Derman’s Financial Modeler’s Manifesto

January 13, 2009

Here is a fascinating reaction to the credit crisis. As you know, we at Socializing Finance have been following the work of Emanuel Derman very closely, and count ourselves as admirers of his book. The credit crisis threatens to completely destroy the legitimacy of quantitative finance. And this is their response:

The Financial Modelers’ Manifesto

by

Emanuel Derman and
Paul Wilmott

Preface

A spectre is haunting Markets – the spectre of illiquidity, frozen credit, and the failure of financial models.

Beginning with the 2007 collapse in subprime mortgages, financial markets have shifted to new regimes characterized by violent movements, epidemics of contagion from market to market, and almost unimaginable anomalies (who would have ever thought that swap spreads to Treasuries could go negative?). Familiar valuation models have become increasingly unreliable. Where is the risk manager that has not ascribed his losses to a once-in-a-century tsunami?

To this end, we have assembled in New York City and written the following manifesto.

Manifesto

In finance we study how to manage funds – from simple securities like dollars and yen, stocks and bonds to complex ones like futures and options, subprime CDOs and credit default swaps. We build financial models to estimate the fair value of securities, to estimate their risks and to show how those risks can be controlled. How can a model tell you the value of a security? And how did these models fail so badly in the case of the subprime CDO market?

Physics, because of its astonishing success at predicting the future behavior of material objects from their present state, has inspired most financial modeling. Physicists study the world by repeating the same experiments over and over again to discover forces and their almost magical mathematical laws. Galileo dropped balls off the leaning tower, giant teams in Geneva collide protons on protons, over and over again. If a law is proposed and its predictions contradict experiments, it’s back to the drawing board. The method works. The laws of atomic physics are accurate to more than ten decimal places.

It’s a different story with finance and economics, which are concerned with the mental world of monetary value. Financial theory has tried hard to emulate the style and elegance of physics in order to discover its own laws. But markets are made of people, who are influenced by events, by their ephemeral feelings about events and by their expectations of other people’s feelings. The truth is that there are no fundamental laws in finance. And even if there were, there is no way to run repeatable experiments to verify them.

You can hardly find a better example of confusedly elegant modeling than models of CDOs. The CDO research papers apply abstract probability theory to the price co-movements of thousands of mortgages. The relationships between so many mortgages can be vastly complex. The modelers, having built up their fantastical theory, need to make it useable; they resort to sweeping under the model’s rug all unknown dynamics; with the dirt ignored, all that’s left is a single number, called the default correlation. From the sublime to the elegantly ridiculous: all uncertainty is reduced to a single parameter that, when entered into the model by a trader, produces a CDO value. This over-reliance on probability and statistics is a severe limitation. Statistics is shallow description, quite unlike the deeper cause and effect of physics, and can’t easily capture the complex dynamics of default.

Models are at bottom tools for approximate thinking; they serve to transform your intuition about the future into a price for a security today. It’s easier to think intuitively about future housing prices, default rates and default correlations than it is about CDO prices. CDO models turn your guess about future housing prices, mortgage default rates and a simplistic default correlation into the model’s output: a current CDO price.

Our experience in the financial arena has taught us to be very humble in applying mathematics to markets, and to be extremely wary of ambitious theories, which are in the end trying to model human behavior. We like simplicity, but we like to remember that it is our models that are simple, not the world.

Unfortunately, the teachers of finance haven’t learned these lessons. You have only to glance at business school textbooks on finance to discover stilts of mathematical axioms supporting a house of numbered theorems, lemmas and results. Who would think that the textbook is at bottom dealing with people and money? It should be obvious to anyone with common sense that every financial axiom is wrong, and that finance can never in its wildest dreams be Euclid. Different endeavors, as Aristotle wrote, require different degrees of precision. Finance is not one of the natural sciences, and its invisible worm is its dark secret love of mathematical elegance and too much exactitude.

We do need models and mathematics – you cannot think about finance and economics without them – but one must never forget that models are not the world. Whenever we make a model of something involving human beings, we are trying to force the ugly stepsister’s foot into Cinderella’s pretty glass slipper. It doesn’t fit without cutting off some essential parts. And in cutting off parts for the sake of beauty and precision, models inevitably mask the true risk rather than exposing it. The most important question about any financial model is how wrong it is likely to be, and how useful it is despite its assumptions. You must start with models and then overlay them with common sense and experience.

Many academics imagine that one beautiful day we will find the ‘right’ model. But there is no right model, because the world changes in response to the ones we use. Progress in financial modeling is fleeting and temporary. Markets change and newer models become necessary. Simple clear models with explicit assumptions about small numbers of variables are therefore the best way to leverage your intuition without deluding yourself.

All models sweep dirt under the rug. A good model makes the absence of the dirt visible. In this regard, we believe that the Black-Scholes model of options valuation, now often unjustly maligned, is a model for models; it is clear and robust. Clear, because it is based on true engineering; it tells you how to manufacture an option out of stocks and bonds and what that will cost you, under ideal dirt-free circumstances that it defines. Its method of valuation is analogous to figuring out the price of a can of fruit salad from the cost of fruit, sugar, labor and transportation. The world of markets doesn’t exactly match the ideal circumstances Black-Scholes requires, but the model is robust because it allows an intelligent trader to qualitatively adjust for those mismatches. You know what you are assuming when you use the model, and you know exactly what has been swept out of view.

Building financial models is challenging and worthwhile: you need to combine the qualitative and the quantitative, imagination and observation, art and science, all in the service of finding approximate patterns in the behavior of markets and securities. The greatest danger is the age-old sin of idolatry. Financial markets are alive but a model, however beautiful, is an artifice. No matter how hard you try, you will not be able to breathe life into it. To confuse the model with the world is to embrace a future disaster driven by the belief that humans obey mathematical rules.

MODELERS OF ALL MARKETS, UNITE! You have nothing to lose but your illusions.

The Modelers’ Hippocratic Oath

~ I will remember that I didn’t make the world, and it doesn’t satisfy my equations.

~ Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.

~ I will never sacrifice reality for elegance without explaining why I have done so.

~ Nor will I give the people who use my model false comfort about its accuracy.
Instead, I will make explicit its assumptions and oversights.

~ I understand that my work may have enormous effects on society and the economy,
many of them beyond my comprehension.

Emanuel Derman January 7 2009

Paul Wilmott January 7 2009

Please pass on this Manifesto to interested colleagues and friends.

It would be very instructive to see the reaction of sociologists to this document. Does it go far enough? Does this guarantee that the past modeling excesses will not be repeated? What else is needed? These are the questions, I think, that need to be addressed now.

8 Responses to “Derman’s Financial Modeler’s Manifesto”

  1. Mike Says:

    I enjoy both Wilmott and Derman quite a bit, and I work in quant finance – however I worry that the Marx quote for quant finance isn’t “A spectre is haunting Europe” but more “the first time as tragedy, the second time as farce.”

  2. typewritten Says:

    Hey, Mike’s point is very good! It’s very intriguing that top people from the financial community is now feeling so much at ease with the communist jargon, though. In a couple of months, we will have some quantitative folk using the concept of “internal contradiction of capital”.

    One comment on content: this is a nice and sympathetic call for a sober use of maths in finance. Fair enough. But in a sense it pushes forward the argument that finance went wrong because of the maths, doesn’t it? From a “social student of finance” perspective, though, a far more powerful argument is that finance went wrong because financiers were trying to make money with whatever piece of thing was falling into their hands, including extremely crappy things. We would have loved Derman and Wilmott adding some more sociological oath in the line of: we will look at the *thing* we model and for *who* (and, why not, for *what*, hoping that “for making money” will not be the only response). In a sense, it’s already quite nice (and sociological) that these authors move from the “right/wrong” epistemology to the “useful/useless” realism. But the “usefulness” question opens the door to the “for what/who” question.

  3. Coleman Says:

    typewritten nails it: were the maths simply bad? Do we just need to use them more carefully? Plenty of actors in the financial system were receiving warning signals of all kinds before the big freeze up. If (broadly) lack of common sense is to blame, then the religious metaphor of idolatry is apt.

    But were the complex models themselves the idols that were worshiped? Unfortunately, the true idol might have been that *essential* component of financial markets: money. The models, rather, were the rites spoken by the priests before the golden calf, the magic words that increase the asset’s value (or help us pretend that this is taking place).

  4. Zach Says:

    http://www.washingtonpost.com/wp-dyn/content/article/2009/01/12/AR2009011202109.html?hpid=topnews

    testosterone (bio anthro) + mathematical certitude (social structure) + class formation (critical theory) = financial crisis (confused economists)?

  5. danielbeunza Says:

    I agree with Typewritten, and perhaps less so with Mike and Coleman. The point is, what was the source of the problems in the credit crisis? My view is that it is not modeling per se, but the organization of modeling. I owe the readers of the blog a detailed post on this issue. My work with David Stark offers plenty of suggestions for how to locate, schedule, staff and incentivize modeling in a way that is productive, not destructive to the organization. More soon…

  6. Mike Says:

    I would really love to read a detailed post on “My view is that it is not modeling per se, but the organization of modeling.” Even if it was a summary of research still to be fleshed out, knowing where the boundaries of the debate would be would be very interesting.


  7. I second Mike’s call for the longer blog post.

    I am also curious about the broader phenomena of quants (be it financial modelers in this case, or statisticians, or any other purveyor of numbers and theorems) blaming those who used the models or figures and not the models and figures themselves.

    If we follow Desrosières, then isn’t the point of statistics to make things hold together? Having made something sufficiently convincing that it holds together sufficiently to be used as inputs in other processes, how can the purveyors then blame the purchases for believing? That is, enchantment grows with distance – but isn’t that the point?

    • danielbeunza Says:

      OK. I am going to deliver this week. A long detailed post on my own view of modeling. But, to reply to Dan more directly, the analogy to Desrosieres is right on… in the end, the quants will be judged by whether the financial economy holds together. And, yes, quants are in themselves a fascinating figure. Someone should be studying them.


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