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Business Insider, NASSIM TALEB EXPLODES: JP Morgan’s Ina Drew Was Paid More Money Than Mob Boss John Gotti For Taking Risks With Our Money, here. Taleb’s anger followed by the Donna Summer tribute seems like a setup for the Gregory Brothers to autotune.
Zerohedge, Nassim Taleb Is Angry That Not Even John Gotti Got Paid As Much As JPM’s Ina Drew, here.
Team Firm Risk as we discussed before, in Team Firm Risk: 0 Taleb: 2Bn (halftime), this is on you.
NYT, The Facebook Offering: How It Compares, here. Killer IPO chart from 1980 to Facebook. Log valuation and first day pop look the best.
Blodget, Great Job, Wall Street and Facebook! The IPO Was Perfectly Priced, here.
It was a huge relief this morning when Facebook stock opened a modest 10% above the IPO price.
This price level was ideal for almost everyone involved–with the exception of short-term traders who bought the stock only to instantly flip it. (And no one should cry for them).
With such a modest pop, Facebook and its selling shareholders did not leave tens or hundreds of millions (or even billions) of dollars on the table–an expensive mistake that most companies make.
Salmon, Much ado about nothing, here. Salmon is great always will be; Felix TV … not feeling it so much.
This of course helps to point up just how silly all the Facebook IPO hype really was. Yes, Facebook is now a public company, but it’s still controlled by Mark Zuckerberg, and the IPO itself was a bit of a farce: delayed at the open, artificially supported by the underwriters at the close, and mainly serving to demonstrate that a brand-new company, which no one knows how to value, trading at a stratospheric valuation, can still somehow end up trading within an incredibly narrow range on enormous daily volume.
For that, you can probably thank the surprisingly old-fashioned book-building process, where a team of investment bankers took Facebook on a classic roadshow, complete with a slick and rather embarrassing video, all for a record-low fee of 1.1% of the proceeds. Still, never mind the low fee: the bankers were paid to do a job, and they did it, providing a rock-solid bid at exactly $38 per share and thereby sending a clear signal to any potential future client: we’re never going to let investors lose money on the first day. Frankly, there are worse ways of spending money to try to bolster your reputation.
Damodaran, Musings on Markets, Facebook and “Field of Dreams”: Hoodies, Hubris and Hoopla, here.
Bottom line: Facebook, in spite of its ubiquitous presence in our lives, is just one company and not a very big one (at least in terms of revenues and earnings) yet. The market will obsess about it tomorrow but it will move on very quickly to the next worry, fear or fad.
Roger McNamee, Roger and Mike’s Hypernet Blog, here.
In December 2010 I decided to open source my investment strategy, in the form of a slide show and presentation called Ten Hypotheses for Tech Investing. When you open source ideas, you expose them to improvement. I presented the Ten Hypotheses to many smart people, including executives at Google, Facebook, Twitter, Yelp, the New York Times, Wall Street Journal, NBC, and many others … and they shredded them. It was fantastic!!!
From that process emerged ideas like the Hypernet. While I characterize the Hypernet as a hypothesis, it already exists. We use it every day. In reality, the first four of the Ten Hypotheses are new interpretations of the present, rather than predictions about the future. This blog post will focus on those four hypotheses.
Which is better: McNamee@TED2011, here or Weyland@TED2023, here. Octagon cage match? Oh, I don’t know, I guess I’ll take McNamee after I finish selling some 10Y US Treasuries in the 401K account so I can get some Facebook shares on Monday.
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.
Why?
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.
Lisa Pollack, Alphaville, Two billion dollar ‘hedge’, here. Looks like Pollack and Zerohedge conclude the London Whale position is in CDX tranches. One other thing if I could, JPM spent 3 years getting it’s 8 hour overnight Gaussian Copula batch into an award winning FPGA supercomputer (see JP Morgan’s London Whale needs Maxeler’s FPGA Supercomputer to run Risk?) that runs in 238 seconds; is that right, Sir? And then there’s this, Recipe for Disaster: The Formula That Killed Wall Street. Now, I’m not Lt. Columbo or anything but help me out here, wouldn’t some people call that means, motive, and … opportunity.
Concerning how one can make a $2bn loss on this, we have become convinced that it’d only be possible if the above was also done with tranches, which would seriously lever up any such position. Several FT Alphaville commenters have alluded to this already — thank you, guys. Even then, a $2bn loss is a lot to chalk up. But if it isn’t that, what else could it be?
Zerohedge, Is The Pain Over For Bruno Iksil? here.
Today, for the first time since the advent of the JPM prop trading fiasco last Thursday, the IG9-10 Year skew has diverged, dipping from -3 bps to -5 bps as the index remained flattish while the intrinsics widened by about 2 bps. While hardly earthshattering, this move likely means that either JPM’s CIO trading desk is playing possum and is no longer unwinding its original pair trade exposure (either because it no longer has anything to unwind, or because it can’t take the pain any more and is out of the market entirely), or the hedge fund consortium has had enough of pushing IG 9 wider in hopes that max pain would force JPM to cover its synthetic leg. As a reminder, this is where last Thursday we said the time to push JPM would likely end. As for the question of how much additional P&L loss JPM has sustained from Friday through today is a different matter entirely, and we are confident the next announcement from JPM will come momentarily, coupled with the announcement that Bruno Iksil, the last remnant of the CIO desk, and now having completed his duty of unwinding the trade that brought so much pain for Jamie Dimon, has been retired.
Business Insider, REPORT: Traders Are Saying This Legendary Deutsche Bank Veteran Harpooned JP Morgan’s ‘London Whale’, here.
But who was on the other side of that trade?
According to a new report from the The New York Post’s Michelle Celarier, the man on the other side was Boaz Weinstein, head of Saba Capital Management and former proprietary trader at Deutsche Bank.
Naked Capitalism, Satyajit Das, Topiary Lessons – JP Morgan’s US $2 Billion Loss, here.
Having benefitted from risk management failures of others such as investment bank Bear Stearns and hedge fund Amaranth, JP Morgan (“JPM”) appears to have made an “egregious” and “self inflicted” hedging error. The bank would have done well to reflect on John Donne’s meditation: “send not to know for whom the bell tolls it tolls for thee”.
NYT, At JPMorgan Chase, a Complex Strategy That Backfired, (via DealBreaker) here. Nice diagram illustrating how opening a umbrella over your umbrella can become a bomb.
WSJ, Hedge Funds Profit as J.P. Morgan Sees Losses, here.
Firms such as BlueMountain Capital Management LLC and BlueCrest Capital Management LP each scored gains of about $30 million, according to people familiar with the matter.
Tchir, What the TF? The Coolest Trade I Ever Saw, here; TFMkts Analysis: the JPM Conference Call – A Closer Look, here.
Here is the “transcript” that everyone is talking about, but it would seem only listened to with selective hearing and have read without reading. This is my analysis of what was said and what it could mean.
So he comes clean about the loss. It is $800 million after taxes. Just over $2 billion on the trade, offset by about $1 billion in gains from an Available for Sale (AFS) account run by the CIO’s office.
DealBreaker, Matt Levine, You Say “Voldemort” Like That’s A Bad Thing, here. Levine plays who’s the narc:
What is going on here? Like, for one thing: who narc’ed on him? And why? The most sensible account as always comes from Lisa Pollack; her take is basically that (1) a bunch of hedge funds are betting that the skew between spreads on the individual names in the CDX.IG.NA.9 (which names they are long) and spreads on the actual index (which they are short) will converge, (2) Iksil recently got massively long the index, blowing out that skew and losing them money on a mark-to-market basis, and (3) the hedge funds are mad and sad and going to the press to embarrass and/or regulate JPMorgan out of this market? This seems fine except that except it’s hard to see the hedge funds making money on an actual skew trade; Markit shows a -12bps skew and my sense is that after bid/ask you just can’t make a living on 12bps of convergence.
There’s a part of me that wants the narc to be JPMorgan itself, calling attention to its brilliant risk management, spooky nicknames, and ability to move markets with one flick of a London-based Frenchman. Also perhaps to provide a platform for its anti-regulatory case.
Levine seems to get a handle on the moment and the Lisa Pollack reference seems valuable long term.
But realistically, the press has been bad, with Bloomberg going so far as to say “Neither Iksil nor JPMorgan have been accused of wrongdoing,” which, ouch! So maybe it’s other banks, jealous of how good JPMorgan’s hedging is, calling attention to the Very Important Issue of how un-Volckery and maybe-market-manipulative it is?
If so I feel like they’re … doing kind of a weak job? I will be surprised if anyone gets worked up about the market manipulation angle given that (1) the losers are eeeeevil hedge funds and (2) it’s having a fairly small effect on the market for one off-the-run CDX index. And for the Volcker Rule angle … I am serenely untroubled by JPMorgan risking $100bn on US investment grade credit, and everyone else is similarly untroubled given that there’s no real evidence that this trade (arguably a hedge, arguably long-term, etc.) would actually violate the Volcker Rule. Regardless of how you get there, though, if your model of bank regulation prohibits JPMorgan from risking $100bn on diversifid US investment grade credit, your model is wrong.
If I were writing the anti-JPMorgan PR campaign here I might come at it differently. If you take these reports at face value – and you can’t entirely; I don’t believe that JPM is long all this credit risk unhedged and neither does anyone who talked to the Journal or Bloomberg – then JPMorgan has invested $100bn of its huuuuge but finite balance sheet in US corporate credit via this trade. Unlike its loans, this extension of credit is unfunded, but still – JPMorgan is not exactly short of cash, as they’d be the first to tell you, and they can always get more if they need it. So it’s reasonable to think that JPMorgan’s ability to extend credit is finite and that is due to capital, not funding. But that $100bn of credit risk has been extended not to the 121 actual businesses in the CDX.IG.NA.9 index, many of whom probably also have too much cash but some of whom could presumably use the money to like Build A Factory or Hire Some Workers or Buy An Instagram or whatever. Instead it’s being extended to … well, indirectly, to eeeeevil hedge funds who are short the credits and churlish enough to complain about it to the press. If you’re a regulator or politician whose complaint about banks is that they aren’t doing enough lending to support the real economy, news that 5% of JPMorgan’s balance sheet is in the form of synthetic corporate lending that doesn’t actually go to those corporates might be enough to get you mad.
Trading environment seems a little more toxic than usual. Levine gives it all a gritty early 70s Popeye Doyle, French Connection feel.
Ft.com/alphaville, Lisa Pollack, Hedge funds and the Whale, credit index edition, here. Lisa Pollack is publishing this reasonably early, 6 Apr. Look at the charts in “A graphical investigation” toward the end of the piece to get some sense of how the IG9 market has moved and on what volumes.
Zerohedge, Behind ‘The Iksil Trade’ – IG9 Tranches Explained, here.
So what was once a 3%-7% tranche is now roughly a 2.4% – 6.4% tranche.
So if you sell protection on this tranche, you need further cumulative defaults of 2.4% before you make any payments, and then you make payments until 6.4% of the notional has had losses. If there is a 0% recovery on each default, you could have 3 defaults before having to make any payment (each name is 1/125 or 0.8%). If recovery was 40% then you have no payments until the 6th default.
The big question is, what do you get paid on this tranche? 20 points up-front and 500 bps running. So if you sell $1 billion of this tranche, you receive $200 million up front and $50 million per annum. In a relatively tight credit spread environment, this is a lot of money. If you use the upfront payment to “defease” losses, the $1 billion of exposure has a maximum loss of $800 million, and would require 4 defaults at 0% recovery before actually having a loss, and more realistically, would only take a loss on the 8th default with a 40% recovery. Suddenly the trade seems less scary, as least to me.
But how do people come up with a number of a “100 billion”? That comes down to “deltas”. The delta on this tranche is about 7.5 times. So if someone wanted to take this risk, without delta (just sell the tranche and not have a “correlation” bet), every $1 billion would create $7.5 billion of index trading.
You could sell this “no delta” and the buyer would pay you for the tranche, but then have to go and sell 7.5 times that amount of index out to the market so they could manage their “correlation” risk – a giant model based book. Some dealers are very good at tranches, but are weak at trading the underlying index. In those cases, you might sell the tranche “with delta” and sell the index position yourself because you can get better execution that way. So you sell the tranche and buy 7.5 times the index from the correlation desk (the with delta trade). Then you sell the straight index into the market. It would explain why you are seen as a seller of index when the real trade is actually being a seller of the tranche.
Morgan Stanley, 2012 Handbook of Credit Derivatives and Structured Credit Strategies, here. 250+ page doc on credit derivatives via Levine at DealBreaker. I’ll take a look at it today.
Ft.com/alphaville, Lisa Pollack, The mystery of Morgan Stanley’s footnote unravels Part 1, here. Part 2, here. MS reduced exposure to Italy by $3.4bn while benefiting from a positive hit to net revenue of $600m. How did that work? Alternative Termination Event clauses – just like they teach in the CVA courses.
Bloomberg, JPMorgan Trader Iksil Fuels Prop-Trading Debate With Bets, here. London Whale needs some P from Series 9 Investment Grade CDX. Can Bloomberg and some prominent officials help fix the situation?
Zerohedge, From Bruno Iksil’s Personal Profile: Enjoys “Walking Over Water” And Being “Humble”, here. Oh, so the London Whale is shorting tranches of series 9 CDX in $100bn quantities.That starts to make sense given all the hysteria. So they make the purchases at the corporate-level to hedge SCDO exposure that is not actively traded by the desk. Maybe Bruno is the one who needs the the Maxeler FPGA Supercomputer Credit batch at JPM (see Credit Derivatives, Flynn’s Architectural Case for Maxeler in 2012?, and Street FP Due Diligence 3: Epic Yet?) to run in 238 seconds. How do you tag that? London Whale needs Mammoth Supercomputer to Stay Afloat? Too much, right? I still suspect that the entire JPM credit batch (as described) completes in less than a minute on a low-end Mac Pro even with the Gaussian Copula positions. Sort of more like “Bruno uses iPhone to Track Purchases.” (see Business Insider, Financial Post, Wall Street Journal blog, Sober Look, New York Times Dealbook, Financial Times Alphaville, blogrunner)
Zerohedge, 31 Dec 2011 Notional Amt. of Derivative Contracts Top 25 Comm. Banks, here. Didn’t MS carry derivative inventory in the past?
ISDA, here. Settlement Recovery rate auction 19 Mar.
Salmon, Greece’s CDS: more lucky than smart, here. Invocation of the CAC (Collective Action Clause) did it. US CDS docs don’t have restructuring so it would not work the same US side.
Zerohedge, ISDA CDS Trigger Decision is Unanimous, here. Settlement auctions primer, here. Short Greek Bonds vs Long Apple: No Contest, here.
Naked Capitalism: Gillian Tett likes Data and Models, here.
Ritholtz, The Big Picture, How to Create Financial Content, here. Turning the snark up to 11.
Question:
Over/Under: 12 seconds of floating point computation; P&L and risk for several 100K default swaps; off the shelf servers in 2012 – running standard ISDA CDS model code; 5 to 6 decimal place tie out?
Even for non-SNAC positions I’m thinking under, perhaps epically under depending on the hardware budget. Ideally you run code to determine this and we do not have the optimized code in hand, but we can write it. Before writing the code you want to run the due diligence to see if writing the code is worth it. Let’s see.
History:
We recall from previous 2009 estimates ( see Totally Serial) for unoptimized scalar code cooking of par and perturbed curves certainly costs less 7 milliseconds and valuation of a default swap’s contingent and fee legs take about 40 microseconds . This is of course assuming that the default swaps have custom cashflows and are not SNAC default swaps (standard instruments with IMM date cashflow schedules) in which case the default swap valuation execution time massively collapses and the credit curve cooking time can be significantly reduced as well.
Methodology:
Let’s assume 100K OTC (non SNAC default swaps) and 10K credit curves and we can scale to fit the actual curve and deal counts with more accurate estimates. Lets further assume that the 10K credit curves are independent (there is no known geometric or arithmetic structure to the traders marks). So, you actually have to cook each of the 10K credit curves. For unoptimized code then we expect about 7 ms*10K + 0.04ms * 100K or ~80 seconds floating point computation elapsed time on a single contemporary microprocessor core. Naively, you would guess this benchmark optimization would be looking for about 20-30 seconds (performance improvement 3x-4x over unoptimized) on a single x86 core. So assuming we buy a $3000 6-8 core microprocessor in 2012 the 100K/10K batch optimized fp code executes in a few seconds, expected case. Variance in the estimate will depend on some assumptions like the average number of iterations for convergence with Brent, the average term of the default swaps, the number of perturbed credit curves needed for full risk, the scaling across multiple on-chip cores among other things.
Amdahl’s Law directs the due diligence to first focus on the curve cooking so that’s what we will do in addition to rechecking the previous estimates ( I think they were very conservative) in the outlined argument. Lets count the operations in the ISDA code to nail this estimate down. Then we will estimate what the L1 and L2 cache efficiency we can plausibly expect to get a target due diligence number. What do we look at in the ISDA src distribution? Bunch of src files we don’t care about (DK) from the perspective of getting an estimate of the floating point computation time. The other annotated file names (in bold) contain source we may need to study.
- badday.c DK
- bsearch.c DK
- buscache.c DK
- busday.c DK
- cashflow.c DK
- cds.c computes contingent leg, fee leg, vanilla CDS PV
- cdsbootstrap.c Brent root finder for credit curve cooking
- cdsone.c converts upfront to flat spread
- cerror.c DK
- cfileio.c DK
- cfinanci.cpp DK
- cmemory.c DK
- contingentleg.c computes contingent leg and one period integral
- convert.c DK
- cx.c DK
- cxbsearch.c DK
- cxdatelist.c DK
- cxzerocurve.c calc zero price for given start and maturity
- date_sup.c DK
- dateadj.c DK
- dateconv.c DK
- datelist.c DK
- dtlist.c DK
- feeleg.c calc PV of single fee, fee leg, accrued
- fltrate.c DK
- gtozc.c looks like curve cooking
- interpc.c interpolation of rates
- ldate.c DK
- linterpc.c curve interpolation
- lintrpl.c DK
- lprintf.c DK
- lscanf.c DK
- rtbrent.c root finding for curve cooking
- schedule.c possibly DK – cash flow schedule generation
- streamcf.c calculate cashflows
- strutil.c DK
- stub.c stub payments
- tcurve.c discounting
- timeline.c not sure maybe DK
- version.c DK
- yearfrac.c DK
- zcall.c zero curve
- zcswap.c add strip of swap to zcurve
- zcswdate.c DK
- zcswutil.c cashflow lists for swap instruments
- zerocurve.c build zero curve from mm and swaps
- zr2coup.c calculates the par swap rate
- zr2fwd.c calculates zero coupon forward rate
We should be able to pound this due diligence through with a little effort, let’s see how it goes. Maybe I am missing something? It’ll take a couple postings I expect, but it will be interesting.
Milken in WSJ: Why Capital Structure Matters
Salmon: Do CDSs cause more bankruptcies?
Bookstaber: Faster isn’t always better. Funny argument against a strawman no one actually believes in. Think of the quest for higher performance like washing. While everyone agrees that compulsive washing, ala Lady Macbeth, is of limited general benefit, the observation hardly merits use of the electrons needed to blog it i.e. “dude, did you hear, washing is not always better”. Its sort of too Billy Madison to be offered without some obvious ironic distance. The operative issue is how to identify code that should be kept competitively fast as well as those programs that figuratively haven’t bathed in an extraordinarily long time. I suppose you could also throw in code that is odiferous/stinky from inception or by design. Perhaps APL interpreted code is concise and gets along well with some of the rocket scientists but it hasn’t seen a bar of soap since Alan Perlis roamed the lecture halls in New Haven. Today it is not unheard of to find executives getting used to the nutty fragrance of essentially free-of-charge high performance Java code running in their production infrastructure. I have come to expect better insights from Bookstaber – I’m looking for a blog post clarification of his position – “In the age of Moore’s Law, slower is harmful, almost everywhere”.
Alavian et.al., 2008 Counterparty Valaution Adjustment.
Eric Rosenfeld LTCM: talk at MIT. Head of Fixed Income Trading at Salomon back in the day.
Accounting: Satam Issue confronting PwC.
Salmon’s Bond Dealer talk about Bistro
