Showing posts with label correlation. Show all posts
Showing posts with label correlation. Show all posts

Tuesday, June 16, 2009

The facticity of the “public fact” of market price thus rests on the ability of market markers to fund themselves.

TO BE NOTED:

"socializing finance

Anush Kapadia just posted a fascinating reaction to Donald MacKenzie’s review of Gillian Tett’s book on the credit crisis. I thought that the elaborate comment, buried under eleven previous comments, deserved a lot more prominence. Here it is:

Tett’s is the best account I have seen of the crisis, but she leaves us asking the question: why did super-senior tranches of CDOs and synthetics prove to be the achilles heel of the system? She gives us a few clues, and MacKenzie picks up on one of them, correlation, in his review. While adequately measuring correlation was of course critical, hiking the level of correlation would still fail to account for the magnitude of the disaster. Leaving dollars on the sidewalk, both MacKenzie and Tett provide the materials for a more satisfying answer without putting these ingredients together. Perry Mehrling, ironically cited as an historian of economics by MacKenzie rather than the historically-informed monetary economist he is, has.

The key ingredient for a fuller story is an account of liquidity; it is here that MacKenzie always seems to fall short, (Mehrling has a liquidity-driven account of LTCM as well). The term is of course notoriously difficult to define uniquely, but its frequent pairing with the word “deep” ought to tell us something. Liquidity suggests robustness: a market is liquid if one can buy and sell in significant amounts without affecting the price. But who does one deal with? Typically, a market maker offering a spread. It is thus the ecology of market makers that ultimately determines the depth and resilience of a given market, offering to deal in any amount at their stated prices. The robustness of a market thereby reduces to the robustness of the balance sheets of the key market makers therein. The facticity of the “public fact” of market price thus rests on the ability of market markers to fund themselves. Once these balance sheets start to look anaemic, the prices built on them start to look less reliable.

So it was with the key “public fact” that both Tett and MacKenzie point to: the CDS indices. Oddly, MacKenzie does not think it fit to bring up the key role of the indices that he himself eloquently pointed to in his “End-of-the-World Trade” in his review of Tett. This is unfortunate as Tett is quite categorical about their centrality, and indeed her interpretation differs significantly from MacKenzie’s. For Tett, as for Mehrling, the important thing about the indices was that, being more standardised, their market was more liquid and therefore their prices more reliable. Robust pricing in the liquid derivative ABX index would then enable traders to price the illiquid underlying CDO tranches. Tett:

“Trading in mortgage bonds, let alone mortgage derivatives, was sparse. The only obvious guide was the ABX index, which had been launched in 2006. It provided a gauge of the value of the range of bonds in the CDOs—from BBB to AAA. So what many funds—including Bear Stearns—did was to look at the prices as given by ABS and then use that to deduce the prices of the bonds in their own CDOs,” (pg. 171).

MacKenzie notes this relationship in EOTWT, but for him it is not a matter of trading providing liquidity to a price point but trading providing solidity to the facticity of correlation:

“…trading of index tranches made correlation into something apparently observable and even tradeable. The Gaussian copula or similar model can be applied ‘backwards’ to work out the level of correlation implied by the cost of protection on a tranche, which again is publicly known.”

Correlation is critical to MacKenzie’s story because it goes into the manufacturing of that archetypical public fact, ratings. This is true in both EOTWT and the Tett review. In the latter, he notes that “Essential to the [CDO] assembly line was that the higher tranches of its final products…be able to gain Aaa ratings. A critical issue was the likely correlation of mortgage-backed securities.” This focus on correlation over and above the trading architecture leads MacKenzie to interpret Tett too narrowly, in my view, and thereby to miss a critical functional feature of a public fact that he has himself sniffed out as vital. Of course, Tett leaves herself open to this interpretation because she does not fully spell out the criticality of the CDS indices and their consequent impact on the super-senior tranche prices.

MacKenzie suggests that Tett is praising the Morgan credit derivative inventors for noting an empirical fact, that mortgage default correlations were simply unobservable and therefore the risk in betting on them could not be prudently measured. In the absence of further explication, Tett does indeed give this impression, and MacKenzie’s own commitment to correlation pushes him further in this direction, (although it is indeed strange that, in light of his own observation that CDS indices gave these correlations public facitcity, he does not think the indices warrant a mention in his Tett review). But Tett’s observation that is it trading and therefore liquidity in the CDS indices that enabled the pricing of the underlying CDOs leads us in another direction: not to facticity from modelling and ratings but facticity from trading.

To be fair, MacKenzie does note the importance of trading, but it appears to him merely as a “fact-generating mechanism” by way of marking-to-market. Thus he notes towards the end of EOTWT that:

“It has become common to use a set of credit indices, the ABX-HE (Asset Backed, Home Equity), as a proxy for the underlying mortgage market, which is now too illiquid for prices in it to be credible. However, the ABX-HE is itself affected by the processes that have undermined the robustness of the apparent facts produced by other sectors of the index market; in particular, the large demand for protection and reduced supply of it may mean the indices have often painted too uniformly dire a picture of the prospects for mortgage-backed As Carruthers and Stinchcombe note, market liquidity depends on facts. However, today’s financial facts depend on liquidity. The credit markets remain stuck in a vicious circle.”

But if liquidity is so critical why, despite Tett’s corroborating suggestion, does MacKenzie provide no account of it as generative of facticity in addition to the other way round? Tett herself elides the matter.

Liquidity is the missing piece of the puzzle that enables us to understand Tett’s main point regarding the impairment of the super-senior tranches of CDOs. Simply put, these “safer than safe” tranches were so badly hit not merely because all of Wall St. neglected the extent of the correlation of the underlying mortgages but because the markets that priced these tranches had no market maker of last resort. No emergency market-maker, no liquidity, no rational pricing.

When the banks need liquidity, they go to the interbank market and borrow/lend at LIBOR. When they all run out, they go to the central bank’s discount window. As Tett points out, shadow banks had only one liquidity backstop: the absolutely vital “liquidity puts” with the banks themselves, (MacKenzie makes no mention of them at all. See Tett pg. 205-6). Insurance sellers on the ABX were also providing a kind of backstop, and those backing up AAA risks were in effect backing up systemic risk, really the only kind of risk that is expressed in that coveted rating. By making AAA insurance contracts liquid, insurance market makers were implicitly acting as systemic risk providers. Cheap liquidity led them to underprice systemic risk and help create an unsustainable credit boom. When this became clear and everyone ran for the doors, there was no market maker of last resort who the system as a whole could turn to. The system itself melted because the systemic watchdogs were private, profit-driven entities (AIG and the monolines) who, when it comes to systemic risk, are by definition under-capitalized. With the backstops blown out, even the safer-than-safe risks looked unsafe.

This answers the question MacKenzie poses at the end of EOTWT: “Why…have people not been selling end-of-the world insurance when the returns from doing so have jumped ten-fold while the risk of having to pay out remains small?” As noted above, he cites mark-to-market as the paradoxical answer: that fact-generator now blocks the reestablishment of the pubic fact because of…lack of liquidity! This is not paradoxical but circular: what is the difference between mark-to-market as fact generator and fact inhibitor? More generally, when do positive feedback loops turn into negative ones, and why?

A more coherent answer, unavailable in MacKenzie’s vocabulary, is that, in the absence of a liquidity backstop to the insurance market, traders did (do?) not have a sense that the outcomes are bounded in any way. When we are talking about system risk insurance, the only entity capable of providing this backstop is the state (given its super-sized balance sheet) as it does through its central bank in the interbank market, and even it might prove insufficient. We were missing such an entity in the key CDS index markets, markets that form a structural analogy in the erstwhile shadow banking system to the boring credit markets of its “regulated” parent. Given that such devices are only ever the creation of crises, we ought not to be surprised at their absence. But if we want to get these markets starting again, we ought to be agitating for their construction. This is precisely what Mehrling has been doing.

That MacKenzie came so close to this answer but failed to connect the dots might indicate that SSF has a serious epistemological blind spot. So I would like to end this over-long post by briefly reflecting on what this absence of attention to market structure and credit means for the sociology of finance. MacKenzie has made his name by seeking to break open the black boxes of finance and eschew the Parsonian division of labour between sociology and economics. While he has gone further than anyone else in doing this, he has not gone far enough. This is perhaps understandable given his role as a pioneer, but those who have followed in his wake tend to replicate the error, academic markets being acutely prone to herding. While consistently being drawn to the most interesting and critical aspects of modern finance, MacKenzie’s tight focus on particular models and markets has left us without a more general theory of market activity and the pivotal role of the credit markets generally, even when discussing the crisis.

This is ironic, for market making, liquidity, and ultimately credit-money itself are perhaps the most performative aspects of our modern economy. Yet because their performativity has macro-structural predicates—ultimately undergirded by a market theory of money—these objects fall outside the purview of SSF. Yet this is precisely where economic sociology might really take on a faltering mainstream economic paradigm. It is not simply that economics is performative. The critical question is, if the economy is a social entity that does not submit to the scientism of modelling, how is macroeconomic control achieved at all, and how does it break down?

In the conclusion to EOTWT, MacKenzie points out that the power of central banking comes ultimately from the state’s power to tax. True, but this power remains platonic as a control device unless there is a social mechanism for its transmission. Since the inception of central banking, this mechanism has been the credit markets. What does it say about SSF that it was silent on these “boring” markets till after this crisis?

(For Perry Mehrling’s account of the crisis, from which this post is drawn, refer to his interventions here:
http://cedar.barnard.columbia.edu/faculty/mehrling/mehrling_credit_crisis.html)"

it was perfectly valid to discuss money in abstract, mathematical, ultra-complex terms, without any reference to tangible human beings

TO BE NOTED:

1
Draft review essay for London Review of Books. Third draft
Safer than Safe
FOOL’S GOLD: HOW UNRESTRAINED GREED CORRUPTED A DREAM,
SHATTERED GLOBAL MARKETS AND UNLEASHED A CATASTROPHE by
Gillian Tett. Little, Brown, 338 pp., £12.99, 30 April, 978 1 4087 0167 6
Donald MacKenzie

Few people’s reputations have been improved by the credit crisis. One is the BBC’s
Robert Peston; another Vince Cable. A third is Gillian Tett, capital markets editor of
the Financial Times. Prior to the crisis, she and her team were the only mainstream
journalists who covered in any detail the then arcane, technical world of ‘credit
derivatives’ (of which more below). Tett saw – however imperfectly – the huge risks
that were accumulating unnoticed within that world, and spoke out about them.
Fool’s Gold begins in a conference room in Nice in spring 2005. Tett admits
that at that point she was baffled by the technical language – ‘Gaussian copula’,
‘attachment point’, ‘delta hedging’ – being spoken by the participants. However,
before joining the FT she had conducted fieldwork in Soviet Tajikistan for a PhD in
social anthropology, and the ethnographer in her re-awoke. The conference reminded
her of a Tajik wedding. Those attending it were forging and refreshing social links,
and celebrating a tacit worldview – in this case, one in which ‘it was perfectly valid to
2
discuss money in abstract, mathematical, ultra-complex terms, without any reference
to tangible human beings’.
Who were the key actors in the ceremony, those up on the conference hall’s
stage? She whispered the question to the man sitting beside her. ‘They used to all
work at J.P. Morgan. … It’s like this Morgan mafia thing. They sort of created the
credit derivatives market.’ The answer surprised her. J.P. Morgan was not Goldman
Sachs: it wasn’t an exciting bank. It bore the name of America’s most celebrated
financier, but it was ‘dull’: safe, boring, perhaps a little snobbish. (When its current
chief executive, the now well-respected Jamie Dimon, joined the bank from Bank
One, which was headquartered in Chicago, Tett reports one Morgan banker muttering
‘Not another retail banker from Hicksville, USA!’)
The core of Tett’s fine book, which is by far the most insightful of the first
wave of books on the crisis, is the story of J.P. Morgan’s credit derivatives team. For
all the bank’s traditionalism – the door staff at its London offices wear uniforms that
would not be out of place outside the Ritz – it was quietly innovative, and its blueblooded
heritage did not block all diversity. One of the team’s driving forces was a
young Englishwoman, Blythe Masters; another, Terri Duhon, makes no secret of her
upbringing in a trailer in Louisiana; central to its technical work was an Indian
mathematician, Krishna Varikooty. Boisterousness that would have horrified John
Pierpont Morgan was tolerated. Tett describes how at one off-site gathering in
Florida, one of the team’s managers broke his nose when he was being pushed into a
hotel swimming pool by drunken colleages.
3
The team’s pivotal innovation was a December 1997 deal they called ‘Bistro’
(Broad Index Secured Trust Offering). For a decade, banks had been experimenting
with credit derivatives, which are ways of separating out the ‘credit risk’ involved in
lending (the risk that borrowers will default on their obligations, failing to make the
required interest payments or not repaying their loans) and making that risk into a
product that can be bought and sold. Bistro helped turn this tentative activity into big
business.
Bistro transferred to external parties the credit risk of loans totalling $9.7
billion that J.P. Morgan had made to 307 companies. The scheme was an influential
version of the CDOs (collateralised debt obligations) that I described in LRB on 8
May 2008. Like other CDOs, Bistro was divided into ‘tranches’, of which originally
there were two. Investors in the lower or ‘junior’ tranche received a healthy rate of
return, 375 basis points over Libor (London interbank offered rate), which is the
average rate at which a panel of leading banks report they can borrow from other
banks. (A basis point is a hundredth of a percentage point.) This compensated the
junior investors for the fact that their investments would bear the initial losses,
beyond a small reserve built up during the deal’s first five years, should any of the
307 borrowers default.
Only if those losses were to exceed the entirety of the investments in the
junior tranche would the holders of Bistro’s senior tranche – which paid only 60 basis
points over Libor – suffer. The loans that made up Bistro were well-diversified across
industries, and predominantly to blue-chip companies, so losses to Bistro’s senior
tranche seemed unlikely enough to Moody’s, one of the three leading credit rating
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agencies (the others are Standard & Poor’s and Fitch), that it awarded the tranche its
highest rating, Aaa.
Aaa was a rare distinction. Only a dozen corporations and less than two dozen
governments were judged worthy of it: neither Italy nor Japan, for example, has an
Aaa rating. (As readers will know, Standard & Poor’s has indicated that the UK is
now also in some danger of losing its top rating.) Blythe Masters had formidable
powers of persuasion, which helped when selling a deal that ‘look[ed] like a science
experiment, with all those arrows’, as one investor quoted by Tett described Bistro’s
documentation. Yet 60 basis points over Libor, for an investment judged safer than
the sovereign bonds of some of the world’s leading economies, was the most
powerful argument of all: an investor would normally struggle to find an Aaa
investment that yielded as much as Libor.
For J.P. Morgan, Bistro solved one problem and potentially addressed a
second. First, while the 307 corporations were low risks, even the most creditworthy
borrowers can default. So $9.7 billion in loans to the 307 corporations was a
significant constraint on the bank’s future lending. Bistro removed that constraint.
Second, the Basel Capital Accord, signed by the world’s leading banking regulators in
1988 and implemented by them in 1992, forced banks to carry reserves equal to 8
percent of their risk-weighted lending. While certain categories of lending – to other
OECD banks, for example – qualified for a reduced reserve requirement, loans to
even the safest industrial corporation incurred the full 8 percent, a figure that bankers
felt was far larger than justified by the risks involved. J.P. Morgan hoped that the
transfer of credit risk achieved by Bistro would persuade regulators to reduce that
5
requirement considerably, and Tett reports that Blythe Masters and her colleague Bill
Demchak pushed the Federal Reserve and the Office of the Comptroller of the
Currency to clarify what exactly would be needed to achieve that.
Bistro differed from earlier CDOs in that it did not, in fact, transfer to external
investors all the credit risk of the $9.7 billion of loans. The junior and senior tranches
amounted in total to only $700 million; the bank believed that the chances of losses
ever exceeding that figure were too tiny for it to be worth paying investors to shoulder
them. The regulators, however, demanded that the bank do something to remove that
residual ‘unfunded risk’ before they would relax the 8 percent capital requirement.
The residual risk was like a topmost tranche, sitting on top of the senior
tranche; it would come into play only if losses wiped out the latter in its entirety. The
senior tranche was Aaa, as safe as it gets; the residual ‘super-senior’ tranche (as the
J.P. Morgan team christened it) was thus safer than safe. To satisfy the regulators,
however, the team turned to the Financial Products division of the leading US insurer,
AIG. Sharing J.P. Morgan’s analysis that the super-senior tranche was ultrasafe, AIG
agreed to insure it against all remaining losses, charging an annual premium of only a
fiftieth of 1 percent of the sum insured. From the viewpoint of AIG Financial
Products, it was small-scale business but apparently highly profitable: by covering an
effectively non-existent risk, the firm earned $1.8 million a year.
In that little afterthought to Bistro – what to do with the super-senior tranche –
lay the germ of much of the credit crisis, especially of its disastrous effects on many
of the world’s leading banks. Bistro-like deals started in the world of corporate
6
borrowing, but from 1999 onwards began also to be implemented in the world of
consumer debt, especially mortgages. There was actually longer experience of
packaging mortgages into securities than of packaging corporate debt into CDOs, and
mortgage-backed securities had acquired an admirable reputation for safety.
Mortgage-backed securities have a structure like that of CDOs, with different tranches
carrying various levels of exposure to risk. The safest, Aaa, tranches of those
securities had impeccably default-free records, and even the riskier tranches had
performed well: indeed, on average generally better than corporate bonds with the
same ratings. It wasn’t that people never defaulted on their mortgages – they did –
but the securities were designed to take this into account, for example by building up
reserve funds (analogous to but usually proportionately larger than Bistro’s small
reserve) that would absorb the anticipated losses. For many years, such provisions
proved in general fully adequate.
What happened from 1999 on was that mortgage-backed securities, which
already represented one layer of packaging of debt, then started to be repackaged into
CDOs, thus creating a ‘Russian doll’ product: a tranched, packaged product each of
the components of which was itself a tranche of a packaged product. Given their
excellent reputation, putting mortgage-backed securities rather than corporate bonds
or loans inside CDOs might seem a small step. Yet when in 1999 Bayerische
Landesbank, which had become involved in the US mortgage market, approached J.P.
Morgan to package $14 billion of bundles of mortgages and other forms of
predominantly consumer debt into a Bistro structure, there were initially serious
doubts within the Morgan team.
7
The problematic issue was correlation( NB DON ), which is at the core of evaluating a
CDO. Low correlation means that defaults are essentially idiosyncratic events, with
the consequence that only the bottommost tranche of a typical CDO is at significant
risk. In contrast, high correlation means that if defaults happen they tend to cluster,
and the clustering of defaults puts investors in the higher, apparently safer, tranches at
risk of loss.
Participants in the emerging credit-derivatives market tended to be confident
that they had a fair grasp of the correlation( NB DON ) of corporate defaults. The rating agencies
had large databases of such defaults from which the extent of clustering could be
inferred at least roughly, and other market participants often took the easily measured
level of correlation between the moves of different corporations’ stock prices as a
guide to the correlation of their net asset values. (The link between the latter and
default is that the most important cause of corporate default is bankruptcy, which can
be though of as happening when a corporation’s net asset value falls below zero: that
is, when its liabilities exceed its assets.) Clearly, the correlation of the asset values of
two different corporations was unlikely to be zero, since general economic conditions
will affect both. Nor, however, were corporate asset correlations thought likely to be
1.0, the value that indicates perfect correlation. 0.3 was a commonly-used figure.
That, for example, was the standard level of correlation between the asset values of
firms in the same industry that Standard & Poor’s initially assumed in CDO
Evaluator, the software system it began using in 2001 in the rating of CDOs.
The credit crisis has inured us to gigantic numbers – losses measured in
billions or trillions of dollars – but we need to pay attention to its little numbers as
8
well as its big ones if we’re going to understand it properly. A correlation of 0.3 was
modest. If it was correct it would be highly unlikely that the senior tranche of a CDO
such as Bistro would suffer a loss – unlikely enough to warrant an Aaa rating – and
effectively inconceivable that the supersenior tranche would be hit.
However, the analysis that had initially produced the widely-used figure of
0.3 was of corporate debt. How could one estimate the equivalent correlation for
mortgage-backed securities? Paradoxically, their very safety was a disadvantage in
this respect: there was effectively no record of default that could be scrutinised for
traces of clustering. Nor did such securities trade often enough for the correlation of
their prices to be measured: most investors in them simply held them until they
matured. Intuitively, though, it seemed conceivable that defaults in bundles of
mortgages or other forms of consumer debt could be quite highly correlated, because
of the likely role played by matters such as the overall unemployment level, and that
could make a CDO based upon mortgage-backed securities an unduly risky product.
In an interview I conducted with her, Terri Duhon, who led the Bayerische
Landesbank mortgage-backed CDO, told me that this caused some of her J.P. Morgan
colleagues initially to doubt whether the deal should proceed: they argued that ‘there
is no way that we should be doing this because it’s way too correlated’. Tett reports
that Krishna Varikooty, for example, was concerned by a correlation risk that seemed
to him to be unquantifiable. Intensive discussion and analysis, and very conservative
structuring of the deal eventually led to agreement that it was safe to go ahead (it
helped that unlike in many more recent deals the ratings of the underlying assets were
high – around 95 percent had Aaa ratings – and it contained no securities based on
9
subprime mortgages). Yet the reservations remained, and J.P. Morgan was only ever
to construct one further large CDO, and a limited number of smaller ones, in which
the underlying assets were bundles of mortgages.
In consequence, the bank remained on the sidelines as the previously largely
distinct worlds of CDOs and of mortgage-backed securities became increasingly
linked from 2002 on. It was an encounter of two subtly different cultures, with for
example quite different mathematical approaches. (Understandably, Tett, the former
anthropologist, limits the more ethnographic aspect of her analysis to only on one of
those cultures, that surrounding CDOs.) The CDO world developed explicit and
increasingly elaborate models of correlation – the ‘Gaussian copula’ that initially
puzzled Tett is a correlation model – while the mortgage world handled the
phenomenon entirely implicitly. In most investment banks, and also – as far as I have
been able to discover – in the New York head offices of the rating agencies, separate
groups or departments handled mortgage-backed securities and CDOs based on
corporate debt. In the investment banks, for instance, those different departments
seem to have had surprisingly little to do with each other. The two cultures never
really merged; instead, the CDO, a structure invented by the corporate-debt world,
was applied to the products of the mortgage world.
Members of both cultures now see the encounter as corrupting. ‘They’ –
constructors of CDOs based on mortgage-backed securities – ‘took our tools’ and
misused them, one specialist in corporate credit derivatives told me a few weeks ago.
Those with a background in mortgage-backed securities blame CDOs (with some
justice) for being indiscriminate buyers of those securities, concerned only with their
10
ratings and the spreads (increments over Libor) they offered. Two experienced
industry observers, Mark Adelson and David Jacob,1 suggest that the fatal point was
when CDOs became the almost the only purchasers of the riskier tranches of
mortgage-backed securities. Previously, those tranches were either guaranteed
against default by specialist insurers, or bought by canny investors with their firms’
own money at risk, who would carefully assess the risks involved. These insurers and
investors acted as a brake on the riskiness of the lower tranches, and thus on the
overall riskiness of mortgage-backed securities, and they demanded a healthy rate of
return for taking on their risks. They were displaced by those buying tranches in
order to package them into CDOs, who were prepared to buy them at lesser rates of
return, and who cared a lot less about their riskiness, because those risks were going
to be passed on to the investors in the CDOs.
With the brake removed, the construction of CDOs based on mortgage-backed
securities became a fast-moving assembly line (participants frequently turn to
machinic metaphors when describing the process). Brokers sold mortgages knowing
that they could readily be sold on in the form of mortgage-backed securities. Instead
of having to worry whether the couples sitting on the other side of their desks really
had the wherewithal to keep up their payments, all that mattered was the dozen or so
quantitative characteristics – such as borrowers’ FICO (Fair Isaac Corporation)
creditworthiness scores – that influenced rating agencies’ mortgage models. The
constructors of mortgage-backed securities no longer had to satisfy specialist insurers
or experienced investors: CDOs had an apparently insatiable demand for those
securities.
1 Their papers can be found at http://www.adelsonandjacob.com/
11
Essential to the assembly line was that the higher tranches of its final products
– CDOs in which the underlying assets were mortgage-backed securities – be able to
gain Aaa ratings. A critical issue was the likely correlation of mortgage-backed
securities. Standard & Poor’s, for example, used the same system, CDO Evaluator,
that it employed for CDOs based on corporate debt, and it employed the same modest
baseline correlation assumption, 0.3, for mortgage-backed securities that it initially
used for corporations within the same industry. (S&P would later reduce this last
figure, while increasing its assumption about cross-industry correlation. These
baseline correlation figures could be increased by the analysts rating a specific CDO
if it was highly concentrated in a particular industry or consumer-debt sector.) I
haven’t been able to ascertain the equivalent figures used by the other agencies,
whose methods differed somewhat from Standard & Poor’s, but the similarity of their
ratings to S&P’s suggest similar judgements. My focus is on S&P here simply
because – commendably – it seems to have been more explicit than the other agencies
in laying out in CDO Evaluator’s publicly-available documentation these crucial
assumptions underpinning how the system worked.
The choice of 0.3, or a number close to it, as the baseline was critical: one
specialist has told me that even a moderate increase in the baseline correlation
assumption, for example to 0.5, would have made many CDOs based on mortgagebacked
securities much less attractive, perhaps even not economically viable.
However, as far as I can discover, analysing CDOs built out of mortgage-backed
securities using only modest correlation levels seems in general to have been
uncontroversial. Certainly, the performance of mortgage-backed securities – which,
12
as noted above, had in general been better than that of corporate bonds – offered little
reason to be more stringent when rating CDOs based on them. For example, S&P’s
statistical analyses suggested a correlation of mortgage-backed securities lower than
0.3; the latter figure was retained as a baseline because it was understood that the
correlation would rise when economic conditions became less benign.
Had the world remained as it was in 2002, the agencies’ assumptions and
ratings might well have turned out to be perfectly appropriate. The trouble with an
assembly line, though, is that it produces identical products. The only person outside
of J.P. Morgan I’ve so far found who thought, at the time, that the correlation
estimates being used to analyse CDOs of mortgage-backed securities were much too
low had discovered this by accident. In a previous job as an auditor, he was checking
the statistical tables that the sellers of mortgage-backed securities provide to
prospective buyers. These tables show the breakdown of the underlying loans by
state, FICO score, loan-to-value ratio, and so on. When checking the tables for one
security, he inadvertently used the loan tape (the underlying mortgage data) for
another, and found almost complete agreement. ‘These deals’ – apparently different
mortgage-backed securities – ‘were the same deal’, he told me. Even geographical
dispersion of the underlying mortgages across the US (a desirable feature when an
individual mortgage-backed security was considered in isolation, because it reduced
exposure to the vagaries of particular local housing market) had the paradoxical effect
of increasingly the homogeneity of different mortgage-backed securities. In a
situation of severe economic stress – falling house prices, rising unemployment – not
just some of those securities would perform badly: they all would. Instead of
correlation remaining modest, my interviewee came to fear that it would be nigh on
13
perfect.
Specialists in mortgage-backed securities in the US have not been entirely
surprised at the fraud and malpractice in mortgage lending that has come to light: it
was always present, and changed only in scale( NB DON ). (There had been an earlier US
subprime crisis in the late 1990s, which only specialists seem to remember.2 It was
much more limited in its scale, but it revealed extensive over-optimistic accounting by
lenders.) That mortgage defaults have risen, and the value of repossessed homes
fallen, is not in itself surprising to specialists, although the size of the changes
certainly is. At least some of them did begin to suspect that longstanding statistical
relationships – for example between individuals’ credit scores and the risk of them
defaulting on their mortgages – had ceased to be valid, but as far as I can tell that
suspicion arose only in 2006, by which time the processes that led to the credit crisis
were well underway. One problem, for instance, seems to have been that with
individuals’ scores increasingly determining their access to credit and the rates of
interest they had to pay, they found ways to manipulate those scores. A modest webbased
industry developed which arranged (in return for fees of around $1,000-$2,000
per person) for people – in some cases, apparently dozens of people – with low credit
scores to be added as ‘authorised users’ to the credit card account of someone with a
high score and an impeccable payment record. Within one to three months, the
benefits of the primary cardholder’s regular payments fed through into improvements
in the credit scores of the card’s ‘renters’.
If, however, CDOs backed by mortgages had worked as the J.P. Morgan team
2 It is discussed in the final chapter of an excellent book that, while more limited in scope and more
technical than Tett’s, deserves to be better known: Laurie S. Goodman et al., Subprime Mortgage
Credit Derivatives (Wiley 2008, $80.00, 978-0-470-24366-4).
14
had envisaged when designing Bistro, the losses to investors in those CDOs that the
US housing bubble and its collapse have caused, though very large, would have been
spread widely across the many institutions that bought the tranches of such CDOs. As
Tett notes, what has shocked the members of that team – many of whom now work
for other banks and hedge funds, but still stay in touch – is the concentration of such
losses, especially at apparently sophisticated global banks such as Bear Stearns,
Lehman Brothers, UBS, Citigroup, Merrill Lynch, Morgan Stanley and the Royal
Bank of Scotland.
The primary vehicle by which risk was concentrated was Bistro’s afterthought,
the super-senior tranches of CDOs. Even the riskiest mortgage-backed CDOs – those
that predominantly bought the ‘mezzanine’ (next-to-lowest) tranches of mortgagebacked
securities – have super-senior tranches that are bigger than all the other
tranches put together. These super-senior tranches were hard to sell to most outside
investors, because the need for attractive returns on lower tranches means a supersenior
tranche can offer only a slender increment over Libor. By 2005, Tett reports,
that spread was as low as 15 basis points.
So many banks did as J.P. Morgan did with Bistro: they kept the super-senior
tranches, sometimes insuring them via AIG or the specialist bond insurers. (Adelson
and Jacob point out the resultant irony. Risks that the mortgage experts in the
insurers would have charged heavily for or perhaps even declined were insured in
packaged form in huge amounts – and quite cheaply – by different departments of the
same firms.) If only a handful of deals had been insured in this way, it would have
made perfect sense. As Tett notes, however, AIG insured super-senior tranches
15
totalling $560 billion. Its bail-out by the US taxpayer dwarfs that of any bank, and as
John Lanchester wrote in the LRB on 28 May, it keeps rising (the current total is $173
billion), but AIG cannot be allowed to fail, because the loss of these crucial supersenior
insurance contracts could bring much of the banking system down with it.
Perhaps most surprising of all, top banks also bought super-senior tranches
originated by other banks. If you are a top bank, you can borrow at around Libor
(that, after all, is what Libor means); if you are particularly well regarded, it may be
possible to borrow at a rate a tiny bit lower than Libor. So you could borrow at Libor
or below, buy a tranche that seemed safer than safe, and from it earn a slender spread
over Libor. It looked like free money. It was especially tempting to traders whose
banks ‘charged’ them for their use of capital, in the systems by which traders’ P&L
(profit and loss) is measured, at around Libor, and credited them with the small
additional spread that super-senior tranches offered. The slenderness of the spread
meant that you had to do the trade on a very large scale to earn a really big bonus, so
traders did just that.
As I’ve already indicated, the vulnerability of super-senior is correlation.
Losses on uncorrelated assets are unlikely ever to impact on super-senior tranches.
When correlation approaches 1.0, however, a CDO’s asset pool starts to behave like a
single investment. It may suffer no defaults, or it may default effectively in its
entirety. If the latter happens, even the super-senior tranche, safer than safe, is
doomed.
As the US historian of economics Perry Mehrling points out, events in
16
financial markets cast their shadows ahead of them, not behind. What has haunted the
banking system for the last two years is above all the shadow of the gigantic, systemwide
default of the super-senior tranches of all the CDOs based on those US
mortgage-backed securities issued towards the end of the bubble( NB DON ). (Residential
mortgages have been the focus of most of the attention, but there are also lots of
problems with commercial mortgages.) Although, alas, the losses will not stop there,
most immediately at risk have been CDOs made up primarily of the mezzanine
tranches of subprime mortgage-backed securities issued from late 2005 on. Defaults
have risen enough, the value of repossessed homes has fallen enough, and the
structure and composition of these securities has been similar enough, that as far as I
can tell almost all such tranches have been or will be wiped out in their entirety. So if
a CDO contains little else but such tranches, even its super-senior portion faces closeto-
total losses. So far, only a limited part of those losses have actually been realised,
but the banking system is braced for the rest of them – and, with the massive aid of
taxpayers, it is hopefully now well enough capitalised to survive it and the other
losses that sharp recession will bring.
Unfortunately, this analysis – that the crux of the problem has been not in
CDOs per se but in the uncomfortable encounter between the world of CDOs and that
of mortgage-backed securities – remains only a hypothesis. The world of corporate
CDOs has itself manifested some of the phenomena of the mortgage CDO assembly
line: increasingly risky loans were made to private equity firms and to other highlyindebted
corporate borrowers because it was possible to package and sell on those
loans in the form of CDOs. I’ve just come back from New York, where I questioned
some of those I spoke to on the magnitude of the problems that may lurk below the
17
still comparatively quiet surface of this other sector of the CDO market, which, while
not as large as as the mortgage sector, is still huge. My interviewees seem convinced
that while the problems are real they do not approach the same scale: the amount of
truly irresponsible lending to corporations was much smaller. I hope they are right.
At its heart, the tale Tett tells is a moral one. She believes that the history of
the J.P. Morgan credit derivatives team shows that banking can be technically
innovative while remaining responsible( NB DON ). Her readers may fear that the anthropologist
has here simply gone native, but I don’t think so. I have met a good number of those
she is writing about, and have studied many of the events she has, and I largely share
her judgement. In particular, J.P. Morgan’s decision not to set up a mortgage CDO
assembly line (despite Dimon at one point wanting one) has meant that the bank has
not suffered the catastrophic losses that so many of its peers have; unlike theirs, its
solvency has never been in doubt. It is too easy right now to condemn all of those
who work at the heart of the financial system as either rogues or fools: for example,
Tett reveals that Blythe Masters, who stands out because even today female senior
bankers are relatively rare, gets hate mail. So Tett is right to emphasise that despite
all the pressures and all the temptations, prudent banking was still practised ( NB DON ) –
sometimes – even at the centre of history’s largest-ever credit bubble.
9 June"

Thursday, June 4, 2009

The optimist in Wilmott thinks that people realize these models don't work. But he's not really an optimist.

TO BE NOTED: From Newsweek Via Marginal Revolution:

Paul Wilmott is out to save Wall Street's soul—one dork at a time.

Matthew Philips
NEWSWEEK
From the magazine issue dated Jun 8, 2009

Jude Edginton / Redux for Newsweek

Imagine an aeronautics engineer designing a state-of-the-art jumbo jet. In order for it to fly, the engineer has to rely on the same aerodynamics equation devised by physicists 150 years ago, which is based on Newton's second law of motion: force equals mass times acceleration. Problem is, the engineer can't reconcile his elegant design with the equation. The plane has too much mass and not enough force. But rather than tweak the design to fit the equation, imagine if the engineer does the opposite, and tweaks the equation to fit the design. The plane still looks awesome, and on paper, it flies. The engineer gets paid, the plane gets built, and soon thousands just like it are packed full of people and sent out onto runways. They fly for a while, but eventually, because of that fatal tweak, they all end up crashing.

In a way, this is what's happened in quantitative finance. The planes are the complex derivatives—like collateralized debt obligations—that now lie smoldering on the balance sheets of banks. The engineers are the "quants": those math and science Ph.D.s who flocked to Wall Street over the past decade and used mathematical models to build these new investment products. These are the people Warren Buffett was talking about when he said, "Beware of geeks bearing formulas" in his letter to shareholders this year. The quants aren't entirely to blame for the financial meltdown; there's plenty of guilt to be shared by regulators, top executives and the investors who bought the instruments the quants created. Yet while aeronautical engineers who willfully designed a faulty plane might be on trial for criminal negligence, Wall Street's math gurus are, for the most part, still employed. Strangely, the banks need quants more than ever right now. If anyone's going to figure out how to price these toxic assets, it's them. Quantitative finance isn't going away, but it is in desperate need of reform. And one man—a math geek himself—thinks he knows where to start.

Paul Wilmott is a 49-year-old Oxford-trained mathematician and arguably the most influential quant today, the brightest star in their insular, nerdy universe. The Financial Times calls him a "cult derivatives lecturer." Nassim Taleb, the mathematician and author of the bestseller The Black Swan, calls him the smartest quant in the world. "He's the only one who truly understands what's going on ... the only quant who uses his own head and has any sense of ethics," says Taleb. Wilmott stands atop a veritable quant empire. His wonk-made-simple textbooks sell for hundreds of dollars. A subscription to his bimonthly magazine, Wilmott, costs $695 a year. His Web site, Wilmott.com, is the nerve center of the quant community, with 65,000 registered users and a chat forum that buzzes over such topics as geodesic stochastic manifolds and swaption vol arbitrage.

Over the past decade, and increasingly since the crash, Wilmott has cultivated a loyal following of truth-seeking converts from the failed school of thought that the entire world can be turned into Greek symbols, plugged into equations, priced and predicted. He's especially critical of the notion that math can forecast human behavior, essentially the basis of finance. "This," says Wilmott, "is absolute rubbish." To rectify the situation, he's started his own training program, the Certificate in Quantitative Finance, or CQF. It's the gem of his empire, the key, he hopes, to saving the quants from themselves, not to mention all of us from their future destruction. In essence, the course is rehab for quants, a six-month, $18,000 program designed to break them of the abstract, theoretical approach to finance they learned in their Ph.D. or financial-engineering programs, and replace it with a more practical set of skills that are actually used on Wall Street. "What banks really need are quants who can translate theoretical math and tell them how it applies," says Wilmott. "Because what good is being fluent in geek if you can't apply it? You might as well stay in university."

Ever since Ed Thorp, the first true quant, left his job teaching math at UC Irvine to start a hedge fund in the 1970s, math and science types have been setting out from academia to try their hand at finance. The watershed came in the mid-1970s when MIT-trained economist Robert Merton along with Myron Scholes, a University of Chicago economist, and Fischer Black of Harvard developed the Black-Scholes equation for pricing options, which eventually garnered a Nobel Prize. Over the past 20 years, quantitative methods gradually spread into commercial and investment banks, fueling a huge demand for math savants. Since the mid-1990s, dozens of master's-degree programs in financial engineering have sprouted up at top universities. (The highest-rated ones are at Carnegie Mellon, Columbia, Stanford and Princeton.) Along with physics Ph.D. programs, these are the primary breeding grounds for the many thousands of quants who have found their way to Wall Street. It's these programs that Wilmott has taken direct aim at with his CQF. "I'm building a new army of quants," he says. His ranks currently stand at 1,273: the number of CQF alumni who have graduated since the program was founded in 2003. Wilmott realizes he's vastly outnumbered, but he's undaunted. "Ever see the movie Zulu?" he asks, referring to the 1964 Michael Caine film about a battalion of 140 British soldiers besieged by 4,000 African warriors in 1879. The Brits won, mostly because they were better-trained and -equipped, just as Wilmott wants his graduates to be.

The CQF is taught at night in London's financial district by Wilmott and a handful of his buddies, most of whom have worked in finance. These instructors are miles away from the stereotypical math or finance professors. Among them is Espen Haug, a Norwegian former JPMorgan options trader who looks like a cross between James Bond and Arnold Schwarzenegger, and often lectures in sunglasses and a tuxedo. One evening this spring, Wilmott is wrapping up the final class on interest rates and fixed income. He stands at a podium, looking out at about 30 students, most of whom have just gotten off work from their jobs at banks: Citigroup, Deutsche Bank, Credit Suisse. Another 100 or so watch the lecture streaming over the Internet. The official topic of the evening is how to decompose the random movements of the forward rate curve into its principal components—i.e., how to predict random fluctuations in interest rates in order to determine bond prices. "There's nothing difficult here," Wilmott says without irony. "If you can keep your head, you'll be fine."

He's just scribbled a handful of equations on a whiteboard, including one called the Heath-Jarrow-Morton model. Developed in the late 1980s, the formula looks horrifically complicated to the layman. But to a mathematician it's elegant, simple—and dangerous. Behind its simplicity lie hidden mistakes, unobservable variables like volatility and correlation that can provide a false sense of security. "With models, you want to see where things go wrong," says Wilmott. "You want to see the dirt. But HJM is actually just a big rug for [mistakes] to be swept under." For the next half hour, Wilmott deconstructs the thing, cautioning students on overreliance. "In the end, we should all like models that wear their faults on their sleeves," he tells the class.

Two hours later, Wilmott is treating his students to beers at a nearby pub. Among them are half a dozen fresh-faced Citigroup employees, all in their mid-20s, all recent physics or engineering Ph.D. graduates, and all being sponsored by Citigroup to take the CQF. (Banks have only recently started sponsoring students to take Wilmott's classes, a tacit acknowledgment that quants need more holistic training.) "There's an arms race going on in quantitative finance," says one of the students. "The CQF gives us another weapon in our arsenal."

On a typical April weekday, Wilmott spends the morning in his office, a small, gray room that's completely nondescript save for the giant blackboard filled with mathematical notation. There he goes over the final touches of an options formula he's getting ready to publish with a former student. He then takes the tube across town to London's posh Grosvenor Square to meet with three former investment bankers who want to hire him as a quant on call for their consultancy startup. They pitch Wilmott while sitting in the modern dining room of Gordon Ramsay's Maze Grill restaurant. Wilmott tells them he'll think about it, then sets off down Oxford Street, a stretch of shops that's swarming with tourists.

Picking his way through, on and off his cell phone, Wilmott talks about the contradictions in his life: he's a car enthusiast who hates to drive; he goes on Swiss ski trips but doesn't ski. Though he's nearly 50, he could pass for 30-something, especially when dressed in skinny jeans, black Chuck Taylors, Gucci eyeglasses and a retro zip-up cardigan. This is his outfit of choice so long as he's not giving a speech somewhere, in which case he might change his shoes and throw on a jacket.

Born in Birkenhead, a small town outside Liverpool, Wilmott studied applied math at Oxford. In his spare time, he dabbled in juggling and competitive ballroom dancing. After earning a Ph.D. in fluid mechanics from Oxford in 1985, he got his start as an applied mathematician, working on jet-engine turbines for Rolls-Royce and calculating detonation sites for an explosives company. In the late 1980s, he started applying math to finance. His first burst of fame came in 1993 when he co-wrote a textbook on derivatives. Soon he'd made a name for himself as a contrarian guru, writing more textbooks, and giving speeches around the world to rooms full of bankers. From 2001 to 2005 he ran a $170 million hedge fund that returned an average of 15 percent a year.

Back on Oxford Street, Wilmott walks by several tube stops, deep in conversation on the topic that gets him most agitated these days: structured credit, the area of finance most at fault in the crash, and where quants inflicted the most damage by applying mathematical models they swore could predict default rates. "A complete lapse of ethics and responsibility," he calls it.

A collateralized debt obligation (CDO) is the most common form of structured credit. Banks build CDOs by putting together a bunch of loans, slicing them into little pieces (tranches) and selling them off to investors. Think of it as disassembling a cow into different cuts of meat—from prime steaks to ground beef—that are priced according to their quality. The first CDO was issued in 1987 by Drexel Burnham Lambert, the same firm that went bust in 1990. After the fall of Drexel, CDOs went away for a while, until the quants came along. In 2000, the CDO market was jump-started by David X. Li, who, while working at JPMorgan, created the Gaussian copula function, a formula for determining the correlation between the default rates of different securities. In theory, the model tells you the odds that, if one CDO goes bad, others will too. The apparent genius of the Gaussian copula is its abstraction. Rather than relying on the immense amount of data used to figure the odds that a CDO might default, Li appeared to have discovered a law of correlation. That is, you didn't need the data; the correlation was just there. Armed with it, quants could price CDOs much faster, and traders could buy and sell them at record speeds. Gaussian was rocket fuel for the CDO market. The global volume of CDO deals went from $157 billion in 2004 to $520 billion in 2006. As more banks got in on the game, the once large profit margins started to shrink. In order for banks to make the same kind of returns, they had to pack more and more loans into a CDO, essentially making bigger bombs. Li was on his way to a Nobel Prize when the world blew up. Wilmott marvels at the carelessness of it all. "They built these things on false assumptions without testing them, and stuffed them full of trillions of dollars. How could anyone have thought that was a good idea?"

To Wilmott, Gaussian is an example of how dangerously abstract quant finance has become. "We need to get back to testing models rather than revering them," he says. "That's hard work, but this idea that there are these great principles governing finance and that correlations can just be plucked out of the air is totally false." Wilmott spends a lot of time with another former student trying to tackle the biggest problem facing quant finance right now: how to price all those CDOs sitting on the balance sheets of banks, the toxic assets we hear so much about. "We don't have the tools yet to truly price them," Wilmott says. "People thought we did, but they were nowhere near robust enough."

The optimist in Wilmott thinks that people realize these models don't work. But he's not really an optimist. "What I think is going to happen is that people will forget and we'll just keep going on the way we have been with nothing really changing," he says. Wilmott is encouraged by President Obama's proposals to tighten regulation of derivatives; he thinks it'll keep quants on a shorter leash. But he's also stunned by the lack of outrage over the financial mess. The violence that erupted at this year's G20 summit wasn't anywhere near what he thought it should've been. "Where the hell was everybody? If people aren't angry now, they'll never be."

Wilmott realizes he's fighting a losing battle, and that changing finance will take a lot more than a few thousand better-prepared quants. As long as banks get paid in the first year for selling a CDO that doesn't mature for 30 years, little will change. Still, he does sense a tidal shift. "I've been helped by recent events, but I don't really take solace in that. I'm not gonna say I told you so."

For now, Wilmott will go about the hard work of retraining his merry band of quant soldiers, hoping his attempts at remedial education can help minimize the odds of a future derivatives-fueled melt-down. Waiting for traffic in the shadow of St. Paul's Cathedral, having walked the three miles from Grosvenor Square, Wilmott realizes he's late for the CQF class. "My God, I'm lecturing in 10 minutes!" For investors who hope to be protected from these "financial weapons of mass destruction," it's not a moment too soon.

URL: http://www.newsweek.com/id/200015"

Friday, May 22, 2009

The fact was, however, that the assumption about correlation was just that: guesswork

TO BE NOTED:

"Information Processing

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Steve Hsu
Professor of physics at the University of Oregon. Homepage. Archive (list of posts, by date and category).
View my complete profile

Thursday, May 21, 2009

Gillian Tett at LSE



Highly recommended: FT journalist Gillian Tett, a PhD in social anthropology, discusses her book on the financial crisis: Fool's Gold, at an LSE public lecture.

I haven't read the book yet, but it's on my list :-) Here are two nice excerpts that appeared in the FT. She does a great job of covering the birth and development of credit derivatives, CDOs, etc.

Genesis of the debt crisis

How panic gripped the world's biggest banks

Below is a discussion of correlation from the first excerpt.

The problem with correlation

Demchak was acutely aware that modelling the risks involved in credit derivatives deals had its limits. One of the trickiest problems revolved around the issue of “correlation”, or the degree to which defaults in any given pool of loans might be interconnected. Trying to predict correlation is a little like working out how many apples in a bag might go rotten. If you watch what happens to hundreds of different disconnected apples over several weeks, you might guess the chance that one apple might go rotten – or not. But what if they are sitting in a bag together? If one apple goes mouldy, will that make the others rot too? If so, how many and how fast?

Similar doubts dogged the corporate world. JP Morgan statisticians knew that company debt defaults are connected. If a car company goes into default, its suppliers may go bust, too. Conversely, if a big retailer collapses, other retail groups may benefit. Correlations could go both ways, and working out how they might develop among any basket of companies is fiendishly complex. So what the statisticians did, essentially, was to study past correlations in corporate default and equity prices and program their models to assume the same pattern in the present. This assumption wasn’t deemed particularly risky, as corporate defaults were rare, at least in the pool of companies that JP Morgan was dealing with. When Moody’s had done its own modelling of the basket of companies in the first Bistro deal, for example, it had predicted that just 0.82 per cent of the companies would default each year. If those defaults were uncorrelated, or just slightly correlated, then the chance of defaults occurring on 10 per cent of the pool – the amount that might eat up the $700m of capital raised to cover losses – was tiny. That was why JP Morgan could declare super-senior risk so safe, and why Moody’s had rated so many of these securities triple-A.

The fact was, however, that the assumption about correlation was just that: guesswork. And Demchak and his colleagues knew perfectly well that if the correlation rate ever turned out to be appreciably higher than the statisticians had assumed, serious losses might result. What if a situation transpired in which, when a few companies defaulted, numerous others followed? The number of defaults required to set off such a chain reaction was a vexing unknown. Demchak had never seen it happen, and the odds seemed extremely long, but even if there was just a minute chance of such a scenario, he didn’t want to find himself sitting on $100bn of assets that could conceivably go bust. So he decided to play it safe, and told his team to look for ways to cut their super-senior liabilities again, irrespective of what the regulators were saying.

That stance cost JP Morgan a fair amount of money, because it had to pay AIG and others to insure the super-senior risk, and those fees rose steadily as the decade wore on. In the first such deals with AIG, the fee had been just 0.02 cents for every dollar of risk insured each year. By 1999, the price was nearer 0.11 cents per dollar. But Demchak was determined that the team must be prudent.


Me:

Don said...

"The number of defaults required to set off such a chain reaction was a vexing unknown. "

Here's where I disagree with Tett and a lot of others. It is not that the CDSs actually defaulted. What happened was that the rise in the foreclosure rate led to uncertainty about the solvency about CDSs, and this led to credit downgrades and calls for more capital.

What then followed is what I call a Calling Run, following the ideas of Irving Fisher on Debt-Deflation. Many people decided to Flee to Quality, cash or its equivalents, all at once. This included investors not even directly hit by the CDSs or foreclosure problems. A perfect example is China moving from Agencies into Treasuries. Why did they do that?

At this point, in a Calling Run, assets are revalued according to safety and liquidity. On that basis, CDSs and CDOs, etc., lost value precipitously because they are very low on the Flight to Safety Chart, where low isn't good.

For two good papers on this:

Read Irving Fisher's "The Debt-Deflation Theory Of Great Depressions" here:

http://fraser.stlouisfed.org/docs/meltzer/fisdeb33.pdf

And:

http://www.frbatlanta.org/news/CONFEREN/09fmc/gorton.pdf

Slapped in the Face by the Invisible Hand: Banking and the Panic of 2007+
Gary Gorton

Having said that, I'm going to read her book and listen to her lectures, as I read everything that she writes in the FT. Big Felix Salmon also interviewed her for Reuters about this book.

Don the libertarian Democrat

PS I'm going to eventually do a series of posts on whether a robot can discover that it's a robot. But ponder this: The Many-Worlds View proves that there is free will, since any action that can occur, does.

Friday, May 15, 2009

As you can see, there does appear to be a moderate correlation between these two measures

TO BE NOTED:


May 14, 2009, 8:06 pm

Reader Feedback: Social Spending and Inequality

Last week, we ran a couple of posts about some interesting trends in the Organization for Economic Cooperation and Development’s latest “Society at a Glance Report.” In response, a reader who goes by Pzaud wrote:

What would be even more interesting would be to graph inequality of wealth against social spending. According to the OECD, we have the fourth highest inequality, and the fourth lowest social spending. Coincidence?

So, per Pzaud’s suggestion, here’s a graph showing the relationship between these two variables. Click on the image below to see a larger version.

[via Apture]

Here’s how to read this chart. The horizontal axis shows public social spending as a percent of net national income. The vertical axis shows the Gini coefficient, which is a measure of income inequality. A low Gini coefficient means a country has more equal income distribution, while a high Gini coefficient shows more unequal distribution.

As you can see, there does appear to be a moderate correlation between these two measures.

For all 30 O.E.C.D. member countries, public social spending accounts for an average of 24.4 percent of net national income, and the average Gini coefficient is .311. The same respective numbers for the United States are 18.1 percent and .391.

And another noteworthy trend: Across the O.E.C.D., the percent of N.N.I. used for public social spending has generally been growing since the 1980s. This is true for the United States (the green line closer to the bottom), too.

Friday, May 1, 2009

what to make of the unusually low level of capital available to cover losses on the derivatives

TO BE NOTED: From the FT:

"
Genesis of the debt disaster

By Gillian Tett

Published: May 1 2009 19:16 | Last updated: May 1 2009 19:16

illustration of dominoes depicting the role of JP Morgan in the financial crisisIn the 1990s, a young team at Wall Street investment bank JP Morgan pioneered a new way of making money – credit derivatives. Within a decade, the market for these exotic securities had exploded to more than $12,000bn – and some people later blamed them for fuelling the global financial fiasco. In the first of two extracts from her book, Fool’s Gold, the FT’s Gillian Tett reveals how the innovation genie was first let out of the bottle – and eventually devoured the system, to the horror of its creators. The first sign that there might be a structural problem with the innovative bundles of credit derivatives that bankers at JP Morgan had dreamed up emerged in the second half of 1998. In the preceding months, Blythe Masters and Bill Demchak – key members of JP Morgan’s credit derivatives team – had been pestering financial regulators. They believed that by using the new credit derivative products they had helped create, JP Morgan could better manage the risks in its portfolio of loans to companies, and thereby reduce the amount of capital it needed to put aside to cover possible defaults. The question was by how much. (Though these bundles of credit derivatives later went under other names, such as collateralised debt obligations [CDOs], at that time these pioneering structures were known as “Bistro” deals, short for Broad Index Secured Trust Offering). Masters and Demchak had done the first couple of Bistro deals on behalf of their own bank without knowing the answer to their question for sure. But when they were doing these deals for other banks, the question of reserve capital became more important – the others were mainly interested in cutting their reserve requirements.

The regulators weren’t sure. When officials at the Office of the Comptroller of the Currency and the Federal Reserve had first heard about credit derivatives and CDOs, they had warmed to the idea that banks were trying to manage their risk. But they were also uneasy because the new derivatives didn’t fit neatly under any existing regulations. And they were particularly uncertain over what to make of the unusually low level of capital available to cover losses on the derivatives.

When the team did their first Bistro deal, they pooled more than 300 of JP Morgan’s loans, worth a total of $9.7bn, and issued securities based on the income streams from these loans. The lure of the idea was clear: the team had calculated that they only needed to set aside $700m – a strikingly small sum – against the risk of defaults among the 300-plus loans. After much debate, the credit rating agencies had agreed with the team’s assessment of the risks, and the deal had gone ahead on the basis that if financial Armageddon wiped out the $700m funding cushion, JP Morgan would absorb the additional losses itself. To Masters and Demchak, the chance that losses would ever eat through $700m were minuscule.

That argument didn’t wash with European regulators, and some of their US counterparts were uneasy, too. Christine Cumming, a senior Fed official, indicated to Masters and Demchak that JP Morgan should look for a way to insure the rest of the risk – the “missing” $9bn in their original Bistro scheme – if the bank wanted to gain approval to cut its capital reserves. So Masters and her team set out to find a solution. They started by giving the bundle of “uninsured” risk a name. Masters liked to refer to it as “more than triple-A”, since it was deemed even safer than triple A-rated securities. But that was too clumsy to market, so they came up with “super-senior”. The next step was to explore who, if anyone, might want to buy or insure it.

The task did not look easy. As far as JP Morgan was concerned, this risk was not really risky at all, so there was no point paying anything other than a token amount to insure it. On top of that, whoever stepped up to acquire or insure the super-senior risk had to be brave enough to step into an unfamiliar world.

. . .

The seeds of AIG’s destruction

illustration of dominoes depicting the construction of triple A credit ratingsMasters eventually spotted one solution to the super-senior headache. In the past, one of JP Morgan’s longstanding blue-chip clients had been the mighty insurance company American International Group. Like JP Morgan, AIG was a pillar of the American financial establishment. It had risen to prominence by building a formidable franchise in the Asian markets during the early-20th century. That business was later extended to the US, making the company a powerful force in the American economy after the second world war. AIG was considered a weighty and utterly reliable market player, and like JP Morgan, it basked in the sun of a triple-A credit rating.

But within AIG, an upstart entrepreneurial subsidiary was booming. In the late 1980s the company hired a group of traders who had previously worked for Drexel Burnham Lambert, the infamous – and now defunct – champion of the junk-bond business under Michael Milken in the mid-1980s. These traders had developed a capital markets business, known as AIG Financial Products and based in London, where the regulatory regime was less restrictive. It was run by Joseph Cassano, a tough-talking trader from Brooklyn. Cassano was creative, bold and highly ambitious. More important, he knew that, as an insurance company, AIG was not subject to the same burdensome rules on capital reserves as banks. That meant it would not need to set aside anything but a tiny sliver of capital – at most – if it insured the super-senior risk. Nor was the insurer likely to face hard questions from its own regulators because AIG Financial Products had largely fallen through the cracks of oversight. It was regulated by the US Office for Thrift Supervision, whose officials had scant expertise in the field of cutting-edge financial products.

Masters pitched to Cassano that AIG take over JP Morgan’s super-senior risk, and Cassano happily agreed. It was a “watershed” event, or so Cassano later observed. “JP Morgan came to us, who were somebody we worked with a great deal, and asked us to participate in some of what they called Bistro trades [which] were the precursors to what [became] the CDO market,” he explained. It seemed good business for AIG.

AIG would earn a relatively paltry fee for providing this service – just 0.02 cents per dollar insured per year. But if 0.02 cents is multiplied a few billion times, it adds up to an appreciable income stream, particularly if no reserves are required to cover the risk. Once again, the magic of derivatives had produced a “win–win” solution. Only many years later did it become clear that Cassano’s trade had set AIG on the path to ruin.

With the AIG deal in hand, the JP Morgan team returned to the regulators and pointed out that a way had been found to remove the rest of the credit risk from their Bistro deals. They started plotting other sales of super-senior risk to other insurance and reinsurance companies, which snapped it up, not just from JP Morgan but from other banks too.

Then, ironically, just as this business was taking off, the US regulators weighed in again. Officials at the Office of the Comptroller of the Currency and the Fed indicated to JP Morgan that after due reflection they thought that banks did not need to remove super-senior risk from their books after all. The lobbying by Masters and others had seemingly paid off. The regulators were not willing to let the banks get off scot-free. If they held the super-senior risk on their books, they would need to post reserves one-fifth the size of the usual amount (20 per cent of 8 per cent, meaning $1.60 for every $100 that lay on the books). There were also some conditions. Banks could only cut their capital reserves in this way if they could prove that the risk of default on the super-senior portion of the deals was truly negligible, and if the securities being issued via a Bistro-style structure carried a triple A credit rating from a “nationally recognised credit rating agency”. Those were strict terms, but JP Morgan was meeting them.

The implications were huge. Banks had typically been forced to hold $800m reserves for every $10bn of corporate loans on their books. Now that sum could fall to just $160m. The Bistro concept had pulled off a dance around the international banking rules.( NB DON )

For a while, Demchak’s team stopped transferring super-senior risk from JP Morgan’s books. But then Demchak became uneasy. The super-senior risk was ballooning to a staggering figure, because when the bank arranged these credit derivatives transactions for clients, it typically put the super-senior risk in the deal on its own balance sheet. In theory, there was no reason to worry. But by 1999, the total pipeline of future deals had swelled towards $100bn. Something about that mountain of risk started to offend Demchak’s common sense. “If you have got $60bn, $100bn or however many billions of something on your balance sheet, that is a very big number,” he remarked to his team. “I don’t think you should ignore a big number, no matter what it is.”

. . .

The problem with correlation

Demchak was acutely aware that modelling the risks involved in credit derivatives deals had its limits. One of the trickiest problems revolved around the issue of “correlation”, or the degree to which defaults in any given pool of loans might be interconnected. Trying to predict correlation is a little like working out how many apples in a bag might go rotten. If you watch what happens to hundreds of different disconnected apples over several weeks, you might guess the chance that one apple might go rotten – or not. But what if they are sitting in a bag together? If one apple goes mouldy, will that make the others rot too? If so, how many and how fast?

Similar doubts dogged the corporate world. JP Morgan statisticians knew that company debt defaults are connected. If a car company goes into default, its suppliers may go bust, too. Conversely, if a big retailer collapses, other retail groups may benefit. Correlations could go both ways, and working out how they might develop among any basket of companies is fiendishly complex. So what the statisticians did, essentially, was to study past correlations in corporate default and equity prices and program their models to assume the same pattern in the present. This assumption wasn’t deemed particularly risky, as corporate defaults were rare, at least in the pool of companies that JP Morgan was dealing with. When Moody’s had done its own modelling of the basket of companies in the first Bistro deal, for example, it had predicted that just 0.82 per cent of the companies would default each year. If those defaults were uncorrelated, or just slightly correlated, then the chance of defaults occurring on 10 per cent of the pool – the amount that might eat up the $700m of capital raised to cover losses – was tiny. That was why JP Morgan could declare super-senior risk so safe, and why Moody’s had rated so many of these securities triple-A.

The fact was, however, that the assumption about correlation was just that: guesswork. And Demchak and his colleagues knew perfectly well that if the correlation rate ever turned out to be appreciably higher than the statisticians had assumed, serious losses might result. What if a situation transpired in which, when a few companies defaulted, numerous others followed? The number of defaults required to set off such a chain reaction was a vexing unknown. Demchak had never seen it happen, and the odds seemed extremely long, but even if there was just a minute chance of such a scenario, he didn’t want to find himself sitting on $100bn of assets that could conceivably go bust. So he decided to play it safe, and told his team to look for ways to cut their super-senior liabilities again, irrespective of what the regulators were saying.

That stance cost JP Morgan a fair amount of money, because it had to pay AIG and others to insure the super-senior risk, and those fees rose steadily as the decade wore on. In the first such deals with AIG, the fee had been just 0.02 cents for every dollar of risk insured each year. By 1999, the price was nearer 0.11 cents per dollar. But Demchak was determined that the team must be prudent.

. . .

The mortgage time bomb

illustration of domino pieces on fire depicting the financial crisisAround the same time, the JP Morgan team stumbled on a second, potentially bigger problem. As the innovation cycle turned and earnings declined from the early Bistro deals based on pools of corporate loans, Demchak asked his team to explore new uses for Bistro-style deals, either by modifying the structure or by putting new kinds of loans or other assets into the mix. They decided to experiment with mortgages. Terri Duhon was at the heart of the endeavour. Only 10 years earlier, Duhon had been a high-school student in Louisiana. When she told her relatives she was going to work in a bank, they had assumed she was going to be a teller. Now she was managing tens of billions of dollars. She was trained as a mathematician, and she thrived on adrenaline, riding motorbikes in her spare time. Even so, she found the thought of being in charge of all those zeros awe-inspiring. “It was just an extraordinary, intense experience,” she later recalled.

A year after Duhon took on the post, she got word that Bayerische Landesbank, a large German bank, wanted to use the credit derivatives structure to remove the risk from $14bn of US mortgage loans it had extended. She debated with her team whether to accept the assignment; working with mortgage debt wasn’t a natural move for JP Morgan. But Duhon knew that some of the bank’s rivals were starting to conduct credit derivatives deals with mortgage risk, so the team decided to take it on.

As soon as Duhon talked to the quantitative analysts, she encountered a problem. When JP Morgan had offered the first Bistro deals in late 1997, it had access to extensive data about all the loans it had pooled together. So did the investors who bought the resulting credit derivatives, since the bank had deliberately named all of the 307 companies whose loans were included. In addition, many of these companies had been in business for decades, so extensive data were available on how they had performed over many business cycles. That gave JP Morgan’s statisticians, and investors, great confidence in predicting the likelihood of defaults. But the mortgage world was very different. For one thing, when banks sold bundles of mortgage loans to outside investors, they almost never revealed the names and credit histories of the individual borrowers. Worse, when Duhon went looking for data to track mortgage defaults over several business cycles, she discovered it was in short supply.

While America’s corporate world had suffered several booms and recessions in the later 20th century, the housing market had followed a steady path of growth. Some specific regions had suffered downturns: prices in Texas, for example, fell during the Savings and Loans debacle of the late 1980s. But since the second world war, there had never been a nationwide house-price slump. The last time house prices had fallen significantly en masse, in fact, was way back in the 1930s, during the Great Depression. The lack of data made Duhon nervous. When bankers assembled models to predict defaults, they wanted data on what normally happened in both booms and busts. Without that, it was impossible to know whether defaults tended to be correlated or not, in what circumstances they were isolated to particular urban centres or regions, and when they might go national. Duhon could see no way to obtain such information for mortgages. That meant she would either have to rely on data from just one region and extrapolate it across the US, or make even more assumptions than normal about how defaults were correlated. She discussed what to do with Krishna Varikooty and the other quantitative experts. Varikooty was renowned on the team for taking a sober approach to risk. He was a stickler for detail and that scrupulousness sometimes infuriated colleagues who were itching to make deals. But Demchak always defended Varikooty. His judgment on the mortgage debt was clear: he could not see a way to track the potential correlation of defaults with any confidence. Without that, he declared, no precise estimate could be made of the risks of default in a pool of mortgages. If defaults on mortgages were uncorrelated, then the Bistro structure should be safe for mortgage risk, but if they were highly correlated, it might be catastrophically dangerous. Nobody could know.

Duhon and her colleagues were reluctant simply to turn down Bayerische Landesbank’s request. The German bank was keen to go ahead, even after the uncertainty in the modelling was explained, and so Duhon came up with the best estimates she could to structure the deal. To cope with the uncertainties the team stipulated that a bigger-than-normal funding cushion be raised, which made the deal less lucrative for JP Morgan. The bank also hedged its risk. That was the only prudent thing to do, and Duhon couldn’t see herself doing many more such deals. Mortgage risk was just too uncharted. “We just could not get comfortable,” Masters later said.

In subsequent months, Duhon heard through the grapevine that other banks were starting to do credit derivatives deals with mortgage debt, and she wondered how they had coped with the lack of data that so worried her and Varikooty. Had they found a better way to track the correlation issue? Did they have more experience of dealing with mortgages? She had no way of finding out. Because the credit derivatives market was unregulated, details of the deals weren’t available.

The team at JP Morgan did only one more Bistro deal with mortgage debt, a few months later, worth $10bn. Then, as other banks ramped up their mortgage-backed business, JP Morgan largely dropped out. Eight years later, the unquantified mortgage risk that had frightened off Duhon, Varikooty and the JP Morgan team had reached vast proportions. And it was spread throughout the western world’s financial system.

Gillian Tett is an assistant editor at the FT. In March, she was named Journalist of the Year at the British Press Awards

This is an edited extract from ‘Fool’s Gold: How Unrestrained Greed Corrupted a Dream, Shattered Global Markets and Unleashed a Catastrophe’ by Gillian Tett. It is published this week by Little, Brown, £12.99, and in mid-May by Simon & Schuster in the US. To buy the book at 20 per cent discount, call the FT ordering service on 0870 429 5884 or go to www.ft.com/bookshop

Go to FT.com/tettinterview for a video interview of Gillian Tett by Andrew Davis, FT Weekend editor