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Jaikumar Menon Great read Helped me understand the worlds of academia and investment banking. Financial engineering is a multidisciplinary field involving, methods of, tools of and the practice of. It has also been defined as the application of technical methods, especially from and, in the practice of. It is generally but not always a disparaging term, implying that someone is profiting from paper games at the expense of employees and investors.
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Toggle navigation. Download Size KB. Download Size 42KB. Download Size 68KB. Download Size 81KB. Download Size 34KB. Download Size 28KB. Download Size 20KB. Download Size 16KB. Download Size 52KB. Download Size 19KB. Wall Street began to beckon to them. Mostly, they build models to determine the value of securities.
Buried in investment banks, at hedge funds, or at financial software companies such as Bloomberg or SunGard, they tinker with old models and develop new ones.
And by far the most famous and ubiquitous model in the entire financial world is the Black-Scholes options pricing model. Stocks are commonplace securities, bought and sold daily, but a call option on a stock is much more arcane. The value of the option on that future date when it expires will depend on the prevailing value of a share of IBM. In a sense, the option is a bet that the stock price will rise. During the past twenty years derivative securities have become widely used in the trading of currencies, commodities, bonds, stocks, mortagages, credit, and power.
Derivatives are more intricate than unvarnished stocks or bonds. Then why do they exist? Because derivatives allow clients such as investment banks, money managers, corporations, investors, and speculators to tailor and fine-tune the risk they want to assume or avoid.
This asymmetry between upside gain and downside loss is the defining characteristic of derivatives. You can buy or sell options retail on specialized options exchanges, or you can trade them with wholesalers, that is, the dealers. How, then, do dealers handle the risk they are forced to assume? Dealers are analogous to insurance companies, who are also in the business of managing risk. Neither Allstate nor the options dealer wants to go broke if the insured-against scenario comes to pass.
Because neither Allstate nor the dealer can foretell the future, they both charge a premium for taking on the risks that their clients want to avoid. Unfortunately, this is rarely possible. So instead, the dealer manufactures a similar option. This is where the Black-Scholes model enters the picture. According to Black and Scholes, making options is a lot like making fruit salad, and stock is a little like fruit.
Suppose you want to sell a simple fruit salad of apples and oranges. What should you charge for a one-pound can? Rationally, you should look at the market price of the raw fruit and the cost of canning and distribution, and then figure out the total cost of manufacturing the hybrid mixture from its simpler ingredients.
In , Black and Scholes showed that you can manufacture an IBM option by mixing together some shares of IBM stock and cash, much as you can create the fruit salad by mixing together apples and oranges. Of course, options synthesis is somewhat more complex than making fruit salad, otherwise someone would have discovered it earlier.
Options require constant adjustments to the amount of stock and cash in the mixture as the stock price changes. In fruit salad terms, you might start with 50 percent apples and 50 percent oranges, and then, as apples increase in price, move to 40 percent apples and 60 percent oranges; a similar decrease in the price of apples might dictate a move to 70 percent apples and 30 percent oranges.
The exact recipe you need to follow is generated by the Black-Scholes equation. Its solution, the Black-Scholes formula, tells you the cost of following the recipe. Before Black and Scholes, no one even guessed that you could manufacture an option out of simpler ingredients, and so there was no way to figure out its fair price.
This discovery revolutionized modern finance. With their insight, Black and Scholes made formerly gourmet options into standard fare. Dealers could now manufacture and sell options on all sorts of underlying securities, creating the precise riskiness clients wanted without taking on the risk themselves.
It was as though, in a thirsty world filled with hydrogen and oxygen, someone had finally figured out how to synthesize H2O. Dealers use the Black-Scholes model to manufacture or synthesize, or financially engineer the options they sell to their clients. They construct the option from shares of raw stock they buy in the market. In this way, dealers mitigate their risk. Since the Black-Scholes model is only a model, and since no model in finance is percent correct, it is impossible for them to entirely cancel their risk.
Dealers charge a fee the option premium for this construction and deconstruction, just as chefs at fancy restaurants charge you not only for the raw ingredients but also for the recipes and skills they use, or as couturiers bill you for the materials and talents they employ in creating haute couture dresses.
The last thirty years have seen it applied not just to stock options but to options on just about anything you can think of, from Treasury bonds and foreign exchange to the weather. Behind all these extensions is the same original insight: It is possible to tailor securities with the precise risk desired out of a mix of simpler ingredients using a recipe that specifies how to continually readjust their proportions.
But this is a subtlety—when a new product is first created, a crude Black-Scholes-like model often suffices. Then, an arms race begins. As competitive pressures increase and spreads tighten, quants at different firms refine and extend their first pass at the model, adding new and more accurate descriptions of the motion of the ingredients and obtaining better recipes for the salad. Extending the model demands a grasp of financial theory, mathematics, and computing, and quants work at the intersection of these three disciplines.
The life of a practitioner quant in a trading business is quite different from that of a physicist. When, after years of physics research, I first came to work on Wall Street at the end of , my new boss asked me to take a second pass at a problematic Black-Scholes-like model for bond options that he had built a year earlier.
After several weeks he became impatient with my lack of progress. Of course, the model used more advanced mathematics than arithmetic. Yet his insight was correct. The majority of options dealers make their living by manufacturing the products their clients need as efficiently as they can—that is, by providing service for a fee. For them, a simple, easy-to-understand model is more useful than a better, complicated one.
Too much preoccupation with details that you cannot get right can be a hindrance when you have a large profit margin and you want to complete as many deals as possible. Though I did ultimately improve the model, the traders benefited most from the friendly user interface I programmed into it.
This simple ergonomic change had a far greater impact on their business than the removal of minor inconsistencies; now they could handle many more client requests for business. Although options theory originated in the world of stocks, it is exploited more widely in the fixed-income universe.
Stocks at least at first glance lack mathematical detail—if you own a share of stock you are guaranteed nothing; all you really know is that its price may go up or down. In contrast, fixed-income securities such as bonds are ornate mechanisms that promise to spin off future periodic payments of interest and a final return of principal. This specification of detail makes fixed income a much more numerate business than equities, and one much more amenable to mathematical analysis.
Interest-rate derivatives are naturally attractive products for corporations who, as part of their normal business, must borrow money by issuing bonds whose value changes when interest or exchange rates fluctuate. It is much more challenging to create realistic models of the movement of interest rates, which change in more complex ways than stock prices; interest-rate modeling has thus been the mother of invention in the theory of derivatives for the past twenty years.
It is an area in which quants are ubiquitous. There, most investors are concerned with which stock to buy, a problem on which the advanced mathematics of derivatives can shed little light. Fixed income and equities have fundamentally different foci. When you walk around a frenetic fixed-income trading floor, you hear people shouting out numbers—yields and spreads—over the hoot-andholler; on a busy equities floor, you mostly hear people shouting company names.
Fixed-income trading requires a better grasp of technology and quantitative methods than equities trading. Increasingly, some of them work on statistical arbitrage, the attempt to seek order and predictability in the patterns of past stock price movements and then exploit them—that is, to divine the future from the past.
A decade ago, in , a sudden unexpected rise in global interest rates caused severe losses on many proprietary bond trading desks whose bets turned sour. This led banks to enlarge their previously rudimentary risk management efforts, and caused regulators of the securities industry to focus on risk limitation.
But probabilities are necessarily extracted from past events; they provide notoriously poor estimates of the likelihood of future catastrophes. Market crashes are not randomly occurring lightning bolts; they are the consequence of the madness of crowds who are busy avoiding the last mania as they participate in what will turn out to be the current one. More and more, therefore, the market for quants is in risk monitoring and management.
When I first came to work at Goldman, Sachs and Co. If the colleague you were talking to had been at the firm a little longer than you, then he—most quants are male—would shift uncomfortably and try to change the subject.
Soon, you began to realize, it was bad taste for two consenting adults to talk math or UNIX or C in the company of traders, salespeople, and bankers. People around you averted their gaze. There was something terminally awful about being outed. Even in the mids geeks were fair game. One afternoon a colleague and I were standing on either side of one of the narrow aisles between the banks of trading desks on the floor when one of the chief traders walked between us, his head momentarily between ours.
The force field! Let me out of the way! Traders pride themselves on being tough and forthright while quants are more circumspect and reticent. These differences in personality are reflections of deeper cultural preferences. Traders are paid to act. All day long they watch screens, assimilate economic information, page frantically through spreadsheets, run programs written by quants, enter trades, talk to salespeople and brokers, and punch keys.
In consequence, they learn to be opinionated, visceral, fast-thinking, and decisive, though not always right. They thrive on interruption. Quants do not. Like academics trained in research, they prefer to do one thing from beginning to end, deeply and well. This is a luxury that is difficult to enjoy in the multitasking world of business, where you have to do many things simultaneously.
When I moved to Wall Street, the hardest attitude adjustment for me was to learn to carry out multiple assignments in parallel, to interrupt one urgent and still incomplete task with another more pressing one, to complete that, and then pop the stack. Traders and quants think differently, too. Good traders must be perpetually aware of the threat of change and what it will do to the value of their positions.
Stock options in particular, because of their intrinsic asymmetry, magnify stock price changes and therefore suffer or benefit dramatically from even small moves. Quants think less about future change and more about current value. According to financial theory, at any instant the so-called fair value of a security is an average over the range of all its possible future values.
Fair value and change are therefore two sides of the same coin; the more ways in which a security can lose value from a future market move, the less it should rationally be worth today, and hence the mantra: more risk, more return. Indeed, thinking too much about physics while cycling may prove a hindrance. Similarly, options traders need not be expert quants; they can leave the details of the recipe for manufacturing options to others as long as they have the patience to thoroughly understand how to use it and when to trust it, for no model is perfect.
You cannot just follow formulas, no matter how precise they appear to be. A good quant must be a mixture, too—part trader, part salesperson, part programmer, and part mathematician. Many quants would like to cross over to become traders, but they face the formidable obstacles of scholarly backgrounds, introspective personalities, and hybrid skills. Ostriches are birds, but do not fly in the sky. Both are nonkosher. Similarly, cloth made from a mixture of linen a plant and wool an animal product is also proscribed.
Those who were brought up keeping kosher can feel nauseated at the thought of eating categoryviolating food. Quants are the nonkosher category violators of Wall Street, half-breed players who make pure traders or undiluted information technology managers uncomfortable. Quants are amateurs with no clear professional role model. While traders and programmers in investment banks have distinct ladders to climb and clearly marked rungs to ascend, the quant professional ladder is short and often ends in midair.
Nevertheless, in the twenty-first century, as universities have initiated financial engineering programs and financial institutions have embraced risk management, being a quant has slowly become a more legitimate profession. The overheated tech-stock market of the late s cast a warm, reflected glow on geeks of all types, as did the droves of hedge funds trying to use mathematical models to squeeze dollars out of subtleties. The guts to lose a lot of money carries its own aura.
And indeed, many of the Long Term Capital protagonists are back in business again at new firms. The capacity to wreak destruction with your models provides the ultimate respectability.
Short of genuine enlightenment, nothing but art comes closer to God. When I was a graduate student at Columbia in the s, physics was the great attractor for the aspiring scientists of the world. Bearing witness to this was the large box of documents kept near the entrance to the physics department library. Eccentric though the documents were, they made fascinating reading. There were eager speculations on the nature of space and time, elaborately detailed papers refuting relativity and quantum mechanics, grandiose claims to have unified them, and farfetched meditations that combined physics with more metaphysical topics.
I remember one note that tried to deduce the existence of God from the approximate equality of the solid angles subtended by the sun and the moon when observed from the earth, a remarkable circumstance without which there would be no solar eclipses. None of these papers had much chance of getting past a journal referee. Few of the writers had much hope of even getting into graduate school.
They may not have wanted to. Instead, peering into the box of manuscripts, I always saw my pale reflection. Out there, beyond academia and industry, were people like us, similarly in thrall to the same sense of mystery and power that lay behind the attempt to understand and master the universe with only imagination and symbols. They were cranks, those letter writers, but they were also genuine amateurs, lovers of the field interested in wisdom and magic rather than money.
There are amateurs in the financial modeling world, too, but they often come in more mercenary flavors, and why not? Once every few months I received a note from someone isolated and far away who thought he or she had made some great breakthrough in financial theory. Often, they would explain, it was a breakthrough whose exact details they were unwilling to divulge without being given a contract promising them a share of the future profits they were certain its use would guarantee.
I sympathized with them. They, too, believed in the power of imagination. Theoretical physicists are accustomed to the success of mathematics in formulating the laws of the universe and elaborating their consequences. The universe does indeed seem to run like some splendid Swiss clockwork: We can predict the orbits of planets and the frequency of light emitted by atoms to eight or ten decimal places.
But when a physicist first pages through a graduate economics or finance textbook, he or she begins to feel aghast. The mathematics of economics is so much more formal than the mathematics of physics textbooks—much of it reads like Euclid or set theory, replete with axioms, theorems, and lemmas. You would think that all this formality would produce precision. And yet, compared with physics, economics has so little explanatory or predictive power. Everything looks suspect; questions abound.
When physicists pursue the laws of the universe, it seems selfless. But watching quants pursue sacred laws for the profane production of profit, I sometimes find myself thinking disturbingly of worshippers at a black mass. What does it signify to use the methods of physics and the language of mathematics to model the economic world? Is it justifiable to treat the economy and its markets as a complex machine? How can traders put their faith in this stuff? And how can people be described by equations and predetermined rules?
Is social science, as the economic historian Robert Skidelsky once observed, merely a compendium of flawed thinking disguised as scientific understanding? If mathematics is the Queen of Sciences, is quantitative finance a science at all?
And finally, are quants scientists or cranks? This book is an account of my experiences as a scientist, quant, and, on occasion, a fellow traveler of cranks. Instead, when I arrived on that hot August afternoon in , the city was grimy and littered, disappointingly unmodern.
I was jet-lagged and weary, and the sweaty cab ride from Kennedy airport to upper Manhattan tilted me towards depression. The sickly green-and-white walls in the corridors and the guards at the back entrance added to the prison sensibility. It took several months before habit obscured all of this ugliness. A few hours after stepping off the airplane, I descended into a state of acute loneliness.
It must have had something to do with the sudden perception of distance and time; I had been away from home many times before, but never this far, and never for so undetermined a period. For weeks, verging on months, I walked around with a lump in my throat that threatened to overwhelm me.
The echoes of that first loneliness never totally faded away. I spoke to almost no one during those first few weeks in I. House, which was virtually empty in the quiet lull before classes began.
Ever cautious, I had arrived three weeks early, compulsively planning to settle down and get acclimated before starting my PhD program in physics. Instead, I felt isolated from everyone I had ever known. It is almost impossible today to be as cut off from any place in the world as I was from Cape Town during that first year in New York.
There were almost no telephones in I. House—one extension in a badly soundproofed booth in the corridor served a floor of fifty people. Phone calls to South Africa were expensive and had to be booked in advance through an operator. I never called home; instead, I wrote letters to family and friends several times a week. Finally, mercifully, my first semester at graduate school started. A blind but avid desire for success in physics spurred me to leave Cape Town; simple chance brought me to Columbia.
I had entered the University of Cape Town four years earlier at the age of We were educated in the British style: You had to choose your major area— science, arts, medicine, or commerce—before you began your studies. I chose the natural sciences. There was not much choice of subtopics; you studied everything they chose to teach and then received a grade based on the grand final exam at the end of each year.
Foolishly, the school had permitted me to study only theoretical physics from my second undergraduate year onwards, and so I emerged with no experimental skills. It was a premature specialization that no good American university would have tolerated. In late I suddenly noticed that the more ambitious students in my class were planning to apply to graduate schools abroad. The dermatologist took a benevolent interest in me, and encouraged me to apply to study physics abroad.
I took his advice without really understanding what I was embarking on, and began to apply for scholarships to programs in the United Kingdom and the United States. The Cape Town physics department was insularly lukewarm about the benefits of study abroad, but I did not let them dissuade me.
If not for the acne, I might have remained in South Africa. Even in Cape Town, 5, miles from Europe and civilization, we knew that we were in the glory days of the field. As the s passed, each year brought yet another triumph. At accelerators around the world, experimentalists clashed ultrahigh-speed protons against each other like cymbals and discovered a multiplicity of new particles emerging from the collision. Richard P. Feynman once said that doing elementary particle physics is a lot like banging two fine Swiss watches against each other and trying to figure out their workings by examining the debris.
That was the challenge. The proliferation of new particles made it difficult to know which were elementary and which were compound. The mystery was a recapitulation of the great puzzle of nineteenth-century chemistry, when the similar proliferation of new substances provoked the quest to understand chemical structure.
A few years later he, too, came to graduate school in the United States, where he is now head of the Stanford Linear Accelerator Center, one of the few great global laboratories for experimental particle physics. Now, in the twentieth century, the race was on to find an analogous table for the qualities of so-called elementary particles. So many new ones were being discovered in cosmic rays or man-made colliders that some serious physicists from California, of course began to propound holistic sorts of models in which no particle was more elementary than any other and any particle could be considered a composite of all the rest.
Some of the subtables in their system contained eight distinct particles. Gell-Mann dubbed his model the Eightfold Way, a sophisticated and hip allusion to the eight Buddhist principles of living. Shortly thereafter, exactly as forecast, the particle was created in a collision in the particle accelerator at Brookhaven National Laboratories on Long Island.
It was recognized by the characteristic trail it left in a giant bubble chamber, a signature whose properties matched the exact predictions of the Eightfold Way. It seemed you could apprehend the universe with thought.
I became deeply attracted to particle physics and general relativity, subjects that dealt with the ultimate nature of matter, space and time; a life spent studying these topics would be a life devoted to the transcendental.
Like many of my physics friends, I began to develop an almost religious passion for fundamental physics. But beneath my passion was an even greater desire for fame and immortality. I dreamed of being another Einstein. I wanted to spend my life focusing on the discovery of truths that would live forever.
Sometimes, I felt arrogantly superior to people who were headed for more mundane professions. My mother encouraged me to devote myself to academic pursuits.
My father, though he was more naturally scholarly than my mother, might nevertheless have been happier if I had gone into business with him. I myself would have laughed quite disbelievingly, at age 16, 21, or 34, if someone had told me that I would be working at an investment bank at age Foley, a charmingly cynical man, quizzed me briefly about my knowledge of atomic physics and discovered how little I had learned in Cape Town about the details of the spin-orbit interactions of electrons.
Then he commanded me to register for G, the introductory Columbia graduate course in atomic physics and quantum mechanics. It was a disheartening setback, the beginning of three long, tedious, and unexpected years of coursework and examinations at a time when I had expected to soon embark on original research. In Cape Town in the early s we had learned a shallow rudimentary version of modern physics and quantum mechanics.
The physics professors there, for the most part, seemed uncomfortable with everything that had developed after He also clearly has a passion for explaining theories clearly as opposed to high-brow. Not the most insightful of memoirs, this was more of a catalogue of professional achievements. In the first half, he reveals himself to be an incredibly arrogant physicist.
Full disclosure - in the book , Derman expresses scorn for both solid state physicists and experimentalists -- I myself belong to both of these categories. In the second half which addresses his financial career, his arrogance is not as noticeable, so either he was actually humbled by his change in career, as he describes i I finally finished Emanuel Derman's 'My life as a quant' He is a great story teller. I found the stories around his life in physics much more appealing than his finance life.
The most interesting part to me was the fact that his most interesting contributions came way after he finished his PhD. Also his detailed description of the implied tree model is very intuitive and interesting. Still, he is doesn't shy away from highlighting his weaknesses and struggles throughout his career and that makes A very honest and balanced account of life in academia and industry that doesn't glorify or damn either of them.
It discusses what life is like in Physics and the nature of the problems he got to work on and similarly in Quantitative Finance. Although it could have used some mathematics he did introduce some things and one doesn't expect or desire a textbook so it was pretty good. I would recommend it to anyone considering switching from academia to industry or vice versa. The book is a professional biography of Emanuel Derman. The book starts off more a philosophical text discussing the author's evolution through tertiary education which culminated in three post-doctoral stints at some of the world's top institutions.
This early part of the book has interesting questions and observations such as 'Ambition degradation' ie how ones ambitions degrade with time. The book surpassed my expectations. It provides a detailed look into the life of physics PhDs and professors in late 70s and 80s. When the author switches jobs, to move to Goldman Sachs, we have the opportunity to understand how the quants, physicists and mathematicians, came to set the trend on Wall Street with the invention of new models for trading complex securities, such as options, swaps and other structured products.
It was liberating to reconcile my observations and learnings 8 months into the job of a quant around two decades after Mr. Derman with his own experiences. The fundamentals have not changed much. Basic introduction of the book: The author, Emanuel Derman, is a co-developer of the short rate model — Black-Derman-Toy model.
I read this book with recommendation from a financial engineering program. It is said to be useful for me to understand and accelerate a career in quantitative finance. The first half part of this book An interesting autobiography of Emanuel Derman describing his life in academia PhD and couple of post-docs, particle physics and as a quant on Wall-Street from early 80ies.
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