Felix Salmon

CMA is out with its quarterly Global Sovereign Debt Credit Risk Report, which includes this league table:

CPD stands for cumulative probability of default, which means that according to the market, Argentina has an 84.5% chance of defaulting at some point in the next five years. Calculating these probabilities is more art than science: Thomson Reuters puts the 5-year default probability at 71%, but both TR and S&P agree that the one-year default probability is about 50%.

How can that be, in a world where it seems all but certain that Argentina is going to default this year?

Well, for one thing, life is never as sure as bloggers make it out to be. But also, all the standard default probabilities assume that if Argentina defaults, bondholders will get back only 25 cents on the dollar. Which is improbably low. Argentina has both the ability and the willingness to pay its debts; it just doesn’t want to pay holdouts, and is likely to be forced into technical default as a result. This is a long way from the kind of outright debt repudiation that we’ve recently seen in countries like Ecuador, and it’s fair to assume that if and when it defaults, Argentina will try its hardest to ensure that its bondholders (holdouts excepted, of course) get repaid in full on everything they’re owed.

So let’s look at the 1-year CDS, which is currently trading at about 38 points up front. That means that if you want to insure $100 of Argentine debt, you need to pay $38 to do so. On top of that, if Argentina does default, you’re going to need to deliver a bond in order to get your $100 back. The way that default probabilities are calculated, they assume that defaulted bonds are going to cost about $25 each. So if you buy protection for $38, and then spend another $25 on the bond you have to deliver, you’re paying $63 in order to get your $100 payout, for a profit of $37. On the other hand, if Argentina doesn’t default within a year, you lose your $38 insurance policy, for a loss of $38. Since the profit and the loss are roughly equal, that means the probability of default is roughly 50%.

What happens, however, if the price of the defaulted bonds doesn’t fall to $25? Right now the cheapest-to-deliver bond is trading at about $33, and I doubt that it’ll fall much further than that, even if Argentina doest default. In that case, the profit to someone who bought protection drops to $29, while the loss if Argentina fails to default within a year remains $38. You wouldn’t take that trade if the probability of default was only 50%: the implied probability of default now rises to something more like 60%. And remember too that the price of the cheapest-to-deliver bond could conceivably rise post-default, depending on the actions of the Argentine government and how it decided to intervene in the markets. After all, the Argentines have a strong political interest in minimizing the profits of those who have bet against them.

The fact is that the markets know full well that countries like Argentina can and will default occasionally, despite the fact that standard CDS calculations always think of defaults as one-off events. (Just look at the presence on the league table of Argentina, Pakistan, Ukraine, and Iraq, all of which have defaulted in recent years and seem to be reasonably likely to do so again within the next half-decade.)

In a fascinating new paper, for the Deutsche Bundesbank, Klaus Adam and Michael Grill try to calculate an optimum sovereign default strategy: they try to work out when it makes sense for a sovereign to default, and when it doesn’t. And it all comes down to what they call λ, a variable which measures the cost of default to a country. They write:

We first consider — for benchmark purposes — a setting without default costs (λ=0). As we show, the full repayment assumption is then suboptimal under commitment and sovereign default is optimal for virtually all productivity realizations. This holds true independently of the country’s net foreign asset position. We then show for “prohibitive” default cost levels with λ≥1, default is never optimal.

The thing to remember, as you read this, is that λ is a variable, even though for the purposes of the paper it’s treated as though it doesn’t change. And while λ might well be relatively high for a country like Germany, the more that a country defaults, the lower it becomes. After all, a lot of the cost of default is related to the lack of market access, and countries like Argentina have precious little market access even if they don’t default.

What we’re seeing in countries like Argentina and Ecuador, I think, is a rational response to λ falling to levels very close to zero. When that happens, such countries will default quite often — and that frequent default will be baked in to bond prices no matter how healthy the country’s broader economy. As a result, the “official” default probabilities for serial defaulters like Argentina are almost always going to be understated. Although I still think that buying 1-year protection on Argentina right at current levels is probably quite a good bet.

Remember synthetic CDOs, the monsters of the financial crisis? Well, according to Mary Childs, they’re “making a comeback”:

Citigroup Inc. (C) is among banks that have sold as much as $1 billion of synthetic collateralized debt obligations this year, following $2 billion in all of 2012, according to estimates from the New York-based lender.

These numbers come from Mickey Bhatia, the head of structured credit at Citigroup, and they’re in stark contrast to the official numbers from Sifma, which show precisely zero synthetic CDOs in 2012, and a total of just $713.4 million in CDOs over the past four years. Read a bit further on, however, and you’ll see that Bhatia is talking about the notional amount of the trades in question; the actual dollar value of the synthetic CDOs being sold is unclear.

As you can tell from the fact that these deals aren’t showing up in the Sifma database, they’re not exactly the same as the creatures which helped to blow up the world in 2008. For one thing, as Childs notes, these new deals don’t bother taking derivatives and turning them into rated securities which can be bought and sold: they’re just bilateral deals with sophisticated clients looking for certain types of exposure.

One such client, quoted in Childs’s piece, is Ashish Shah, the head of global credit at New York-based AllianceBernstein. Encouragingly, he was previously head of credit strategy at Lehman Brothers, which I’m sure reassures everybody who’s entrusting their life savings to the kind of person who says that “a valid strategy for this part of the credit cycle” is to “leverage your exposure to better-quality credit”.

Tom Davidson of Creditflux explained in an article on March 7 that these products — he calls them “single tranche corporate collateralised swap obligations” — are being pushed by “dealers who retain an active correlation book, with Citi leading the charge”. If you don’t know what a correlation book is, my article “Recipe for Disaster: The Formula That Killed Wall Street” might help you out; basically, it’s a place to make bets on whether you think correlations are rising or falling, and/or a place to lose billions of dollars which you never thought were really at risk in the first place.

All of which raises a couple of interesting questions: Why does Citigroup, of all banks, have Wall Street’s largest correlation book? And does anybody think that Citigroup has the risk-management chops to ensure that it doesn’t blow up, a la the London whale?

It seems that what’s going on here is that Citi is trying to hedge its loan book by using credit derivatives; in turn, its credit-derivatives wonks are creating recondite structured products to sell to the buy-side in an attempt to mold their credit risk into exactly the shape they want. I, for one, don’t trust Citigroup — or anybody, really — to be good at this kind of thing: the lesson of the financial crisis, and for that matter of the Senate report into JP Morgan’s Chief Investment Office, is that everybody on Wall Street systematically overestimates the quality of their risk-management skills. If you want to manage risk, do it in simple and obvious ways, rather than by the use of highly-illiquid credit derivatives.

If I were Citigroup’s regulator, then, I’d be asking a lot of questions about whether any of this activity is really necessary or a good idea. Because all of this smells very much like 2006 to me.

I’d never heard of Australian federal judge Jayne Jagot before today, but she’s my new favorite jurist, thanks to her decision in a recent court case which was brought against ABN Amro and Standard & Poors.

The coverage of the decision (Quartz, FT, WSJ, Bloomberg, Reuters) concentrates, as it should, on the hugely important precedent being set here: that a ratings agency — in this case, S&P — is being found liable for losses that an investor suffered after trusting that agency.

S&P is appealing the decision, which runs to an astonishing 635,500 words, or almost 1,500 pages: it’s literally longer than War and Peace. At this point, it’s fair to assume that Jagot is one of the world’s foremost experts on structuring and rating CPDOs — crazy derivative instruments which had a brief moment of glory at the end of 2006 before imploding spectacularly during the financial crisis. And helpfully, her decision begins with a 56-paragraph summary of her findings, which lays out exactly how culpable and incompetent S&P really was.

I haven’t read the whole decision, of course, but I have read the summary — and as someone who’s been writing about CPDOs for six years now, I can attest that Jagot’s summary is the single best one-stop shop for understanding what happened with these things. She concedes at the beginning that “the explanation unavoidably refers to complex concepts which are likely to be unfamiliar to those without specialist expertise in structured finance” — this is not easy reading, and if you don’t enjoy nerding out with such concepts, I would never recommend it to you. But if you are reasonably familiar with credit default swaps, Monte Carlo simulations, volatility assumptions, and the like, then I highly recommend you read Jagot’s summary: it’s utterly eye-opening.

The case at heart is a simple one: 12 local councils in Australia bought a bunch of CPDOs, and they only did so because S&P had given those instruments a triple-A rating. S&P, in turn, should never have given the CPDOs that triple-A rating. So it’s S&P’s fault that the councils lost so much money — jointly with ABN Amro, which structured the things.

How does Jagot come to the conclusion that “a reasonably competent ratings agency” would never have given the CPDOs a triple-A rating? Simple: S&P used utterly bonkers assumptions in order to come to its conclusion.

The way that structured finance worked, pre-crisis, was that banks would come up with ever more ingenious ways of structuring products which just qualified for a triple-A rating; they would then try to persuade the ratings agencies that their reasoning was kosher. This case was no different: ABN Amro put together a model, plugged a bunch of numbers into the model, and — presto — the model spat out a default rate which was so low as to justify a triple-A rating. It then showed the model to S&P.

Given such a new concept and product, S&P really should have developed its own model from scratch. It didn’t, however — it used ABN Amro’s model instead. That was bad enough. But worse — much worse — was that S&P didn’t even come up with its own assumptions to plug into the model: it used ABN Amro’s assumptions! Even though those assumptions were unjustifiable.

Bloomberg’s Mark Gilbert explained the concept of model risk at the time:

Derivatives-based trading strategies rely on computer simulations to test their performance under different market scenarios. These simulations typically expect the future to be like the past; the collapse of Long-Term Capital Management LP in 1998 is proof of the danger of using inductive reasoning to extrapolate general laws from particular observable instances.

In my article about the Gaussian copula function, I explained how this played out in practice. CDOs were priced based on the assumption that the future would be like the past, and then when the future turned out to be very different, they blew up.

But as it turned out, if S&P had assumed that the future would be like the past — if it plugged the market realities of the time into ABN Amro’s model — the CPDOs would never have managed to get a triple-A rating. This is where Jagot’s summary is invaluable: it shows that in order to generate a triple-A credit rating, S&P basically had to stick its head in the sand and ignore market realities. Never mind the future, S&P couldn’t even model the present!

Just look at some of the assumptions which S&P made in order to be able to get the longed-for triple-A credit rating. The most glaring is the starting spread — the money investors put into CPDOs was then invested in a synthetic basket of corporate debt. The lower the yield on that debt, the less money the CPDO could make. And at the end of 2006, spreads on corporate debt were very low. When ABN Amro issued a CPDO called Rembrandt 2006-3, the spread in question was just 29bp. But S&P, in rating Rembrandt 2006-3, used a starting spread of 36bp.

The difference might seem small. But S&P knew that with a starting spread of 32bp, where ABN Amro had hedged the deal, the model could not spit out a triple-A rating. And ABN Amro knew that the triple-A rating could not be justified if the starting spread was lower than 35bp. Yet somehow Rembrandt 2006-3 managed to get a triple-A rating with the lower starting spread. How? S&P simply didn’t model it. Instead, they just used an earlier rating, from a deal which never got issued and which was modeled using the 36bp starting spread.

That wasn’t the only place where S&P made unjustifiable assumptions. For instance, check out the number they plugged in for volatility: ABN Amro assumed that the index had volatility of 15%. But it didn’t. In reality, the volatility of the index was somewhere between 28% and 29%. And S&P had recently rated a different product, called LSS, using a volatility assumption, for exactly the same index, of 35%. The problem was that with a volatility assumption of 35%, the CPDO would never rate triple-A. So S&P just discarded that figure entirely, and used ABN Amro’s instead.

Here’s Jagot:

S&P believed ABN Amro’s assertions that the actual average volatility of the Globoxx since inception was 15%. S&P did not calculate the volatility for itself although it could easily have done so and, in my view, was required to do so as a reasonably competent ratings agency…

This assumption as to volatility was unreasonably and unjustifiably low. It did not represent either a reasonably anticipated or expected (that is, non-stressed) input or market condition or an exceptional but plausible (that is, stressed) input or market condition. I am satisfied that but for this error about volatility the CPDO could not have been rated AAA by S&P on any rational or reasonable basis.

So S&P was seemingly incapable of looking at the market to see (a) the spread on corporate debt, or (b) the volatility of the index. Which probably explains another one of their whopping great errors: their assumption for where the index would be over the ten-year lifespan of the bonds. This was a number known as the long-term average spread, or LTAS, and once again, the higher the yield on corporate bonds, the better the CPDO would perform. Remember that when the CPDO was issued, the spread in question was around 30bp. So what did S&P assume it would be in future?

Although there was no rational or reasonable basis for doing so other than the fact that one approach enabled the CPDO to satisfy the AAA rating quantile and the other approach did not, S&P decided that its base case for modelling and rating the CPDO should be an LTAS of 40 bps for one year and 80 bps for nine years… Although the split between 40 bps and 80 bps was itself arbitrary, the most salient point is that the modelling showed that if the assumed LTAS of 40 bps was extended to two years then, all other assumptions being the same, the CPDO again did not meet the AAA rating quantile. S&P then drew a further arbitrary, irrational and unreasonable distinction between the lower LTAS of 40 bps lasting for one year as opposed to two years, the result of which was that on S&P’s approach the CPDO achieved the AAA rating quantile default rate of 0.728% (which it did not if the lower LTAS of 40 bps continued for two years).

There’s really no way of reading what S&P did, here, except that it simply massaged the assumptions it was using until it managed to find something which was consistent with the triple-A rating it wanted. When spreads are at 30bp, what makes you think they’ll average 40bp over one year and then 80bp over nine years? Especially when the index as a whole has never averaged anything like 80bp? It’s simply not a reasonable assumption, and the fact that S&P made it just goes to show how the agency was acting for its paymasters — ABN Amro — and was not putting out reliable ratings at all.

There’s more, too. S&P also plugged into its model an assumption of 7% for something called roll-down benefit, or RDB. Every six months, the CPDO would exit its existing positions, and buy new positions in the index maturing six months later. In general, bonds which mature later have higher yields, so S&P assumed that on average, the new index would yield 7bp more than the old index. And that was an utterly crucial assumption. Never mind the triple-A rating: without the RDB, the CPDO couldn’t even get an investment-grade rating. It wouldn’t even be triple-B.

That was a reasonable assumption, at the time — in terms of the general slope of the yield curve. In general, corporate bonds do tend to yield about 7bp more if they mature 6 months later. But in assuming the 7bp figure, S&P completely ignored the fact that it was comparing apples and oranges: the components of the new index would not always be the same as the components of the old index. After all, the index was an index of investment-grade corporate debt, so if a company lost its investment-grade credit rating, it wouldn’t be included in the index any more.

You’d think that a ratings agency, of all institutions, would be alive to the risk of ratings downgrades. But, it turns out, not so much. ABN Amro, in its model , simply didn’t include what’s known as “ratings migration” — and S&P, similarly, completely ignored it.

The result, in reality, was devastating. Because companies could borrow at such low rates, they were particularly vulnerable to being taken over by private-equity firms which could load them up with cheap debt, devastating their credit ratings. And that’s exactly what happened. A whole series of investment-grade companies, like Alliance Boots, Alltel, and Boston Scientific, got levered up by their new private-equity owners, and lost their investment-grade credit ratings.

When those companies dropped out of the index, the companies which were left had significantly lower yields than the index as a whole did before. As a result, rather than yielding 7bp more than the old index, the new index actually yielded 15bp less. That just devastated the CPDOs, and ultimately led them to default.

Put it all together, and you get a very shocking view of S&P. Here’s the list:

• S&P used the wrong model input for starting spread.
• S&P used the wrong model input for volatilty.
• S&P used the wrong model input for average spread.
• S&P completely ignored ratings migration.

If S&P had just got any one of these things right, the CPDO would never have gotten that triple-A rating. If it had got them all right, the CPDO would almost certainly not even have been investment grade, let alone triple-A.

S&P was not doing its job, and as a result a bunch of Australian municipalities lost a great deal of money. Jagot has found S&P liable, as she should. Good for her.

Last week, Arik Hesseldahl — a tech writer who’s the first to admit he’s no expert on finance — discovered the wonderful world of credit default swaps in general, and single-name CDS on Hewlett-Packard, in particular. The cost of single-name protection on HP has been going up, and that can only mean one thing: it’s “mainly a barometer of the state of anxiety over its finances and its balance sheet”, he wrote.

Hesseldahl’s corporate cousins Rolfe Winkler and Matt Wirz followed up a couple of days ago:

A $10 million five-year insurance policy on H-P debt costs $260,000 according to data provider Markit. That price has doubled since April and quadrupled since a year ago. While there is no prospect of H-P going under any time soon, bond investors are clearly unhappy about the company’s deteriorating prospects and balance sheet.

But I don’t buy it. For one thing, as David Merkel points out in a comment on on Hesseldahl’s latest post, the HP bond market is not panicking at all: its bond prices remain perfectly healthy. He continues:

The markets for single name CDS are thin because there are no natural counterparties that want to nakedly go long credit risk. Those wanting to nakedly short credit risk therefore have to pay a premium to do so, usually higher than the credit spread inherent on a corporate bond of the same maturity.

And if one or two hedge funds want to do it “in size,” guess what? The CDS market will back off considerably, and make them pay through the nose.

It’s hard to spook the bond market for a liquid bond issuer; it is easy to spook the CDS market.

Why might one or two hedge funds suddenly want to buy protection on HP (and Dell, and Lexmark)? There’s an easy and obvious explanation: their share prices. HP, Dell, and Lexmark are all trading at less than 7 times earnings, at the lowest prices they’ve seen in a decade. They’re all in the fast-changing and volatile technology business. The only certainty here is uncertainty: it’s reasonable to assume that in five years’ time, each of these companies is going to be in a very different place to where it is now.

Which sets up an easy and obvious capital-structure arbitrage. You go long the stock, and then you hedge with single-name credit protection — the only way you can effectively go short the debt. The stock market is deep and liquid enough that you can buy your shares without moving the market; the single-name CDS market isn’t, but no mind. Even if you overpay a bit for the CDS, the trade still looks attractive, on a five-year time horizon.

In five years’ time, it’s entirely possible that at least one of these companies will be toast — in which case anybody who bought the CDS today will have scored a home run. On the other hand, if they’re not toast, the stocks are likely to be significantly higher than they are right now. Basically, the stock price is incorporating a significant probability of collapse, and if you take that probability away, then it should be much higher. And buying protection in the CDS market is one way of effectively taking that probability away.

This kind of trade only works for companies in unpredictable sectors that have low stock prices and relatively low borrowing costs; such opportunities don’t come along very often. But when they do come along, it’s entirely predictable that a hedge fund or two will put on this kind of trade. In no way are such trades a sign that bond investors are worried about the company’s future: in fact, bond investors are not the kind of investors who put on this trade at all.

And so, as Merkel says, reporters should be very wary indeed of drawing too many conclusions from movements in the illiquid CDS market. Sometimes, they really don’t mean anything at all.

I’m not sure which is more flattering: someone getting the Gaussian copula function tattooed across his arm, or Donald MacKenzie titling his latest paper after my Wired story on that function.

MacKenzie is a very smart sociologist, who understands quants and copula functions much more deeply than I ever did. (And, like most journalists, I forgot nearly all of what I ever knew about them within weeks of writing the article.) His paper is largely sociological, and I wouldn’t recommend reading it if you don’t like running across phrases like “the beginnings of a typology of mechanisms of counterperformativity”. But the good news is that if you want an English-language translation, Lisa Pollack has done an amazing job of extracting the interesting bits, and there’s no reason for me to try to replicate what she’s already done so well.

Here’s how Lisa sums it up:

The quant community also didn’t, and doesn’t, rate the Gaussian copula model highly at all. In fact, we’re putting that very mildly if the statements from quants interviewed by the researchers are anything to go by.

Furthermore, this was a view held by many before the financial crisis hit. But even in the face of this rejection, the model has stayed in use through multiple crises and is still in use.

I’m going to push back here a little bit. For one thing, although the “many” is implied, it’s never quite stated. Yes, MacKenzie interviewed 29 people before the crisis, including 24 quants. But all the interviews took place after the “correlation crisis” of 2005, when the weaknesses of the Gaussian copula function first became obvious. And in fact MacKenzie cites only one pre-crisis interview with a quant who was very skeptical of the function — and that was with “a quant who had contributed importantly to its technical development”.

My reaction to this is that of course anybody who contributed to the technical development of the Gaussian copula function was highly aware of its weaknesses. In fact, that was a theme running through my article, as summed up in the quote with which I ended it, from David Li, the inventor of the function. “The most dangerous part,” he said, “is when people believe everything coming out of it”.

What I suspect, here, but don’t know for sure because MacKenzie doesn’t really describe his interviewees, is that the people he talked to for this paper tended to be the most senior, worldly, introspective, and successful quants — not to mention the ones who spoke good English. Every bank has graybeards who love to talk about how tools like Gaussian copula functions or value-at-risk are massively inadequate. Those graybeards get a lot of lip-service. But in practice, both senior management and the traders in the trenches end up using the quick-and-dirty measures because they don’t have the time or the sophistication to do anything else.

MacKenzie doesn’t buy the idea of “F9 monkeys” — people who just input a security, press F9, and out pops a price. But he seems much more interested in making a broader sociological point:

In our research experience, the ‘model dope’ exists, but not as an actual person: rather (in the form of, e.g., ‘sheet monkey’ or ‘F9 model monkey’) it is a rhetorical device that actors deploy. It is a way of describing someone as different from oneself: a way of ‘othering’ them. There is no clear evidence in our research of model-dope behaviour or beliefs. (For example, none of the 29 interviews we conducted prior to the crisis contains anything approaching an unequivocal endorsement of Gaussian copula models.) Any satisfactory notion of ‘culture’, it seems to us, must treat the cultural dope and its local equivalents such as ‘sheet monkey’ as forms of othering, not adequate conceptualizations of the actor. Nor are ‘cultures’ equivalent to sets of meanings, symbols and values: they encompass practices, including the most material of practices. Ultimately, culture should be treated as a verb, not a noun (it is unfortunate that use of the verb is currently restricted to its biological sense): people do cultures, rather than culture existing as a thing causing them to act as they do.

I’ll leave the discussions of verbing culture and “othering” to MacKenzie, but I do still believe that on a day-to-day basis, banks were full of people who were happy to just accept whatever the model spit out — especially, as MacKenzie demonstrates, if doing so did wonders for their end-of-year bonus. And while MacKenzie draws distinctions between banks and investors, and between credit-based and asset-backed securitizations, even he admits that there were people managing enormous CDOs who didn’t even know what a correlation model was, let alone have a deeply skeptical attitude towards it.

The fact is, as MacKenzie demonstrates, that there were enormous numbers of people whose bonuses depended on believing that the numbers thrown out by the Gaussian copula function were accurate:

In the early years of the credit derivatives market it was not unusual for traders to sell a deal ‘at par’ – 100 cents in the dollar – when their ‘bank[‘s] system would have told them that this was worth about 70 cents’. A single trade ‘would make [$]20 million of P&L.’)

While it’s possible, in that context, for a determined researcher in an in-depth interview to elicit doubts about the utility of the function from relatively senior people, it’s really not possible for those doubts to get acted on, or even to really exist in the bank itself, as opposed to in discussions down the pub after work.

MacKenzie doesn’t like the way my article simplified the sociology of Wall Street:

The seventh section then returns to the question posed by Salmon’s article, enquiring into the extent to which the Gaussian copula family of models ‘killed Wall Street’. An adequate answer, we posit, demands both a differentiated understanding of that family of models and also, more importantly, a grasp of the fact that a model never has effects ‘in itself’ but only via the material cultures and organizational processes in which it is embedded.

I genuinely have no idea what a “material culture” might be; I’ll leave that kind of thing to MacKenzie. And I’ll admit that the “Wall Street” of my title was a broad church, including not only investment banks but basically anybody who might ever touch a CDO. If MacKenzie wants to make the case that Gaussian credulity was found much more on the buy side and at the credit rating agencies than at the sell side, I’ll happily give him that.

But the fact is that the Gaussian copula function was invidious, and did cause enormous losses all over the world. The way I like to think about it is that value-at-risk allowed banks to ignore tail risk, and the Gaussian copula function did a magnificent job in maximizing that tail risk. The two together were lethal. And while there are surely many other models which inhabit Wall Street in much the same way, I sincerely hope that none of them are remotely as dangerous as the Gaussian copula function turned out to be.

Matt Levine has an excellent post on the latest storm in a CDS teacup, which has been prompted by Europe’s bailout of Spanish banks. If you feel any need to follow this kind of thing at all, here’s basically what you need to know.

Firstly, this bailout is going to add a good €100 billion or so to Spain’s national debt, over and above its existing bonds. That in and of itself makes Spain less creditworthy: the more debt you have, the lower the chances are that you’re going to be able to pay it all back.

More worryingly, for holders of Spain’s national debt, this new bank-bailout debt (which is owed by the country of Spain, remember, since the money isn’t going directly to the banks) carries something known as preferred creditor status. That means that if push comes to shove, Spain will repay the bailout debt before repaying any of its bonds.

To take a simplified example: if Spain has €100 billion in bailout debt due and another €200 billion in bond payments due, and only has €150 billion on hand, then an equitable treatment would be to ask each of its creditors to take a 50% haircut. But with preferred creditor status, Europe will get its €100 billion back in full, and bondholders would have to take a 75% haircut. So holding Spanish bonds just became significantly riskier, this weekend.

Now here comes the CDS angle: if this subordination is written into European law, does that mean that the Europeans just subordinated Spain’s bondholders? Because if they did, then that might count as a credit event, and allow anybody holding Spanish CDS to trigger those CDS and ask to be paid out in full.

There’s lots of discussion here about the difference between the two different places that the money for the Spanish bank bailout might come from: the EFSF, on the one hand, and the ESM, on the other. The ESM’s preferred-creditor status is enshrined in law; the EFSF’s isn’t. In practice, as Joseph Cotterill points out, they behave the same way: European countries will treat the EFSF exactly the same way they treat the ESM, and the EFSF managed to get away haircut-free in the Greek restructuring. But as far as CDS documentation is concerned, what happens in practice doesn’t matter. What matters is the legal theory.

And when it comes to the legalese, it doesn’t get much more impenetrable than this:

“Subordination” means, with respect to an obligation (the “Subordinated Obligation”) and another obligation of the Reference Entity to which such obligation is being compared (the “Senior Obligation”), a contractual, trust or similar arrangement providing that (i) upon the liquidation, dissolution, reorganization or winding up of the Reference Entity, claims of the holders of the Senior Obligation will be satisfied prior to the claims of the holders of the Subordinated Obligation or (ii) the holders of the Subordinated Obligation will not be entitled to receive or retain payments in respect of their claims against the Reference Entity at any time that the Reference Entity is in payment arrears or is otherwise in default under the Senior Obligation. … For purposes of determining whether Subordination exists or whether an obligation is Subordinated with respect to another obligation to which it is being compared, the existence of preferred creditors arising by operation of law or of collateral, credit support or other credit enhancement arrangements shall not be taken into account, except that, notwithstanding the foregoing, priorities arising by operation of law shall be taken into account where the Reference Entity is a Sovereign.

Christopher Whittall has found some lawyers willing to stick their necks out and translate this into English. Basically, there are two ways that CDS can be triggered on the grounds of subordination. The first one is moot, since it applies to entities which can be liquidated, and therefore clearly doesn’t apply to sovereigns. The second one is a bit more complicated, but it basically comes down to the question of whether Spain would be allowed to pay its bondholders if it was in arrears to the ESM. And that’s a question of Spanish law, not European treaty. Unless and until the Spaniards pass a law to that effect, the CDS probably can’t be triggered — which isn’t to say that some enterprising hedge-fund manager somewhere isn’t going to give it the old college try.

Even if the CDS were triggered, however, that wouldn’t be the worst thing in the world. So long as Spain keeps on paying its bond payments on time, the CDS auction, were there to be such a thing, would happen at a pretty high price, and would be no big deal. Think of the auction for Fannie Mae and Freddie Mac: they both had a credit event, but the clearing price was basically par, so holders of CDS didn’t make any kind of profits.

And more generally, the fact that the European Union has been so blasé about these matters is definitely encouraging. Once upon a time, lots of Eurocrats seemed to think that they really had to worry about the CDS market, and whether there was a credit event in Greece. Now, in the wake of the Greek restructuring, they’ve grown up, and they understand that the credit derivatives market is not important enough to worry about or to build policy around. They’re going to do their thing, and the CDS market will react as it may. (Including, perhaps, by just giving up on the whole sovereign-CDS thing entirely.)

In other words, the subordination matters; whether or not the subordination constitutes a credit event under ISDA rules, not so much. As the EFSF and ESM continue to disburse money to the European periphery, that’s the thing to concentrate on most: how much money have they given out, and when do they need to be repaid? Because all of those payments are going to come first, before any payments to bondholders.

There are a few different ways to look at the seemingly-unstoppable rise of the amount of “excess deposits” that JP Morgan ended up handing to its Chief Investment Office, rather than lending out to individuals and businesses needing loans. Maybe big corporations are flocking to deposit their billions at Chase because they know it’s too big to fail. Maybe Chase just can’t find anybody who both wants to borrow money and is likely to pay it back. Maybe — and more likely — Jamie Dimon funneled increasing sums to the CIO just because the CIO could generate a higher internal rate of return than his plain-vanilla lenders could.

But as Roger Lowenstein explains today, a large part of what we’re seeing here is the way in which lending has morphed into investing. All of us intuitively understand that there’s a difference between lending someone money, on the one hand, and buying a bond, on the other. The former is a bilateral contractual relationship which lasts until the loan is repaid; the latter is an anonymous purchase of securities which can be flipped for a profit (or sold at a loss) after weeks or days or even minutes.

The CIO, playing in the bond and derivatives markets, is very much in the latter camp rather than the former, as you can guess by looking at its name. It makes investments, rather than disbursing loans. But the danger here is not just that $400 billion of JP Morgan’s assets are being put to work gambling in the markets rather than extending loans to clients. The danger is that as the CIO gets bigger, it effectively turns the JP Morgan Chase loan portfolio into an investment, too.

It’s worth quoting Lowenstein at some length, here:

The new Jamie, and the people working for him, don’t have to worry quite so much. They know that if they become uncomfortable with the loans they can always hedge them in the derivatives market…

When JPMorgan hedges, it doesn’t get rid of the risk. That only happens when the customer repays the loan or, say, improves its balance sheet. JPMorgan’s hedges didn’t make the risk disappear; they merely transferred it to someone else.

Jamie had an escape hatch, but hedging doesn’t offer an escape for markets as a whole…

JPMorgan still issues loans but with half an eye on their “hedging” potential, that is, on the willingness of traders who may be halfway around the globe to assume the risk. These traders are less well-placed to evaluate the risk. They don’t know the customer and, of course, they haven’t the faintest concern for character. By habit and preference, their involvement is apt to be brief.

They assume risk by writing a swap contract in the full knowledge that they can unwind it via another swap days or even hours later. Someone may get stuck with the bad coin but, each trader is certain, it won’t be him or her. So the approach of these traders is inherently short-term — too short to invest the time and effort to evaluate the risk. Too short, we might say, to really care.

The plasticity of modern finance — the ease with which institutions can transfer risk — is a major cause of the heightened frequency of meltdowns and increased volatility.

To put this another way: liquidity is dangerous, because it breeds complacency. All you need to do is set a stop-loss, and you’ve protected yourself from large losses. Until, of course, the markets seize up and bids simply disappear from the market altogether. Or until your elaborate and complex hedging operations turn out to have been badly constructed, and you wake up in the morning with a loss pegged at $2 billion and growing.

Alan Greenspan famously said in 2003 that “what we have found over the years in the marketplace is that derivatives have been an extraordinarily useful vehicle to transfer risk from those who shouldn’t be taking it to those who are willing to and are capable of doing so.” In practice, if you look at the actions of the CIO, derivatives were used to transfer risk from those who should have been taking it — big lenders — to hedge funds who make money only when JP Morgan turns out to have fundamentally miscalculated its risk basis.

Entities who want to really take on credit risk are called banks, and they do so by lending. People who sell credit protection in the markets, by contrast, are traders and speculators who trust in the liquidity of the CDS market and who are sure that they will be able to get out quickly if things turn against them. And thus is the CDS market used shunt risks off, unseen, into the tails.

Liquidity isn’t just dangerous in the loan market. Look at houses, which used to be highly-illiquid investments characterized by a long-term relationship between a homeowner and a lender. When did things fall apart? When that relationship was replaced by a frenzy of securitization and refinancings, with even 30-year mortgages lasting for just a year or two before they were paid off by someone flipping their house or deciding they needed a cash-out refinance. The more liquid housing became — the closer it came to being piggy bank, to be tapped for cash at any time — the more dangerous it became, as well.

Lowenstein’s proposed solution — banning credit default swaps entirely — is not going to happen. But it’s a useful lens through which regulators should be looking at the banks they regulate.

Activity in the CDS market, on this view, is a sign of weakness, not strength: it’s a sign that the bank doesn’t have much faith in its own relationships and underwriting standards, and is reduced to having to buy protection from speculators in order to feel comfortable with the risks that it’s taking. Since those speculators, by definition, don’t have remotely as much information about the bank’s borrowers as the bank does, and since they certainly can’t put covenants into loans protecting them from profligacy at the borrower, such trades make very little economic sense in theory.

Regulators should remember this the next time a bank starts boasting about its sophisticated, state-of-the-art risk management systems. Most of the time, those systems involve complex bets in a zero-sum-game derivatives market, where the bank’s counterparties charge a premium for the fact that they’re on the wrong side of an information asymmetry. At best, in such cases, the bank is merely abdicating responsibility for its risks, rather than properly managing them. And at worst, it thinks that it has gotten the credit risk off its books, when in fact it’s just pushed that risk into the tails, where it’s bigger than ever.

Either way, regulators should have precious little time for such antics. They should force banks to go back to basics, instead, and manage their risks the old-fashioned way, by building strong relationships with their borrowers. Lending those borrowers money right now, when such lending is sorely needed, would be a good start.

In February 2009, Deutsche Bank announced that its Credit Trading desk had managed to lose €3.4 billion in the fourth quarter of 2008, with €1 billion of those losses directly attributable to the bank’s prop desk.

The losses in the Credit Proprietary Trading business were mainly driven by losses on long positions in the U.S. Automotive sector and by falling corporate and convertible bond prices and basis widening versus the Credit Default Swaps (CDS) established to hedge them.

In English, Deutsche Bank had put on a basis trade: it owned credit instruments, like bonds, and it also owned credit default swaps designed to hedge against those loans. And then the trade blew up.

The Deutsche trader responsible for the monster losses was Boaz Weinstein, who eventually left the bank to start his own hedge fund, Saba Capital. His first job, obviously, was to make sure he didn’t blow up a second time. But his second job, it seems, was to use his experience at Deutsche to be able to notice when someone else was about to blow up on a massive basis trade. In this case, JP Morgan.

Go back to early February, long before the articles about the “London Whale” came out in Bloomberg and the WSJ, and you’ll find Weinstein revealing his biggest trade at the Harbor Investment Conference:

The derivatives trader and legendary hedge fund manager said his trade idea is to buy Investment Grade Series 9 10-Year Index CDS (maturing on 12/20/2017).

“They are very attractive,” he explained adding that they can be bought at a “very good discount.”

At the time, Weinstein didn’t know — or necessarily even suspect — that his big trade would involve a zero-sum bet with one of the biggest hedge funds in the world, JP Morgan’s Chief Investment Office. But over time, as he bought more and more protection but the price stubbornly refused to rise, he began to learn just how big the other size of the trade was. Whale big.

Tracy Alloway and Sam Jones have pieced together the best account yet of what exactly JP Morgan was up to. Yet again, it was a basis trade, although this one was horribly complex even by basis-trade standards. Essentially, that CDX.NA.IG.9 position was a second-order hedge, designed to offset volatility in JP Morgan’s first-order hedge, which was designed to offset credit risk in the rest of the bank’s portfolio.

The first-order hedge itself doesn’t make a great deal of sense — Iksil seems to have bought “tranches” of CDS indices, which would pay off if some (but not all) credits suddenly got into trouble. For a bank which had broad economic exposure to European meltdown and/or a US double dip, that seems like a pretty narrow hedge.

But if the first-order hedge is weird, the second-order hedge is downright scary. Do you remember the notorious Howie Hubler trade at Morgan Stanley, where he made a smart bet against dangerous subprime securities, but then put on a much larger “hedge” which ended up costing him $9 billion? Iksil’s trade seems a bit like that:

Because of the mechanics of the trade, in order to achieve a “market neutral” position, whereby JPMorgan hedged the bet against volatility as best it could and offset the cost of its short positions, the bank had to sell far more units of cheap protection on the IG.9 as a whole than it bought on short, more expensive tranches.

Inevitably things started to go wrong. There are two things you can do when something starts to go wrong in the markets. You can unwind your position at a loss. Or you can try to fix it. Iksil, and Drew, chose the latter:

The two legs of JPMorgan’s trade did not move according to the relationship the bank had expected, meaning the position became imperfectly hedged. Like many credit models before it, JPMorgan appeared to misjudge correlation – one of the hardest market phenomena to accurately capture in mathematics.

In order to try and stay risk neutral, the dynamic hedge required even more long protection to be sold. The bank continued to write swaps on the IG.9, causing a pricing distortion that was spotted by more and more hedge funds seeking profit.

The rest, pretty much, is history.

Iksil, we’re told, is going to leave JP Morgan, while taking his own sweet time doing so: “although a spokeswoman for the bank said Mr. Iksil is still employed, he is no longer trading on behalf on the bank and is expected to be gone by the end of the year”. I’m sure he’ll use the intervening months to feel out his chances of being able to raise a few billion dollars for a hedge fund of his own, and weigh them up against simply joining a fund like Saba. Iksil’s now learned a $2 billion lesson — and as Boaz Weinstein can attest, once learned, those lessons can be surprisingly valuable.

You want proof that JP Morgan was — is — using its Chief Investment Office to gamble with taxpayer-backstopped funds?

The CIO unit also had a lower cost of capital than other parts of the bank, an artificial advantage that gave it an incentive to take more risk and behave in a less disciplined way, people familiar with the unit said.

“It was very large, but was never very transparent, and it wasn’t clear that they had an appropriate funding cost,” said the source with direct knowledge of the CIO.

In any unit of any bank, one of the key drivers of profit and loss is the internal cost of funds. If you’re paying 1% for your funds and earning 3%, then you can claim profits of the difference, 2%. But if your cost of funds is increased to 2%, then your profit is halved at a stroke. For someone like Ina Drew, who was charged with turning hedges into profitable trades, the easiest way to do that would always have been to simply get Jamie Dimon to decrease the CIO’s cost of funds.

And at JP Morgan, just like at any other bank, the cheapest cost of funds is always deposits. JP Morgan has hundreds of billions of dollars in excess deposits just because it’s too big to fail, and has an implicit government backstop. It’s bonkers that it should then be able to take the resulting ultra-low cost of funds, and turn it into eight-figure bonuses to people like Drew, all for taking that money and playing on derivatives indices in London.

As John Macaskill points out, the CIO, by its own faulty measurements, had for the past two quarters more money at risk than JP Morgan’s entire investment bank — and that was with a more lenient risk measurement and with a lower cost of capital. In reality, the CIO’s risk levels were vastly greater than those at the investment bank, as we discovered after the blow-up.

If JP Morgan wants the CIO to be taking that kind of risk, it has to significantly increase the CIO’s internal cost of funds. The CIO is at heart a hedge fund (it’s designed to put on hedges), and JP Morgan should extend it billions on the same kind of terms that it would extend money to top prime-brokerage clients. The CIO’s secret weapon, all these years, has been its artificially low cost of funds. If that number were more realistic, maybe JP Morgan wouldn’t have ended up parking such an insanely enormous amount of money there.

Ina Drew — the JP Morgan executive who famously “loves crises” — is out; it seems the buck for the $2 billion trading loss in her unit has stopped with her. And slowly, a few shapes are beginning to emerge from the fog of what exactly happened here.

For one thing, it’s becoming increasingly obvious that Drew got paid her eight-figure salary in return for being able to pull off a very neat trick: turning hedging operations into a profit center.

Drew’s Chief Investment Office quadrupled in size between 2006 and 2011, reaching $356 billion in total, and it’s easy to see how that happened. On the one hand, it was incredibly profitable, with the London team alone, which oversaw some $200 billion, making $5 billion of profit in 2010, more than 25% of JP Morgan’s net income for the year. At the same time JP Morgan accumulated enormous new deposits in the wake of the financial crisis, both by acquiring banks and by attracting big new clients wanting the safety of a too-big-to-fail bank. Historically, JP Morgan has served big corporations by lending them money, but nowadays, as the cash balances on corporate balance sheets get ever more enormous, the main thing these companies want from JP Morgan is a simple checking account — one where they can be sure that their money is safe.

With lots of deposits coming in, and little corporate demand for loans, it was easy for all that money to find its way to the Chief Investment Office, which could take any amount of liabilities (deposits are liabilities, for a bank) and turn them into assets generating billions of dollars in profits.

But the CIO does much more than just provide profits for JP Morgan. In contrast to the bank’s lending book, the CIO is nimble. Loans, as a rule, have to be held to maturity: that’s the essence of relationship banking. Investments, by contrast, can be sold at any time. Of course, an investment which can be sold at any time has another name: it’s a trade. Thus did the CIO become home to big traders, making huge bets and huge bonuses.

In the past couple of years, of course, that raised its own set of problems: how could this group of traders possibly be Volcker-compliant? The answer lay in Drew’s love of crises: her incredibly valuable ability to prevent losses and even make profits when the world is falling apart. In that sense, the CIO was one big hedge, and in a narrower sense the CIO was the go-to office whenever JP Morgan saw a risk which needed hedging.

Mark Dow has an intriguing thesis this morning:

We know that over the last 2-3 quarters of 2011 we were gripped by the fear of a European financial meltdown and a second recession in the US.

We know that that the Fed’s swap lines and the ECB’s LTRO reversed this market view and crushed credit spreads lower, hurting those who had been buying protection in the previous months.

Against this backdrop, it seems likely to me that the aggressive selling of protection we heard about in April 2012 was actually the unwinding of the hedge that had been accumulated in 2011 and was by then deeply underwater.

In other words, Jamie Dimon, like everybody else, was worried about a Europe-induced financial crisis at the end of 2011, and so he told Drew to put on positions which would protect against such a crisis. She did so — only this time around, the crisis never happened, and Drew’s positions had to be unwound.

That’s where things seem to have gone very, very wrong. Drew prided herself on turning every hedge into a profit center — having her cake and eating it too, basically. We’re deep in the realm of speculation, here, but it’s entirely possible that Drew positioned the CIO, at the end of 2011, to profit from a European meltdown. When that meltdown didn’t happen, simply selling those positions would involve realizing a substantial loss. And so rather than selling the positions, Drew decided to put on new, profitable positions which would offset the old hedge. Enter Bruno Iksil, the London Whale, and his enormous trade.

Iksil’s trade was fundamentally bullish, which would make sense for a trade designed to offset a fundamentally bearish hedge. Of course it wasn’t a perfect offset — there’s no such thing as a perfect hedge. Traders making multi-million-dollar bonuses don’t get paid to design perfect hedges, in any case. Iksil was being paid to put on a trade which would make money for the CIO, even as it was also hedging existing positions.

As with all imperfect hedges, however, especially when they’re big and public, the market can always move against you in exactly the way you don’t want. We don’t know the details of Iksil’s trade, but let’s say that the big underlying position was a bearish position in cash bonds, while Iksil’s trade involved a bullish position in the CDS market. In April, the cash and CDS markets stopped mirroring each other, and started behaving very oddly — you’d see bullish moves in cash bonds, combined with bearish moves in the CDS market. That combination, it seems, turned out to be the one thing that JP Morgan wasn’t hedged against, and the losses in the CIO started mounting rapidly.

How did Iksil’s trade go so horribly, massively, wrong? Partly it’s because his position was so big and so public. When hedge funds worked out what he was doing, they managed to get the word out, using stories in Bloomberg and the WSJ. And then it was just a matter of watching the market do what it always does, when it smells blood: I’m told that Boaz Weinstein’s Saba, for one, made a lot of money taking the other side of Iksil’s trade.

Taking a much bigger-picture view, however, what was really going on here was that JP Morgan had hundreds of billions of dollars in excess deposits, thanks to its too-big-to-fail status. And rather than lending out that money and boosting the economy, Jamie Dimon decided to simply play with it in financial markets, just as a hedge fund would. Here’s Bloomberg:

Dimon pushed Drew’s unit, which invests deposits the bank hasn’t loaned, to seek profit by speculating on higher-yielding assets such as credit derivatives, according to five former executives. The CEO suggested positions, a current executive said.

It’s always dangerous when a CEO suggests positions for an internal hedge fund to take, because the CEO by definition has no risk manager with enough authority to effectively constrain him. Dimon is powerful and secure enough that he’s not going to lose his job over this. But he probably should. Partly because the bank’s risk-management procedures were so weak that a $2 billion loss could suddenly appear out of nowhere. Partly because Dimon became too cocky, and started thinking that his job was to trade the bank’s billions for profit. But mainly because he’s lost sight of what JP Morgan has to be, in a post-crisis world.

Those excess deposits weren’t gifted to Dimon on a plate so that he could gamble them on the CDX NA IG 9. Rather, Dimon’s job is to take those deposits and lend them productively into the real economy. Every extra dollar in the CIO is a sign of his failure to do that. And the $2 billion loss is really just a symptom of what happens when banks get too much money, and don’t really know what to do with it all.

Here’s the chart you’ve all been waiting for, courtesy of Reuters’s very own Scotty Barber: the spread on the CDX NA IG 9 index — the synthetic index on which JP Morgan’s Bruno Iskil was selling enormous amounts of protection — minus the spread on the index’s constituent bonds.

Three things jump out here. Firstly, the basis is negative, not positive. That means that the obvious trade was to buy the underlying bonds and hedge by buying protection on the index. That obvious trade, if held to maturity, should always make money. Iksil was funding that trade, by selling protection on the index.

Secondly, the chart is going up and to the right. Since Iksil was selling protection, that means the market was moving against him. Or, to put it another way, the obvious trade makes money when it expires at zero, and as the chart moves towards zero, Iksil loses money on a mark-to-market basis.

Finally, the move doesn’t seem to be all that huge — only about 30bp in this quarter. Which doesn’t seem remotely enough to cause a $2 billion loss. Still, Iksil managed it somehow.

Update: Many thanks to Sally Kohn for making the chart infinitely better by putting whales on it.

We’re still not much the wiser on exactly how the London Whale managed to lose $2 billion this quarter, but I think Matt Levine has the smartest take. (This is why the blogosphere is so great: it’s full of people who used to do this kind of thing for a living, rather than just people who write about people who do this for a living.)

The key thing to note here is that while the monster hit to the P&L is what got all the headlines, the real problem here lay with JP Morgan’s risk models. A hint of far out of whack they are is given in the difference between the bank’s earnings release, which showed $67 million of value-at-risk in the Whale’s division in the first quarter, and the new SEC filing, which showed that number as actually being $129 million. Here’s Levine:

This was attributed to modeling changes made over the last year, and someone asked on the call “why did you change the VaR model?,” but I’m not convinced that’s exactly the right question. This, I suspect, is not an issue of a thing called a “VaR model” that sits in a central location and spits out numbers for regulators and 10-Qs; rather, this looks like the CIO’s trading desk modelling the actual P&L and risks of the trade wildly wrong. That seems to me like the simplest way to lose a billion dollars without noticing it.

I’d put this another way. JP Morgan’s Bruno Iksil, it seems, managed to find an incredibly profitable way of hedging the bank’s positions. Like any other economically rational actor, when he saw a lot of dollar bills lying on the sidewalk, he decided to pick them up. But in Iksil’s highly-complex world, a dollar bill isn’t really a dollar bill. Instead, it’s the output of a model. And if a trader can’t trust his model, he’s flying blind.

The problem is that pretty much by definition, it’s impossible to model model risk. We now know that Iksil’s model was deeply flawed. And indeed the minute that the rest of the world found out about his positions, they didn’t really pass the smell test: it’s very hard to see how writing an enormous amount of protection on an off-the-run CDX index would hedge anything much.

This is where grown-ups like Jamie Dimon are meant to step in. If they see billions of dollars in super-senior mortgage exposure, or in off-the-run CDX exposure, they’re meant to say “I know that your highfalutin’ models say that these exposures are risk free, but I don’t understand how this isn’t risky, so go unwind this trade”. Dimon has historically been very good at that — very good at refusing to simply trust that superstar traders earning eight-figure bonuses are doing nothing that might blow up in their faces. In this case, however, for some reason, he had blind faith in Iksil — and in Iksil’s models, which proved to be very faulty.

A modern trading desk is a bit like a high-tech airplane: nearly all of the time, you’re better off trusting your instruments than trusting your gut. But at the same time, if your instruments are broken, then trusting them can lead you to fly straight into the ocean.

This is why Basel I turned out to be much more robust than Basel II. Your sophisticated platform needs to be built on a foundation of dumb rules: simple limits on how big any one position can get, on how much exposure you can have to any one counterparty, or in general on any trade which is based on the hypothesis that your desk is smarter than anybody else on Wall Street.

Those kind of rules won’t prevent all blow-ups, of course, but they’ll help. They would have prevented this one, and they would have put an end to Jon Corzine’s disastrous MF Global trades, as well.

The problem is that traders hate dumb rules, because they cap the amount of money they can make. And traders have enormous power at investment banks these days, because they make the lion’s share of the profits. That’s why it’s important that the CEO of an investment bank not be a trader. And certainly it’s crucial that the CEO shouldn’t have his own trading account and buy and sell from his Blackberry during meetings, as Corzine did. That’s just a recipe for disaster.

I’m not sure if it was the biggest quarterly loss of all time, but Merrill Lynch’s $16 billion loss in the fourth quarter of 2008 certainly ranks very high up there in the annals of investment-bank blowups. It happened after the bank had already been taken over by Bank of America, and it was in the middle of the financial crisis, so it didn’t get nearly the amount of attention it deserved. But it was not simply a case of assets plunging in value. Instead, it was, in very large part, a basis trade blowup.

The basis trade is an arbitrage, basically. There are two different ways the market measures credit risk: by looking at credit spreads — the yield on a certain issuer’s bonds, relative to the risk-free rate — or by looking at CDS spreads, which are basically the same thing but set in the derivatives market rather than the cash bond market. Most of the time, CDS spreads and cash spreads are tightly coupled. But sometimes they’re not. And at Merrill, a huge part of that $16 billion loss was reportedly due to a bad basis bet: the basis on many credits became very large and very negative during the financial crisis.

This time around, the basis-trade disaster has happened at JP Morgan, where the famous London Whale seems to have contrived to lose $2 billion on what was meant to be a hedging operation. And once again, although the details are still very murky, the culprit seems to be the CDS-cash basis.

I’ve been meaning to write a post about the CDS-cash basis for a few days now, which is why I happen to have this chart handy, showing the basis for various European banks as of Tuesday May 8.

These are very big numbers, for very big banks: UBS is at 75bp, Deutsche is at 83bp, Natixis is at 116bp, and IKB is at a whopping 392bp. And this is just the banks — other corporates have seen similar price action. The cost of protection has gone up sharply, while the cash bonds are still trading at very low spreads.

Bruno Iksil, the London Whale, had a massive long position on corporate CDS in general, and the CDX.NA.IG.9 index in particular. He was selling protection, betting that credit spreads would go down, rather than up. The position was meant to be a hedge, although it’s a bit unclear how JP Morgan could have some massive short position in corporate debt that it was hedging against. In any case, CDS spreads went up — and credit spreads, in the cash market, didn’t.

Cue a $2 billion loss.

Rarely has a position been as widely publicized as Iksil’s, and I wouldn’t be at all surprised to learn that the credits with the highest basis were precisely the credits CDX.NA.IG.9 index. Whenever a trader has a large and known position, the market is almost certain to move violently against that trader — and that seems to be exactly what happened here. On the conference call, when asked what he should have been watching more closely, Dimon said “trading losses — and newspapers”. It wasn’t a joke. Once your positions become public knowledge, the market will smell blood.

Of course, this loss only goes to show how weak the Volcker Rule is: Dimon is adamant, and probably correct, in saying that Iksil’s bets were Volcker-compliant, despite the fact that they clearly violate the spirit of the rule. Now that we’ve entered election season, Congress isn’t going to step in to tighten things up — but maybe the SEC will pay more attention to Occupy’s letter, now. JP Morgan more or less invented risk management. If they can’t do it, no bank can. And no sensible regulator can ever trust the banks to self-regulate.

Nick Dunbar has a good column today on how derivatives have followed physics from being clean to being messy. Once upon a time, both physics and derivatives had beautiful, simple models: quantum electrodynamics and Black-Scholes, respectively. But nowadays they’re both vastly more complex.

Physics has moved on to quantum chromodynamics, where complex interactions dominate the simpler ones and models get gnarly, while derivatives have found themselves in a world of counterparty risks and debit valuation adjustments and credit support annexes. Put them all together, says Dunbar, and the result is that the “derivative trading books at major banks lurch around like aircraft in a thunderstorm”.

All of which makes me very happy to see Bruce Dorminey’s column about a rather exciting possible application of high-energy physics to global finance. Remember the $1.5 billion being spent on transarctic fiber cables designed to cut a bit of latency between London and Tokyo? Here’s an even better idea: why not get rid of fiber cables entirely, and use neutrinos to transmit information, at the speed of light, right through the center of the earth?

At the very least, this could provide a fantastic revenue bump for physicists working at extremely expensive particle accelerators in a world of fiscal austerity. In order to make this happen, you’d need to either build your own accelerator, or lease some capacity from an existing one. The sums involved are both big enough to make physicists salivate, and small enough that the private sector could raise the money quite easily:

Learned says a one-way, earth-traversing setup might be constructed for as little as a $1 billion.

It also might be possible for a neutrino-communications startup to buy time on an existing accelerator, says Learned, in order to create and point a neutrino beam-line in the needed direction. Otherwise, private particle accelerators would have to be built from scratch.

But Haug contends that if a group of particle physicists had the right plan for the technology, Wall Street money “would be there” to make it happen.

If this was successfully implemented, price information from Sydney could reach New York in just 40.2 milliseconds, compared to the 84.4 milliseconds it takes to send that information on fibers around the surface of the earth. The difference is more than enough time for traders in New York to make real money arbitraging securities listed in both cities.

Indeed, it wouldn’t even need to be a Wall Street bank building this technology. Back in the 1850s, Paul Julius Reuter built the company I work for today by leveraging state-of-the-art low-latency technology: the undersea cable between Calais and Dover. Maybe David Thomson can do the same with neutrinos. He might even be able to find jobs for a few of those physicist-quants laid off by Wall Street in recent years.

John Carney has been plugging away at what on earth Bruno Iskil, the so-called London Whale, might be doing with his reported $100 billion bet on an obscure off-the-run CDX index. Carney’s idea is that this is all part of some kind of inflation-protection trade, but as Ben Walsh says, if you want to protect against inflation, you just buy TIPS. Corporate credit default swaps aren’t going to help you out much on that front.

But thinking about it a bit more, Carney’s CHIPS theory (for Corporate Hedging Inflation-Protected Securities) makes a certain amount of sense. Let’s say that Iksil, with his $360 billion portfolio, wants to make money in the fixed-income markets even as he sees inflation appearing over the next 5 years. It’s never easy for bond investors to make money in the face of inflation, since they’re receiving a fixed income, and that fixed income is effectively being eroded by inflation. And with rates as low as they are today, investors aren’t being paid for the inflation risk they’re taking.

Most normal investors are faced with a choice: they can either get insanely low yields on TIPS, and protect themselves from inflation, or else they can get slightly higher yields on corporate bonds, but leave themselves open to having their money eroded by inflation.

Iksil, however, might have found a way of managing to have his cake and eat it. He buys TIPS — say the 10-year series issued in January 2007, which matures in January 2017. Because those TIPS are now off the run, he gets a slightly higher yield on them.

At the same time, Iksil wants exposure to investment-grade corporate credit risk. If you or I had put our money in TIPS, we couldn’t do that, because, well, our money would be tied up in TIPS. But Iksil works for JP Morgan, so he can get credit risk without having to tie up any money at all. All he needs to do is sell protection on a CDX index which matures at roughly the same time — in this case, Series 9 of the Markit CDX North America Investment Grade Index, which matures in September 2017.

Now the great thing about selling protection, rather than buying bonds, is that it costs you nothing up-front. Quite the opposite, in fact: you get paid for doing it. Iksil is cashing insurance premiums on a basket of corporate debt every six months, and he can add that cashflow to the much more modest cashflow he’s getting on his TIPS. And because he’s JP Morgan, even if the market moves against him, he’s unlikely to have to put up much if any margin.

Put the TIPS and the CDX trade together into a package, and you get what Carney calls CHIPS, or what Pimco managing director Mihir Worah cals CIPS: Corporate Inflation-Linked Securities. (Yeah, I know, that looks more like CILS to me.)

What happens now if inflation picks up before 2017? For one thing, Iksil will make money on his TIPS, which go up in value when inflation rises. But Iksil will also make money on his CDX trade. The yield on a corporate bond is basically made up of two elements, called credit and rates. The rates part is the bit which goes up when inflation appears. But when you’re selling credit protection, you’re stripping out the rates part, and you’re exposing yourself only to the credit part of the equation.

And when inflation appears, corporate credit risk actually goes down, not up. Inflation is bad for lenders; it’s good for borrowers. And it means, generally speaking, that companies are raising their prices and bringing in more money, in nominal terms. Which means they have a higher income with which to pay off their fixed debts. Which means that they’re more likely to be able to pay those debts off in full.

Indeed, if you look at the rate sensitivity of the index that Iksil is buying, his mark-to-market P&L goes up when rates go up. For every basis point that rates rise, Iksil makes a profit, if he has $100 billion of exposure, of roughly $650,000. If yields go up by one percentage point, Iksil has made himself $65 million, just on the CDX part of the trade. Which is quite an achievement for a fixed-income investor in a rising-rates environment. Add in the profit on his TIPS, and he’s making even more.

It’s a big and risky trade — but it’s not one which he’s ever necessarily going to have to unwind. It has a maturity of about 5 years, and JP Morgan is more than big enough to hold a 5-year trade to maturity.

But is that really what he’s doing? There’s one very good reason to believe it’s much more complicated than I’ve laid out: if you look at those TIPS maturing in 2017, there were only $9 billion of them issued in total. And more generally, what Iksil is doing here is basically replicating the kind of corporate credit exposure that JP Morgan has lots of already. This isn’t in any way a hedge of JP Morgan’s existing portfolio; it’s more of a doubling-down on it.

Still, looked at one way, Iksil’s job is to take the assets that JP Morgan hasn’t been able to loan out, and get the kind of return on those assets that JP Morgan would be seeing if it had been able to loan them out. So maybe it makes sense that he’s making a big bet on corporate credit. I just wonder where on earth he could possibly find $100 billion of inflation protection.