Emanuel Derman

I said I would try to explain what I mean by sophisticated vulgarity in financial modeling, which I will do by imperfect analogy.

Suppose you are thinking of manufacturing tropical fruit salad and you are going to market it in the USA. And suppose there is no tropical fruit salad currently being sold.

Your fruit salad will contain mangos, papayas, passion fruit, coconuts, litchis, kumquats and loquats. Once you quote a price to Whole Foods, your distributor, you will be committed to it, and you need to give them a price. So, you want to figure out how much to plan on charging e for a can of tropical fruit salad which you haven’t actually produced yet. There are a variety of ways you could do it.

1. You could model out how much it would cost to import the raw ingredients –  mangos, papayas, lychees, syrup  and loquats, etc. –from cost at their source, taking account of shipping, shrinkage, insurance, canning, etc, and determine a fair price, allowing for profit. That’s a direct way to proceed, subject to uncertainty about what these costs will be in the future. In that sense it’s a little akin to Black-Scholes modeling of options, which makes assumption about the future that may not/ will not turn out to be true, and then figures out the cost of synthesizing an option.

2. You could build a deeper model, and instead of accepting the market price for the raw mangos, passion fruit, papayas, lychees and loquats  and sugar yourself as a proxy for future fruit prices, you could try to estimate it ab initio, taking into account of the cost of seeds, fertilizer, farmland, your inexperience in raising strange crops, labor etc, and determine the cost that way. That’s deep, but it needs a lot of knowledge you don’t have and will therefore necessarily misestimate. This is a little akin to stochastic volatility models, where you make assumptions about things you really have not much experimental information on. Ambitious, but …

3. Finally, you could be sophisticated. Though this example is a little contrived, I hope it gets at the idea. You can think of tropical fruit salad as a sort of mixture of other processed products already available in the market  that come closer than the raw ingredients to what you are trying to make. Thus, think of the fruit salad as involving Tropical Fruit Life Savers (very tasty!), for example, plus commercial passion fruit juice sweetened with apple juice,  Puerto Rican canned guava desserts filled with cottage cheese, and so on. Then model your tropical fruit salad as a mixture of all of these (and other) processed foods, long some of them, short apple juice and cottage cheese. The price of your tropical fruit salad is then related to the price of processed foods that come close to it.

This is similar to the so-called Vanna Volga heuristic method (I don’t want to call it a theory or even a model)  in options pricing. It’s sophisticated because you have to think about how to make your salad out of other already processed complicated things. It’s vulgar in that you use only things that the crowds can already buy.

I like it, and even a lot of people who are capable of method 2 in theory often use method 3 in practice, pragmatically. I like it because it uses the market values of one set of processed foods to find the market value of another, rather than starting from raw foods. Using processed foods as input removes a lot of the modeling uncertainties.

Edward Hadas of the FT/Lex has an interesting video on the debt ceiling fiasco called  Playing Chicken with America’s Future. He calls himself an American Declinist.

Watching Obama and Boehner last night, it’s hard to disagree. I thought Obama was more smoothly manipulative, but not convincingly so,  and Boehner more transparently and crudely manipulative, but neither one was great statesmanship or even statesmanship.

Unfortunately for me, and it may be unfair, no matter what Obama says, I can’t forget the mismatch between what he seemed to promise and what he seems to have done, most particularly with regard to the financial crisis. This cartoon by Barry Blitt  from a Frank Rich op ed in the NY Times almost a year ago is seared in my memory, and dominates my view of Obama.

When he speaks now, that’s what I see. I suspect, rightly or wrongly, many people see the same. Is it fair? Probably not. I find it much easier to despise foolish “intellectuals” and/or Democrat politicians than foolish “non-intellectuals” and/or Republican politicians, because I hold them to a higher standard. I probably shouldn’t.

Near record heat in NYC, 92 degrees even at 9pm.

U.S. govt messed up as usual. Europe too.

The Norway killings.

I was in Norway twice.  The first time was during the LTCM/Russian default crisis, which occurred while I was hiking around the fjords with a group of Norwegians, mostly middle-aged women it turned out. Every day before the start of the hike they played a cassette with the theme song of the Lillehammer Winter Olympics and did three minutes worth of aerobics to it in unison. I grinned the first time, but you should have seen them bound up and down the hills like mountain goats. Especially down, which took more skill. Not only that, but they all loved Bill Clinton. “I like a man who likes women,” one of them said. I went to Norway a second time, about six years later, to Balestrand and Bergen, and it was beautiful. I’d love to go again.

I spent yesterday and today frustratedly trying to communicate to various people at my publisher how get the figures in the proofs of my book corrected, and trying to get a cover design from them. There seems to be a disconnect between the people in charge of the figures and the people in charge of the text, which should coordinate. It reminds me of the disconnect between programmers building the interface to risk systems and people writing the analytics. One person needs to transcend the divide, else things fall in the cracks.

I am trying to see things the publishers’ way, with only a small amount of success. You imagine yours is the only book they’re handling, and in reality you are one of an endless sequence of books that come by their desk on an assembly line. There’s always the same number of books on their horizon; as one book rolls off their assembly line, another rolls on. Maybe I should think of them as a book production factory, with a certain inherent error rate. Then everything would be less surprising.

To escape the heat and boredom and frustration, I went to the Walter Reade theater last night. They were having a Sidney Lumet retrospective,  and I saw, appropriately, Dog Day Afternoon with Al Pacino, circa 1970s. I’d seen before when it first came out but I’d forgotten how good Pacino was.

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What I meant to write about was what I once said I would: what I like to call sophisticated vulgarity in financial modeling.

In the natural sciences, it pays to be ambitious and deep. Kepler discovering that planets trace out ellipses about the sun is deep;  that their radii sweep out equal areas is unobvious and deeper still. Newton’s laws of mechanics even more so. De Maupertuis’s principle of least action, a broader and deeper restatement of Newton’s laws, but one analytically continuable to other non-mechanical systems, is a couple of levels deeper. “Nature is thrifty in all its actions,” he claimed. It sounds somewhat anthropomorphic and New-Agey, but he, and Euler afterwards, meant something precise and mathematical by “action.”

A theory like de Maupertuis’s penetrates to the inner that corresponds to the outer. You can use the knowledge of the inner to deduce many interesting and correct/true statements about things visible on the surface.

It would be wonderful if the social sciences, finance in particular, satisfied similar general principles that operated inside but affected the surface. But no such general principle works too well. After banging your head searching for really true subterranean general principles for a while, you begin to think that it’s better to be pragmatic and stay on the surface.

Is that because there is no inner – because there’s no inside inside? Or because we haven’t found it yet?

I call staying on the surface being vulgar – staying close to the descriptors of the crowds who make the market.

But it’s good to be sophisticatedly vulgar: to use the descriptors that traders use, but use them in a sophisticated way. (One man’s vulgarity of course, is another man’s sophistication). I’ll try to give an example of vulgarly sophisticated modeling in a subsequent post.

There is an orgy of glee as people watch the News of the World implode and see powerful or formerly powerful people threatened with legal action and loss of power.  It’s a nice distraction in which almost everyone can feel righteous. There was a similar frenzy when FNMA, FHLMC and Lehman collapsed, but with no consequent long term behavioral consequences. One of the questions I don’t know the answer to, but which keeps presenting itself to me these days, is:

How do you restrain or punish corporations, collections of people that have some of the advantages of real people, but less of the disadvantages?

Larry Summers writes that “The European Central Bank is right that punishing creditors for the sake of teaching lessons or building political support is reckless in a system that depends on confidence.” This an argument against punishment, for our own greater good.

But letting corporations and creditors get away with confidence-shattering behavior is confidence shattering too, in the longer run. Punishment for folly should be shared more equally among debtors and creditors. For every put society provides to a creditor as belated insurance, the creditor should  give taxpayers a future call, a late payment of the insurance premium they should have paid.

More generally, the punishment of organizations is much less severe than the punishment of individuals, and consequently much less effective. James Grant, in the Washington Post, wrote:

In Brazil — which learned a thing or two about frenzied finance during its many bouts with hyperinflation — bank directors, senior bank officers and controlling bank stockholders know that they are personally responsible for the solvency of the institution with which they are associated. Let it fail, and their net worths are frozen for the duration of often-lengthy court proceedings. If worse comes to worse, the responsible and accountable parties can lose their all.

In the U.S. it often feels as though banks allegedly guilty of money laundering or other offenses settle by paying fines. The fines “hurt” the banks, but don’t really act as restraints, because a corporation isn’t a person. The same is true of churches whose members have committed abuses. You can’t effectively punish a church. Restraint works best on individuals, not on organizations.

How can you restrain organizations? Only by somehow making clear that organizational acts are at bottom acts of some individual within them.

You’d think physicists would see everything in material terms, but it’s biologists and neuroscientists who are the new materialists …

That is the beginning of a short note I wrote for Wired.UK a few months ago, that continues HERE in their August issue.

The world is complex and you lose a lot by insisting things you don’t understand already fit into the boxes you imagine you do.

There is an article on Bloomberg about the salaries earned by risk managers, here.

Here are brief summaries I found on the web of the backgrounds of the people involved:

• Mr. Thompson holds an MBA from the Darden School at University of Virginia, where he graduated with honors, and a bachelor’s degree from Allegheny College, where he also graduated with honors and was elected to Phi Beta Kappa.
• Zubrow worked at Goldman Sachs from 1979 to 2004, JPMorgan said. His last job before leaving the firm where Corzine was co- chairman until 1999 was chief administrative officer.
• Brian  (Leach) has a B.A. in economics from Brown University and an M.B.A. from Harvard Business School.

What I conclude from this is that these are not traditional risk people now making a lot of money. These are traditional make-a-lot-of-money people now doing risk.

Highly paid risk positions, I suspect, are still not quant positions. Not even clear if they should be, but that’s how it is.

What’s Your Edge?
If you are going to seek a career in quantitative finance, what’s your advantage? Is it computer science, financial theorizing, pragmatic modeling, sales or trading, working on the desk with people or solitarily in an office? Which are you best at and, also, which do you enjoy doing most?

For years I came across people who could have had wonderful careers combining finance and applied computer science, a combination rarely found in investment banks, and yet so few of them want to take advantage of their computer skills.

Collegial or Solitary?
What environment do you like working in? Academic or  bureaucratic? Do you like being supervised and taught or do you want to go your own way? Do you want to work in a large organization or a small one? This can make the difference between choosing an investment bank, a hedge fund, or a financial software company.

One incidental remark about working in business which I’ve realized only slowly. If you like interacting with people and working toward common goals, then well-run businesses are great places to work. Everyone is paid to work towards the same end, and there can be tremendous cooperation and team work. Academic institutions tend to be filled largely with people who by choice want to work quietly and alone, and so often, funnily enough, business life can be more collegial than college life.

If you choose to work in a business, do you have the personality for it? Can you bear the slow bureaucracy of a large organization, the ladder you have to climb, being ordered around and sometimes yelled at, the overwhelming respect paid to generating profit? If you’re a quant working for a desk, can you over the long haul bear being regarded as a tool  rather than a prime mover? Some of this is changing, but some prejudice has always lurked beneath the surface. Money changes everything.

Short-Term or Long-Term? Research or Application?
If you choose to work in research, building models, do you have the inner drive to work alone for long periods without too much feedback or encouragement? (A PhD is a good training for this.).

In my case, I like the mix of both.

And, if you work in a university, to a large extent you will be cut off from knowing what is truly useful in practice, as opposed to what you imagine is useful. Many people in universities have false mental models of the way models are used on Wall Street. They imagine too much reliance on models and prediction, or too little.

The truth is that most useable and useful models are relatively simple filters that convert quantities you can grasp intuitively into dollar values. Black-Scholes converts your intuition about volatility into the dollar price of a complicated derivative security. This is very different from the physics or engineering you were brought up on, where models predict behavior accurately.

Anti-Intellectualism
If you’re a scientist by training or nature, you will have to tolerate the anti-academic attitude of many people in the business world, though that is changing too. Are you comfortable with the idea that research should be kept secret, because that’s what many people on the Street believe, even if the secrets they think they know were sometimes borrowed from someone else, are not really that secret much of the time, and perhaps are rarely worth being kept secret? (Looking at the world with a jaundiced eye, you notice that a manager hates anyone that leaves their firm for another, taking useful experience and knowledge with him, until he or she does it himself.)

As For Man, His Days Are Like Grass
Weber, my son once explained to me, pointed out that as a scientist your every theory will most likely soon be replaced by a better one. You have to live with that. That’s even more true in quantitative finance. And it’s the opposite with art, where beautiful creations are timeless. We all still admire the original Venus de Milo, but no-one reads even the original Newton.

Because financial models are so ephemeral, I sometimes find myself opposing undergraduate degrees in the field; maybe it’s better to spend your undergraduate years learning really solid things that will last forever.

Semi-Apologia Pro Vita Sua
One final remark about values, perhaps not entirely inappropriate.

If you’re interested in more than just equations and solving them, then: What is the meaning and value of the work you do in the larger world?

If you’re a practicing scientist, I think it’s at least superficially clear: you’re adding to knowledge, pursuing the truth, (thinking that you’re) helping (perhaps) make the world a better place.

As regards quantitative finance, it’s less clear. Many people think you’re mis-employing your talents when you go to work in finance. (Nevertheless, when people ask me if I couldn’t be using my skills more usefully, I ask them the same.) Yes, you are adding to knowledge, but what is it used for? Often, simply to make money. Yes, that making of money may make markets more efficient, but is that sufficient social justification? I sometimes think that at least in finance, to paraphrase Johnson, invoking efficiency is the last refuge of scoundrels/self-interested people. But everyone is self-interested.

Personally, I like to think that to do quantitative finance you need to be committed to clarity – that there’s value in the world to seeing anything clearly and understanding it honestly, to seeing the world the way it really is. That’s enough of a vocation — to understand some part of the world. To be a good practitioner I like to think you need a dedication to unsentimental truth about whatever you deal with, wherever it may take you. I like to think that part of our job on earth is to be perceptive and accurate about as much as possible, including ourselves, about the way the world really works. If you do that, even for small things, it can add up to something bigger. It’s the one standard that transcends individual fields of study. That’s part of my rationale. There are others parts too. But mostly, it’s interesting work and sometimes useful and I’m not a saint. You have to decide for yourself what your rationale is, and whether the field is merely your job, or your career, or, if you’re lucky, your vocation.

The other day the new MSFE students showed up at Columbia for orientation and I had to welcome them. These are some notes from what I said.

Part 1

Background
Several years ago, my son, who did a PhD thesis on the reception history of Max Weber, the founding father of sociology, introduced me to two influential essays by Weber, entitled respectively Science as a Vocation and Politics as a Vocation. In them Weber discusses what problems you have to face, and what personality and character you have to own, if you decide to make these fields your calling, and he’s surprisingly thoughtful and yet practical about it.I thought it would be interesting to begin to think about the same questions with respect to entering the field of Quantitative Finance, particularly from a practitioner’s point of view.

Financial “Engineering“?!
According to Zvi Bodie, financial engineering is the application of science-based mathematical  models to decisions about saving, investing, borrowing, lending, and  managing risk. I think that’s a reasonable definition.

Science – mechanics, electrodynamics, molecular biology, etc., – seeks  to discover the fundamental principles that describe the world, and  is usually reductive and analytic. Engineering is about using those principles, constructively and synthetically, for a purpose.  Thus, mechanical engineering is concerned with building devices based on Newton’s laws, suitably combined  with heuristic or empirical rules about more complex forces (friction, for example) that are too difficult to derive from first principles. Electrical engineering is the study of how to create useful electrical devices based on Maxwell’s equations and solid-state physics, combined with similar heuristics. Similarly, bio-engineering is the art of building prosthetics and other biologically active devices based on the principles of biochemistry, physiology and molecular biology.

So what is financial engineering? In a logically consistent world, financial engineering should be layered above a solid base of financial science. Financial engineering would be the study of how to create functional financial devices – convertible bonds, warrants, synthetic CDOs, etc. – that perform in desired ways, not just at expiration, but throughout their lifetime. That’s what Black-Scholes does – it tells you, under certain assumptions, how to engineer a perfect option from stock and bonds.

But what exactly is financial science?

Canonical financial engineering or quantitative finance rests upon the science of Brownian  motion and other idealizations that, while they capture some of the  essential features of uncertainty, are not finally very accurate descriptions of the characteristic behavior of financial objects. (You should perhaps even object to my use of the word ‘characteristic’ since it’s not clear that financial markets even have time-invariant characteristics.) Markets are filled with anomalies that disagree with standard theories. Stock evolution, to take just one of many examples, isn’t Brownian. We don’t really know what describes its motion. Maybe we never will. And when we try to model stochastic volatility, it’s an order of magnitude vaguer.

So, the point I want to make, for those people who consider coming into the field from one of the hard sciences, is that financial engineering rests on a shaky basis. That’s not to say that it isn’t worth doing. In one sense it makes it more interesting. If you’re going to work in this field, you have to understand that you’re not doing classical science at all, and that the classical scientific approach doesn’t have the unimpeachable value it has in the hard sciences. You have to ask yourself if you can live with that.

The Value Of What You Do Is Often Not Deep
A personal story. In 1985, bond options were the hot new product, the synthetic CDOs of the era. At that time most trading desks used Black-Scholes style models for bond options, in which each bond was treated as a weird kind of stock. Our major theoretical problem was how to consistently model the yield curve and all the bonds that defined them in unison, so that we could then value options on them. Eventually we came up with BDT, which, while it had its problems, was at least self-consistent and perhaps more usable than what came before.

But, surprisingly, it was the new user interface to the model, that I built myself in those primitive pre-mouse days of screen-based programs,  that had the biggest impact. It worked well because I made it do what the traders needed, which I learned from working with them. My new version saved them countless keystrokes compared to the command-line interface that drove the previous FORTRAN version. All my model’s input and output were visible on one screen. There were also fields for storing information about the client and the trade. And best of all, you could save a possible trade under discussion and retrieve it the next day for continued development and discussion.

Though primitive by today’s standards, this interface was astonishingly better than what the desk had used before, and the traders and salespeople were overjoyed. By creating and saving the most common types of option trades as templates in files at the start of each day, they could respond rapidly to clients, accommodating many more requests much more efficiently.

Ever since then, it’s been impossible for me to overlook the difference a simple and well-designed piece of software can make to a business, no matter how good or bad the model underneath.

If you work in this field, then, despite the genuine glories of quantitative finance, you may have to face the fact that you can have the most dramatic effect by improving the ergonomics of trading and sales. Is that fact something you can live with?

Crude and Approximate But Often Useful
Financial Engineering is a multidisciplinary field. It involves financial knowledge, business knowledge, mathematics, statistics, and very importantly, computation, because there’s little you can achieve without computation. There are very few analytic solutions that apply to the markets and products you actually deal with, so you must approximate all the time, and decide what complexities to ignore.

Because of this you need experience to be genuinely useful, and so there are very few young geniuses in the field, unlike mathematics or chess. Experience and some wisdom is often necessary, because you’re dealing with people and their quirks, and a large part of it is a social science. Hard science assumes there is a stable world underlying the observed phenomenon; in social sciences that stability is much less obvious, perhaps even non-existent, because you’re playing with people and they keep changing the rules.

The methodology of quantitative finance is different from large parts of physics or chemistry or even biology. Financial models are crude, and are mostly analogies. They say something like “it may be useful to think that people value bonds by discounting them over the paths of all future short-term interest rates.” This isn’t true, but it’s just possibly useful. In contrast, in physics you can say that the quantum mechanical world behaves as though a particle really does take all future paths to its target, with interfering probabilities. This isn’t merely useful, it’s actually true.

So, if you work as a practitioner, you will have to live with the fact that you are going to have to make crude, false but hopefully useful approximations.

Respect
If you go to work in a big investment bank, you’ll soon discover that traders and salespeople order you about and often make more money but have less technical skills than you. That’s less true today, when more trading is technology and algorithm based, and when products are more complex, and when the buy side offers many different opportunities, but it’s still often the case.

Many practitioners or programmers gets weary after a while, and want to become “one of them.” But they may not have the skill or more importantly the personality to do that. There are more opportunities these days, especially at hedge funds, but nevertheless I’ve seen many people get disillusioned by having to continue in their mainly technical role. Can you change to be what you want? Can you live with being who you are?

Part 2 to follow

Last night I went to see Terrence Malick’s “The Tree of Life.” It’s not a movie, it’s a multiverse, a meditation, a “Koyanisqaatsi” inspired by the Bible rather than by Buddhist texts.

Who the hell are you to question what I do? God said to the people who thought he had punished Job for his (Job’s) bad deeds. It’s about the wonder and the horror, the good and the bad that all exist beyond explanation.

God’s existence, according to one part of the movie, is evident just as much in his turning away from you as in his paying attention to you. It ends in a scene reminiscent of the end of “The White Hotel” (a marvelous book) by D.M. Thomas, with everyone from the past, present and future brought together. If you have religious inclinations, it’s a multidimensional story and meditation on Job and time and connectedness and mysterious fate. If you don’t, it’s the same, and you can still be be entranced by its pantheistic vision, but the background music may irritate you.

The audience was absolutely rapt; not a laugh, not a sigh, not a rustle of candy wrappers or crunching of popcorn. Everyone was silent and just got up and left wordlessly at the end.

I don’t know if I recommend it. It’s such an ambitious and sad movie; Brad Pitt and the two sons tug at your heart in the misery they can’t escape; it made you want to repent and seek forgiveness for all the unhappiness you’ve ever been party to. At yet at the same time, I’m not sure how much will remain in memory. Time will tell.

Many articles lately about protecting your portfolio against “tail risk.”

Tail risk, unfortunately, is not one thing.

Death can be caused by many illnesses. To protect yourself against death successfully for an extended period you may need vaccination against many viruses and a variety of antibiotics against germs. Worse, some of the treatments may be hazardous to your health; many diseases are in fact iatrogenic, caused by the doctors and medicines intended to cure the patient. There is no panacea.

Analogously, the value of a portfolio can be substantially destroyed by more than one cause. Portfolios can be ruined by equity crashes, credit spread widenings, bond defaults, high interest rates, sustained inflation, increases in volatility, illiquidity, etc. To protect your portfolio against so-called tail risk may require spending money on insurance against all these risks, and there is no panacea here either.