Prof. Jayanth R. Varma’s Financial Markets Blog

A blog on financial markets and their regulation

Computational and sociological analyses of financial modeling

I have been reading a number of papers that examine financial
modeling in the context of the current crisis from a computational
complexity and sociology of knowledge point of view:

I liked all these papers and learned a lot from each of them which
is not the same as saying that I agree with all of them.

The paper that I liked most was Beunza and Stark which is really
about cognitive interdependence and systemic risk. Their work is based
on an ethnographic study of financial modeling carried out over a
three year period at a top-ten global investment bank. Some of their
conclusions are:

Using models in reverse, traders find out what their rivals are
collectively thinking. As they react to this knowledge, their actions
introduce a degree of interdependence …

Quantitative tools and models thus give back with one hand the
interdependence that they took away with the other. They hide
individual identities, but let traders know what the consensus
is. Arbitrageurs are thus not embedded in personal ties, but neither
are they disentangled from each other.

Scopic markets are fundamentally different from traditional social
settings in that the tool, not the network, is the central
coordinating device.

Instead of ascribing crises to excessive risk-taking, misuse of the
models, or irreflexive imitation, our notion of reflexive modeling
offers an account of crises in which problems unfold in spite of
repeated reassurances, early warnings, and an appreciation for
independent thinking.

Implicit in the behavioral accounts of systemic risk is an emphasis
on the individual biases and limitations of the investors. At the
extreme, investors are portrayed as reckless gamblers, mindless
lemmings, or foolish users of models they do not understand. By
contrast, our detailed examination of the tools of arbitrage offers a
theory of crisis that does not call for any such bias. The reflexive
risks that we identified befall on arbitrageurs that are smart,
creative, and reflexive about their own limitations.

Though the paper is written in a sociological language, what it
most reminded me of was Aumann’s paper more than 30 years ago on
“Agreeing to disagree” (The Annals of
1976). What Beunza and Stark describe as
reflexivity is closely related to Aumann’s celebrated theorem:
“If two people have the same priors, and their posteriors for a
given event A are common knowledge, then these posteriors must
be equal.”

The Brigo et al paper is mathematically demanding as they
take “an extensive technical path, starting with static copulas
and ending up with dynamic loss models.” But it is very useful
in explaining why the Gaussian copula model is still used in its base
correlation formulation though its limitations have been known for
several years. My complaint about the paper is that it focuses too
much on the difficulties in fitting the Gaussian copula to observed
market prices and too little on the difficulties of using it to
estimate the impact of plausible stress events.

MacKenzie focuses on “evaluation cultures” which are
broader than just models. They are “pockets of local consensus
on how financial instruments should be valued.” He argues that
“‘Greed’ – the egocentrically-rational pursuit
of profits and bonuses – matters, but the calculations that the
greedy have to make are made within evaluation
cultures”. MacKanzie highlights “the peculiar status of
the ABS CDO as what one might call an epistemic orphan –
cognitively peripheral to both its parent cultures, corporate CDOs and

The Arora et al paper is probably the most mathematical of
the lot. It essentially shows that an originator can put bad loans
into CDOs in such a way that it is computationally infeasible for the
investors to figure this out even ex post.

However, for a real-life buyer who is computationally bounded, this
enumeration is infeasible. In fact, the problem of detecting such a
tampering is equivalent to the so-called hidden dense subgraph
problem, which computer scientists believe to be intractable
… Moreover, under seemingly reasonable assumptions, there is a way
for the seller to ‘plant’ a set S of such over-represented
assets in a way that the resulting pooling will be computationally
from a random pooling.”

Furthermore, we can show that for suitable parameter choices the
tampering is undetectable by the buyer even ex post. The buyer
realizes at the end that the financial products had a higher default
rate than expected, but would be unable to prove that this was due to
the seller’s tampering.

The derivatives that Arora et al discuss are weird binary
CDOs and my interpretation of this result is that in a rational
market, these kinds of exotic derivatives would never be created or
traded. Nevertheless, this is an important way of looking at how
computational complexity can reinforce information asymmetry under
certain conditions.


4 responses to “Computational and sociological analyses of financial modeling

  1. Ted K January 24, 2010 at 10:33 pm

    This is terrific stuff. I plan to read all of these. Maybe I can read and grasp these 4 papers after 5 years. I love this stuff, but I am afraid my brain is not as good as yours Professor Varma. If I am in your class I will be the one sitting in the back of class, always last to answer questions. Hahaha.

    But don’t stop letting us know about these papers Professor Varma. Really great stuff. We need you to immigrate to America and help us turn this economy around. Getting worrisome with this craziness. Don’t worry, you can get some really good Mangos in San Francisco—think about it ok???

  2. cosmo January 28, 2010 at 9:48 pm

    So what I gather from this is that the schnooks not only were selling dubious instruments but were purposefully masking the crappy loans in the pool. That makes a lot of sense in hindsight.

  3. Ted K January 30, 2010 at 8:09 pm

    That is another thing that is so infuriating about this whole thing. The banks say the “pools” (CDOs and packaged loans) were to diversify risk, when in fact it was a scam to hide/sell loans they knew from the beginning would NEVER be paid off. Lie on top of lie on top of lie.

    Professor Varma, I wrote a post on my blog about some bad accounting practices at Warren Buffett’s Wells Fargo bank. I would be very flattered if you would visit and make some comments.

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