model or go with the J Depp/Dark Shadows/Vampire Quant model. But even though this story has massive potential to connect with loads of people, Pollack is not locating Party B P&L nor has anyone else. It’s a problem if you cannot get that puzzle piece. Plus there is way more premeditation here than just Keystone Cops stuff started to happen on 6 Apr. Maybe there is some offshore vehicle hole getting filled up whose reference entity name cannot be spoken? That would change the story’s complexion, right? Maybe the London Whale is a red herring?
Coverage of the
$2bn$3bn loss emanating from JPMorgan’s Chief Investment Office on its synthetic credit portfolio continues a pace, and FT Alphaville’s tour continues too.
The desire to understand what the trade was and the rationale behind it continues to bug us and many others. Interestingly, some of the discussion of late has come full circle. Bloomberg kicked off the London Whale saga on April 6th, and their follow-up on April 9th contained a detail that has now come back into the narrative. This time, though, it’s more than a mere sidenote — more on this in a minute.
While these more recent explanations are satisfying, we’re still scratching our heads a bit.
The challenge remains: to find trades that have managed to deteriorate with the speed that CEO Jamie Dimon has claimed they have — small in the first quarter, $2bn “all in the second quarter”, and “it kind of grew as the quarter went on”.
Now, credit tranches, which are leveraged positions on credit indices that themselves already involve a lot of leverage, could do this if the model used to determine hedge ratios wasn’t up to the task or if the trades were just outright foolish.
Al Pacino, Any Given Sunday, here; or Clint Eastwood, here. Now we are going to have to endure a seemingly endless stream of Taleb’s gloating i-have-been-warning-you-about-VaR-for-years interviews; the FinQuant equivalent of the Icky Shuffle. Look Team Firm Risk, you all have seen It’s a Wonderful Life, right? Well every time a bank gets their bell rung, Taleb gets another Fox and Friends interview.
Naked Capitalism, JP Morgan Loss Bomb Confirms That It’s Time to Kill VaR, here.
One of the amusing bits of the hastily arranged JP Morgan conference call on its $2 billion and growing “hedge” losses and related first quarter earning release was the way the heretofore loud and proud bank was revealed to have feet of clay on the risk management front. Jamie Dimon said that the bank had determined that its value at risk model was “inadequate” and it would be using an older model. And no wonder. The Financial Times report contained this bombshell:
JPMorgan also restated its “value at risk”, a measure of maximum possible daily losses, of the CIO [the unit that executed the trading strategy that blew up] in the first quarter from $67m to $129m.
“Synthetic credit portfolio”. That’s the book where the $2bn in mark-to-market losses took place for JP Morgan, according to an announcement made on Thursday. A result which has now cost them a their AA- rating from Fitch and landed them on negative outlook with S&P, as announced late on Friday.
FT Alphaville has analysed the credit trades that might be in that portfolio, in an attempt to reason through what may have gone on. The fact, however, remains that we know precious little. Why is that? Is this acceptable that after the financial crisis that this can happen to a bank, let alone a systemically important one like JP Morgan?
Got a buck that says you cannot find a Firm Risk person on 13 May 2012 who knows substantially more about the positions than Lisa Pollack.
Zerohedge, Double or Nothing: How Wall Street is Destroying Itself, here.
This fragile business model is in fact descended from the Martingale roulette betting system. Martingale is the perfect example of the failure of theory, because in theory, Martingale is a system of guaranteed profit, which I think is probably what makes these kinds of practices so attractive to the arbitrageurs of Wall Street (and of course Wall Street often selects for this by recruiting and promoting the most wild-eyed and risk-hungry). Martingale works by betting, and then doubling your bet until you win. This — in theory, and given enough capital — delivers a profit of your initial stake every time. Historically, the problem has been that bettors run out of capital eventually, simply because they don’t have an infinite stock (of course, thanks to Ben Bernanke, that is no longer a problem). The key feature of this system— and the attribute which many institutions have copied — is that it delivers frequent small-to-moderate profits, and occasional huge losses (when the bettor runs out of money).