From http://www.bbc.co.uk/science/horizon/1999/midas_script.shtml with apologies to the BBC for the addition of footnotes.
NARRATOR (DILLY BARLOW): This is the story of a brilliant scientific discovery.
PROF. MERTON MILLER (University of Chicago): When I saw the formula I knew enough about it to know that this is the answer. This solved the ancient problem of risk and return in the stock market. It was recognised by the profession for what it was as a real tour de force.
NARRATOR: A beautiful and elegant mathematical formula which could do something no-one had ever dreamt possible.
STAN JONAS (FIMAT Brokers): Up until the time that they came up with their insight, the world was full of uncertainty and risk. Uncontrollable and un-analysable and then in a moment of tremendous clarity they’d realised that two risky positions taken together can effectively eliminate risk itself.
NARRATOR: But when its creators used the formula to make themselves rich, only then was its dark side revealed.
ROGER LOWENSTEIN (Wall Street Journal): They began to lose 100 million or more day after day after day to finally there was one day when they dropped half a billion dollars, 500 million in a single day and the agony just didn’t end.
NARRATOR: Since the beginning of capitalism there has been one golden rule: that if you want to make money you have to take risks. Then came one of the most improbable scientific projects of the century: the attempt to find a mathematical way to break that rule, to find a formula that would allow anyone to become unimaginably wealthy without taking any risk at all1. If it worked it would bring order to the world’s financial exchanges and challenge the role of one group of people: the traders.
BEN SCHWARTZ (Trader): I’ve seen many people come and go. People come in with a good fresh attitude, but they just can’t survive, they just can’t handle the competition. It’s not for the meek, it’s not for the weak. If you can’t handle it you can’t be there and it’s simple, otherwise people just take your money and they don’t feel bad. Every day when I walk in to the Exchange I walk in with a clear mind, no emotion. The emotion builds up almost like a volcano ready to explode and when I walk in that pit it explodes.
It’s crazy. I communicate by going buy 20 from someone, sell you 20. Constant chaos, it’s constant chaos. The market’s always moving. Yelling, screaming, grabbing, whatever it takes to get people’s attention, to make the market move. Constantly going where am I, where am I, where am I, meaning what is my position, give me an exact total, give me an exact figure, what do I have to do to get out of position.
NARRATOR: The products that are traded in the financial exchanges, like here in Chicago, are strange and complex things, like interest rate swaps and currency derivatives. Their prices are constantly fluctuating as market sentiment shifts. The job of the trader is to try to guess what these prices will be next year, next week or in 10 seconds’ time and do it thousands of times a day. The risks are enormous.
BEN SCHWARTZ: In any one day you can lose 6 figures, millions and millions of dollars exchange hands in these pits every day. Me personally? I’ve lost a lot of money, you know. On any given day, I’m not giving a figure, but you know it’s not... You can lose a car, you know, put in, an apartment, a house, whatever on any given day if you make a simple mistake.
NARRATOR: To work a formula would have to beat the ordinary traders’ instincts. It would also have to compete with the accumulated wisdom of the legendary trader. In his office high above the trading floor is the acknowledged expert.
LEO MELAMED’S CLERK: Yeah.
LEO MELAMED: You see it.
CLERK: It’s 6 even. I want to try 7 even. 6 even is last. NASDAQ is unchanged.
LEO: Right, get me out. I’ll be back, don’t hang up.
NARRATOR: Leo Melamed has been trading successfully for over 30 years.
LEO MELAMED (Chicago Mercantile Exchange): What I do is I pick up the phone to my clerk who is at the pit, she’s at the edge of the pit and she flashes in the order via a hand signal to the broker in the pit who handles my account and he then executes the order that I gave, gives her the confirmation of its execution. All of that is within seconds. Up here it’s kind of still and quiet. I get other kinds of information they don’t get, but I only get what they get which is the screaming and the noise and that noise level changes from time to time and it also gives, provides information as does the, the fear in the eyes of the, of the traders around you. That too is information.
I go in, in the morning and the day is before me and I have to figure out which direction any one of a dozen markets is going to go in. Additionally, you have to figure out from reading the newspapers for, from being a psychiatrist as it were of the public attitude which way they’re going today, what are they going to do, is this, is this information going to make them bullish, this information going to make them fear, and if I can figure that out I can beat the market.
NARRATOR: Traders like Melamed are convinced that success in the markets is all to do with human judgement and intuition, qualities that could never be reduced to a formula. However, this view has powerful opposition. An important group of academics, who study the markets mathematically, believe that such success is largely a matter of - luck.
PROF. ZVI BODIE (Boston University): In flipping a coin if you flip it long enough there may be a long run of heads which doesn’t at all imply that the person flipping it had the ability to make it come up heads. It could just be the luck of the toss.
NARRATOR: This strange view arose from an unexpected discovery. In the 1930s academics decided to find out whether traders really could predict how prices moved. They struggled to find some scientific basis for this belief, but they couldn’t find one, so finally they decided to run a series of experiments. In one of them they simply picked stocks and shares at random. They threw darts at the Wall Street Journal while blindfolded. At the end of the year this random choice out-performed the predictions of top traders. This was a revelation. This meant the prices must themselves be moving totally at random2 and therefore it was impossible, by definition, to predict anything about them. This led the academics to a devastating conclusion: despite all the claims of the traders, it now looked like anyone who managed to make a successful prediction in the stock market must be doing it not by skill but by chance alone.
ZVI BODIE: When some individual made a fortune in the stock market we have a tendency to assume that that was because he knew something, you know after the, and of course the individual himself is happy to reinforce that believe - yes, I was a genius, yeah, or I was very clever, I always said Microsoft was going to make me rich, but what you don’t see are the thousands, hundreds of thousands, perhaps millions of people who are going I always said that ABC company was going to make me rich and ABC company went bust.
MERTON MILLER: If there’s 10,000 people looking at the stocks and trying to pick winners well 1 in 10,000 is going to score, by chance alone, a great coup, and that’s all that’s going on. It’s a game, it’s a chance operation and people think they are doing something purposeful but they’re really not3.
NARRATOR: This attitude disgusted most traders and continues to do so today.
LEO MELAMED: Listen, academics as a rule make terrible traders, so for me to think that I’m going to listen to their theory about trading I beg to differ.
NARRATOR: The discovery of randomness outraged the traders, but it galvanised the academics, for they knew that mathematics had been used successfully to study random, unpredictable phenomena before. Everything from population growth to the weather, so now they began a quest. The equivalent in economics of the race to the Moon to find a scientific and rational way to tame the markets, to use the power of mathematics to conquer risk.
MERTON MILLER: We can deal with random series in a way that to the layman may appear as just chaos, but no, no, no, no, no, once you tell me that the series is random and you’ve got probability distributions we can use some of the apparatus of modern mathematical statistics to do analyses.
PROF. PAUL SAMUELSON (Massachusetts Institute of Technology): The hope that the mathematical theory of probability in statistics could be a skeleton key to help you understand the nature of chance perhaps to predict it better, perhaps to control it, that was born at, at that time and the rest is, as they say, history.
NARRATOR: But it was to be a chequered history. For a long time the academics tried to control risk through probability. By measuring how much prices had moved in the past they calculated the probable range within which they would fluctuate in the future, but after 15 years their predictions were little more accurate than the average weather forecast. What was needed was a much more exact way to ensure someone against risk. In 1955 one academic discovered that someone had already thought of it.
PAUL SAMUELSON: In the early 1950s I was able to locate by chance this unknown book by a French graduate student in 1900 rotting in the library of the University of Paris and when I opened it up it was as if a whole new world was laid out before me. In fact as I was reading it I arranged to get a translation in English4 because I really wanted every precious pearl to be under, understood.
NARRATOR: In 1900 a young French student, Louis Bachelier, set out to do something no-one had ever done before. Using a series of equations he created the first complete mathematical model of the markets. He too realised stock prices moved at random and that it was impossible to make exact predictions about them, but then Bachelier said he had found a solution, a wonderful way to get rid of risk, an obscure, almost magical financial contract called an option. He believed that if someone could discover a formula that would allow this rare contract to be widely used they would be able to tame the markets completely, but he died before he could find it.
PAUL SAMUELSON: After the discovery of Bachelier’s work there suddenly came to the mind of all the eager workers the notion of what the Holy Grail was. There was the next step needed. It was to get the perfect formula to evaluate and to price options.
NARRATOR: The academics returned eagerly to the markets where they began to investigate this strange contract which had so intrigued Bachelier. They discovered that options were, in theory, a miraculous form of financial insurance and they worked in a remarkable way. The risk in the stock market is that if you buy a stock today the price can drop in the future and you could lose money but if you pay for an option contract this gives you the right to wait and buy the stock if it reaches some agreed price in the future, but there’s no obligation. If the stock fails to reach that price you can opt out and you would lose only the cost of the option. In theory options are a perfect way to get rid of risk, but there was a problem. How much would someone pay for such absolute peace of mind? The value seemed to depend on the personal confidence of each investor. No-one could agree on a standardised way to price options. The traders were baffled, but it was exactly the sort of bewildering problem that the academics loved and they attacked it with relish.
Throughout the 1960s the academics developed their mathematical models. They were convinced if they could somehow mathematically describe the emotional confidence of the investors they would crack the problem of how to price options. To do that they kept adding more and more symbols. They added symbols for the level of satisfaction, for reasonableness and aggressiveness. When that didn’t seem to do the trick they added more. Symbols for the guesses of other traders, for defensiveness, for safety. Soon they had a giant mathematical edifice, but the options price seemed as far away as ever.
ZVI BODIE: The mathematical models that were being developed during the 50s and 60s depended on inputs that were completely unobservable in the real world, like expectations of investors which might differ very much from one investor to another and how did you actually come up with a number, how could you come up with a number to input.
STAN JONAS: They would talk about people’s utility structure, they were talking about people’s risk aversion and this made all the models seem more like psychotherapy than real science.
NARRATOR: By the end of the 60s the academics were no nearer to pricing options than they’d ever been, nor any closer to Bachelier’s dream of a perfect mathematical model of the markets. All this was about to change.
PROF. MYRON SCHOLES (Stanford University): From an early age I was very, very fascinated by uncertainty. My parents having lived in a goldmining part of the world, in Northern Canada, would be always buying penny stocks or the family would tend to be buying stocks that had very little prices because there’d be some rumour that there’s be another gold find or a silver find and so the prices would shoot up and, or not shoot up. I mean the family wasn’t seen to be getting rich though. That got me very interested in why was it the case that these prices tend to fluctuate.
NARRATOR: In 1968 Myron Scholes and his colleague Fisher Black set out to tackle the problem of options.
MYRON SCHOLES: When I first discovered options I became very excited about the possibility that here was a contract that enabled you to only be able to take the upside of the returns and not the downside and that being able to take the upside only had value and that was really exciting.
NARRATOR: It was exciting, but a goal that had eluded the greatest minds in economics. They knew that every stock price constantly moved up and down. As it did so the value of an option on a particular stock fluctuated too, but there was no predictable relationship. What they wanted to find was a formula that would obtain the correct price of an option at any moment in time just by knowing the current price of the stock, but they couldn’t see their way through the mass of equations they’d inherited.
MYRON SCHOLES: I read the literature, try to see what others had done and I became dissatisfied with the various models because they had had assumptions that didn’t seem to make that much sense to me.
NARRATOR: But then they decided to try something different. One by one they dropped any symbol which represented something that was unmeasureable. All the things that for years the academics had been busily adding to their equations. It was a brilliant insight. The loss of these elements didn’t affect the calculations at all. All the symbols had been unnecessary. Now for the first time Black and Scholes were left with the bare bones of the problem, the elements which everyone agreed you needed to know to value an option: the stock price, its volatility, the duration of the contract, the interest rate and the level of risk. They could measure all of these things. They were all quantifiable except one - the level of risk.
MYRON SCHOLES: I could do that first part and then I, I, I got stuck.
NARRATOR: So Black and Scholes decided to think laterally. If they couldn’t measure risk exactly perhaps they could somehow make it less significant. They started with the old idea of hedging in which gamblers hedged their bets by betting from the opposite direction. The method they devised was to become one of the most significant discoveries in economics this century5. They created a theoretical portfolio, a mixture of stocks and options. Then whenever either fluctuated up or down they tried to cancel the movement out by making another risky move in the opposite direction. Their aim was to keep the overall value of the portfolio in perfect balance. Since everything moved at random, that was extremely difficult and at first they could only cancel a little of the movement out, but eventually using complex algebra and a mass of calculations they finally found they could balance out a movement precisely and then another.
MYRON SCHOLES: After the fact we called this dynamic hedging, but that means dynamically hedging which you want to be able to eliminate the uncertainty of the movements in the stock.
NARRATOR: They soon discovered that dynamic hedging could balance out any movement at all. They could create a perfect equilibrium in which risks cancel themselves out.
MYRON SCHOLES: We were able to build a portfolio that was essentially riskless6.
NARRATOR: They had found a theoretical way not just to reduce risk, but to eliminate it altogether. By dynamically hedging Black and Scholes were able to remove the last unmeasureable element - risk itself dropped out of their equations. And without risk they finally had a mathematical formula which could give them the price of any option.
STAN JONAS: If I have the proper recipe for dynamically hedging then that position is risk-free. What that means is since I know the price of the stock the only thing that’s left is the price of the option. Unbelievably7 I can fix the price of the option. If I know the price of the stock I now know the price of the option and that is the miracle and the breakthrough of the Black-Scholes price.
NARRATOR: Black and Scholes has solved the problem that had baffled generations of academics. It was a marvellous achievement. But there was a practical problem with their formula. It took time to calculate the dynamic hedging. In the time this took the fast moving markets would have moved on and their calculations would effectively be out-of-date. What was needed was a way to instantly recalculate to keep eliminating the risk continuously. Unbeknownst to Black and Scholes someone had found a way.
ZVI BODIE: He was kind of a, a wunderkind. He was just recognised from the very beginning as an extraordinary intellectual talent. His creative powers with the, the power of the analytical techniques, mathematical techniques that he was bringing to bear on some age-old questions in economics. Savings behaviour, investment behaviour. It was just obvious that here was a guy who was going to make intellectual history in our field.
PROF. ROBERT C. MERTON (Harvard Business School): In college I started studying the stock market. I went down to the stock exchange, watched all the activity from the visitors’ gallery, people running around, calling numbers, shouting and all the paper flying and the bells ringing and of course that was exciting and it seemed to lend itself to my analytical skills.
NARRATOR: By the early 1970s Bob Merton had developed a reputation for using exotic and abstract mathematical ways to study financial contracts like options. He was the perfect person for Black and Scholes to contact.
MYRON SCHOLES: In the fall I went over ideas that we had with Bob and spent a lot of time arguing with him about whether our results were robust and exact, whether there were flaws in our methodology.
ROBERT MERTON: So I’m going to reframe, reformulate their problem in the context of my modelling that I had developed.
NARRATOR: In constructing his own complex mathematical models, Merton had explored theories no-one in finance had even heard of. One was to be the unlikely final piece of the jigsaw. Merton turned to rocket science. He had studied the theories of a Japanese mathematician, Ito, who’d faced a similar problem to Black and Scholes. In order to plot the trajectory of rockets you needed to know exactly where the missile was, not just second by second, but literally all the time. Ito had developed a way of dividing time into infinitely small parcels, smoothing it out until it became a continuum so that the trajectory could be constantly updated. Bob Merton used this idea and adapted it for the Black-Scholes formula. Using the notion of continuous time, the value of the option could be constantly recalculated and risk eliminated continually.
ROBERT MERTON: And then I discovered they were right. By following their procedure, their dynamic trading strategy in the stock and cash at least in the context of my model they could eliminate all of the risk.
MYRON SCHOLES: Bob phoned me up one Saturday morning and said that, you know, he had an alternative proof to our particular model and you know was very, he was convinced that it, that it did work. He had used technology that I hadn’t been aware of and Fischer hadn’t been aware of called Ito calculus to actually, you know, solve the problem in a more elegant way and a more robust way I think than Fischer Black and I had done8.
NARRATOR: The formula that Black, Scholes and Merton unleashed on the world in 1973 was sparse and deceptively simple, yet this lean mathematical shorthand was the fulfilment of a 50-year quest.
MYRON SCHOLES: When we did get the final equation obviously that was eureka.
ROBERT MERTON: This is great. I mean it doesn’t get much better than that. When you solve a problem you really know you’ve cracked it.
NARRATOR: Here was a formula which could, it seemed, get rid of risk in the financial markets9. Academics marvelled at its breathtaking insights and its sheer audacity.
PAUL SAMUELSON: I know and can take my hat off to what their accomplishment was because I, I got near the North Pole, but near is no cigar10.
MERTON MILLER: It exploded. Within a few years it was the most widely cited article in finance, even alas outdoing some of my own articles.
NARRATOR: But barely had the academics time to celebrate their achievement than someone else began to use the formula for real. The traders had never lost belief in their own abilities, but now it seemed the academics had invented something that could complement their intuition. Now option traders simply programmed the Black-Scholes formula into their calculators. By pressing a few buttons they could find the exact price of any option at any time11.
STAN JONAS: Word of the model began to circulate, particularly amongst people in the University of Chicago and more particularly amongst the option traders and literally before the official publication of the model traders had effectively started to programme the model and begin to use it to trade12.
NARRATOR: Soon men and women who had never heard of Bachelier, Ito or continuous time were exploiting the academics formula to make money - lots of it.
GARY LAHEY (Chicago Board of Options Exchange): The computer takes in all the data that I give it during the day. It sends it to, to, (a) it sends it to the little beeping light, it sends it to the CBOE, it sends it to the clearing firm so that the exchange on an on-going basis knows everything I’m doing on line. In addition, I know on line what I’m doing because this thing calculates using a Black-Scholes model what my risk is at all times13.
NARRATOR: Then other traders looked at the Black-Scholes formula and realised it could make them money too. By allowing them to hedge their risks constantly the traders could feel safe enough to conduct business on a scale they had never dreamt possible. The risks in stocks could be hedged against futures, those in futures against currency transactions and all of them hedged against a panoply of new complex financial derivatives, many of which were expressly invented to exploit the Black-Scholes formula.
STAN JONAS: The basic dynamic of the Black-Scholes model is the idea that through dynamic hedging we can eliminate risk, so we have a mathematical argument for trading a lot. What a wonderful thing for exchanges to hear. The more we trade, the better off the society is because the less risk there is. So we have to have more contracts, more futures exchanges, we have to be able to trade the Nikkei futures in Japan, we have to be able to trade options in Germany. Basically in order to reduce risk14 we have to trade everywhere and all the time.
NARRATOR: Capitalism was on the march. The combination of mathematics and money it seemed was unstoppable, and finally, 25 years after they came up with their formula the architects of this revolution received the ultimate accolade.
MYRON SCHOLES: My first reaction on being awarded the Nobel Prize was actually I thought of Fischer Black, my colleague. He unfortunately had passed away. There was no doubt in my mind that if he were still alive he would have been a co-recipient of the Nobel Prize.
ROBERT MERTON: And this is the medal. This is the Nobel medal and on the back of the medal is the insignia for the Royal Academy of Sciences. Now there’s nothing like it, not, not once in a lifetime, but once in many lifetimes.
MAN: Bob Merton. Bob.
NARRATOR: Then at the very height of their reputation they too decided it was time to make money.
ZVI BODIE: I first heard about LTCM from Bob Merton at lunch. He didn’t have a name yet, but he was describing some new contraption, you know be, it would be the analogy of, of a rocket scientist, you know describing some new rocket.
ROBERT MERTON: The idea of building something from scratch, but not a little prototype company, but something that would be quite large and on a global basis. It was too good to pass up.
MYRON SCHOLES: It was for me a way to see the application of ideas to practice.
NARRATOR: In 1995 Myron Scholes and Robert Merton went into business and they did it on a vast scale. Together with a team15they created a giant company, a hedge fund called Long Term Capital Management. LTCM promised to use mathematical modelling to make investors unheard of amounts of money. Merton and Scholes reputation as the greatest academic minds in finance made it easy to raise money. The most prestigious investors, the grandest names in capitalism, banks, pension funds and institutions all competed to invest.
STAN JONAS: It was as though the apostles had effectively come down to raise money in a bingo parlour. This was going to be the team of the century.
ROGER LOWENSTEIN: They went around institutions who were considering investing and this was enormously powerful calling card because many of the people making decisions had studied under Merton and Scholes. They’d all read their books. They felt as if they were meeting the high priests and almost as if they were honoured to be asked to invest with them.
NARRATOR: Within months they had raised $3 billion and were ready to start investing across the globe. They set up not in Wall Street but far away from ordinary traders, in the rich little town of Greenwich in Connecticut. From their headquarters they devised one of the most ambitious investment strategies in history. It was to be shrouded in secrecy. Not even their investors were allowed to know what they were doing. It combined all the academics’ insights. It used probability to bet that key prices would move roughly as they had in the past, but just in case any prediction went wrong they protected themselves by using the key insight of the Black-Scholes formula dynamic hedging. In effect taking out options in the opposite direction. Supremely confident, LTCM placed vast sums of money on the markets.
MYRON SCHOLES: The broad strategy of LTCM was to figure out how to hedge out the risks of your position such that you can do a lot of it much more than you can do if you didn’t hedge out the risks.
NARRATOR: And it worked. LTCM was spectacularly successful, out-performing any other investment company. Merton and Scholes had proved, it seemed, academics could cut it in the real world and they basked in the success.
ROGER LOWENSTEIN: LTCM started out with three truly fabulous years. The first year they made 20% and that was after the partners had collected handsome fees. The second year they returned 43% to their investors, the third year another 41%16.
MERTON MILLER: Ask ‘em what is it you guys are doing at LTCM and Myron characterised it in that way of his. He said well you know one way is to think of us as a gigantic vacuum cleaner sucking up nickels from all over the world.
STAN JONAS: It was as though the world was behaving exactly the way it had been writ on the blackboard. Long Term Capital thought that they had discovered the path to Nirvana. Here they are doing their day-to-day activities, playing golf in lush Greenwich or attending hedge fund conferences in Bermuda, or raising funds in Cannes and then slowly and totally unexpectedly from an area that some probably had never even heard of, certainly one in which they had not invested, a change in the market dynamics began to become apparent17.
NARRATOR: In the summer of 1997 across Thailand property prices collapsed. This sparked a panic which swept through Asia. Banks went bust from Japan to Indonesia and then people took to the streets. These things were so improbable they had never been included in any mathematical models.
BEN SCHWARTZ: Everyone in the marketplace thought the sky was falling and there was instant reaction. The market broke, then rallied, then broke, then rallied. We didn’t know what to believe18.
NARRATOR: As prices leapt and plunged as never before, the models traders used began to give them strange results, so they relied instead on their instincts. In a time of crisis cash is king. Traders stopped borrowing and dropped investments in risky places19.
LEO MELAMED: You’ve got to be able to get out while the getting out is good, but that’s true for any investment you make. That’s true for real estate, that’s true in every sort of business. You can’t ignore a error. Once you realise that you’ve made an error the best thing is to get out of that error and start again, fresh, and that’s what a good trader does.
NARRATOR: But at LTCM their models told them everything would return to normal soon. There was no reason to panic. After all, if any of their bets was going wrong all they needed was another bet in the opposite direction. But as panic spread options cost ever more. Now LTCM did the opposite of the normal trader - they began to borrow and do it heavily. LTCM took on debts of $100 billion.
STAN JONAS: If I have $100 billion position and I lose 1% I’ve lost a billion dollars and if all I have to start out with is $3 billion if I lose 3% on my portfolio in aggregate I’m going to be wiped out20.
NARRATOR: LTCM was just about able to afford the cost of this extra borrowing. They would be able to continue to hedge just as long as one more totally improbable thing did not happen21.
LEO MELAMED: That’s an old market rule: the market will test you and do what you don’t expect it to do.
NARRATOR: In August 1998 something happened no-one had considered possible: the biggest country in the world suddenly and without explanation refused to pay all its international debts and now all the calculations in LTCM’s models were finally, hopelessly out of kilter22.
MYRON SCHOLES: August 1998 after the Russian default, you know all the relations that tended to exist in a recent past seemed to disappear.
MERTON MILLER: Models that they were using not just Bachelier’s models but all kinds of models, were based on normal behaviour in the markets and when the behaviour got wild no models were able to put up with it.
ROGER LOWENSTEIN: Although their models told them that they shouldn’t expect to lose more than 50 million or so on any given day they began to lose 100 million and more day after day after day till finally there was one day 4 days after Russia defaulted when they dropped half a billion dollars, 500 million in a single day.
NARRATOR: In Greenwich LTCM faced bankruptcy, but if the company went down it would also take with it the total value of the positions it held across the globe. These were now staggering. For LTCM’s models had led them to bet a total of a trillion dollars. The equivalent of a year’s turnover of the American government was about to be wiped out. The world’s top financial regulators met in crisis23.
ROGER LOWENSTEIN: Suddenly they seemed to be staring at this nightmare where one firm linked up to every major firm on Wall Street was going to be seized up and markets might just stop working. That was the great fear.
NARRATOR: In order to prevent a global, economic collapse the American Central Bank, the Federal Reserve, had no choice but to organise a bail-out of LTCM. The terms were humiliating. Merton and Scholes lost millions. So did their investors. Among them pension funds, the Central Bank of Italy and Britain’s Barclays Bank lost an average of $200 million each. Then the public recriminations began.
BERNIE SANDERS (Congress Committee Representative): We expect that they’re going to explain to the members of this Committee why the Federal Reserve has organised the $3½ billion bail-out for billionaires, why Americans should be worried about the gambling practices of the Wall Street elite.
ALAN GREENSPAN (Federal Reserve Chairman): How much dependence should be placed on financial modelling which for all its sophistication can get too far ahead of human judgement.
CAROLYN MALONEY (Congress Committee Representative): If the Nobel Prizewinners of Long Term Capital Management didn’t fully understand what they were doing or the risks involved who else could be expected to understand these activities?
ALAN GREENSPAN: Sometimes, and I suspect it was part of the LTCM case, human beings get bedazzled by the people with whom they are working.
PAUL KANJORSKI (Congress Committee Representative): Who’s going to pay these losses that this fund incurred and these investors incurred? Every senior citizen that’s relying on savings account. We just tapped into their funds because these high-flying dude billionaires went at risk.
ROBERT MERTON: It’s like getting hit by a truck. I can’t imagine anyone wouldn’t feel very deep emotions of loss, of why and I’m no different from them.
MYRON SCHOLES: Some people have asked me how I felt going through the LTCM experience and obviously I felt quite bad, badly for, you know, investors, for others who had worked with us for generally because it was the case that we had a great idea and a great franchise, a great application of these ideas to problem-solving and essentially realising that that was very difficult to effect.
NARRATOR: The beautiful Black-Scholes formula continues to be used millions of times a day by traders who know when to trust it and when to turn instead to their own intuition. By people who understand the financial markets are places full of dangers and mysteries, but have not yet been reduced to a scientific explanation24.
STAN JONAS: When do you admit that you’re wrong, start all over again, or when do you hang on and assume that the markets will turn around in your way? That’s the biggest decision we all have to make. However, there’s one thing that’s clear. Over the last several hundred years we’ve been able to identify some people that can do it better than others. They don’t necessarily go to MIT, they don’t necessarily have degrees in mathematics though that doesn’t automatically rule them out. They’re the kind of people that can make that judgement that says something’s different here, I’m going back to harbour until I figure it out. Those are the kind of people you want running your money.
PAUL SAMUELSON: There is a tempting and fatal fascination in mathematics. Albert Einstein warned against it. He said elegance is for tailors, don’t believe in something because it’s a beautiful formula25.
1 Not strictly true, if BS assumptions hold, buying or selling an option will give a normal, risk adjusted (CAPM) return. Of course not all the assumptions hold but the use of the formula itself has greatly contributed to better (more efficient) option prices.
2 This may not be sufficient to prove that prices evolve randomly, but it is certainly strong evidence.
3 Yet markets would not be efficient if traders did not participate at all!
4 James Boness translated it for Cootner (1964), The random character of stock prices, MIT Press, Cambs, MA.
5 The dynamic hedging argument is the real contribution and source of an idea worthy of a Nobel Prize. The formula itself is a mere by product!
6 This implies that in a BS world, the option seller only earns a fair rate of return on capital. In order to make a profit, they have to charge more than BS prices.
7 It was a great surprise that prices did not depend on preferences amongst other things.
8 Merton’s contribution was to implement continuous time finance to produce “closed form” solutions to option problems.
9 Assuming that dynamic hedging is possible and can eliminate all the risk, it quantifies the cost of the hedging strategy.
10 Paul Samuelson, having already won a Nobel Prize in 1970, came quite close to this result that may have won him a second.
11 The continuous time version developed by Merton, was considerably quicker than the original discrete version from Black and Scholes.
12 This made the options market considerably more “efficient”, although it reduced profit margins, it enormously increased volume and therefore total profit in the industry.
13 Even from the outset, traders used the BS formula as a heurisitic to incorporate their own views on volatility for example (even though BS assumes volatility is constant!). Option traders often quote options prices in terms of BS Implied Volatilities.
14 Make risk manageable.
15 Probably the most influential of the team was the legendary ex Salomon’s trader John Meriwether (see Lowenstein’s “When Genius Failed”).
16 These returns cannot really be interpreted without an understanding of the leverage and underlying risk present.
17 LTCM made aggressive use of what trading margin they could negotiate, also broadly speaking they were “short” volatility (were exposed to its increase). This they did by selling options and dynamically hedging away the risk created leading them to become known as “The Central Bank of Volatility”.
18 Volatility had risen.
19 Most traders relied on dynamic hedging less and less. LTCM relied on it more and more, selling even more option positions.
20 This extreme leverage was possibly because of the unique margin requirements LTCM had negotiated with its clearing banks. As LTCMs balance sheet became more stretched, these banks became more nervous.
21 I.e. if volatility did not continue to rise!
22 Implied volatility reached an all time peak; on the S&P500 index, some contracts traded implying annual variability of returns near 45%!
23 Wall Street Banks started to turn against LTCM seeing them as a target rather than a privileged customer.
24 Dynamic hedging is still a bona-fide activity, however there is now a deep understanding that its cost depends on the volatility of volatility (and that this may generate another risk premium)!
25 In summary, the BS formula was just the first, there are already many others with more to come. Each captures a little more of the complex and rich dynamics that are present. Whether or not a complete, all embracing and never to be supplanted model (as in Physics?) will ever exist, is anyone’s guess!