Black-Scholes Options Pricing Model And Financial Economics With Nobel Prize Winner Myron Scholes

>> Jon Hartley: This is the Capitalism and Freedom in the 21st Century podcast, an official podcast of the Hoover Institution Economic Policy Working Group where we talk about economics, markets and public policy. I'm Jon Hartley, your host. Today, my guest is Myron Scholes, who is the Frankie Buck professor of finance Emeritus at the Stanford Graduate School of Business and winner of the 1997 Nobel Prize in Economics for developing the Black Scholes option pricing model.

Welcome, Myron.

>> Myron Scholes: How are you doing, Jon? Nice to be with you today.

>> Jon Hartley: Thanks so much for joining us, it's a real honor to have you on. I really wanna get into your early life. You grew up in Canada like me, and also you were born in Ontario, like me.

You were born in Timmins, which for those who aren't familiar with Timmins, Ontario, it's a mining town. And you later moved to Hamilton, where you grew up as well, I think you're competing with country singer Shania Twain for the most popular person to come on Timmins. I know you did your undergrad at McMaster University in Hamilton, Ontario.

I understand you have a street there named after you. How did you first get interested in economics growing up, a mining town influenced you to study derivatives at all? And maybe a similar way to how growing up in Canada might have influenced, I think, various other Canadian economists.

Specifically, Canadian international economists like Robert Mondell or Harry Johnson and Ron McKinnon? Canada was, I think, the first country to move off the Bretton wood's fixed exchange rate system in the 50s. I think that influenced the Mundell Fleming paper, I mean, how did you get influenced to study economics?

And how did you come to the US to study for your PhD at the University of Chicago, where your advisors were Eugene Fama and Merton Miller?

>> Myron Scholes: Wow, that's a long question, but I'll try to parse it out if I remember all the parts in my advanced age.

I was born in Timoth, Ontario, and as you said, it's 500 miles north of Toronto, and you mentioned that you were born in Oakville. So maybe it was 520 miles north of Oakville, but the interesting part of living in a mining town in a cold and desolate part of Ontario where in the winter the temperature can fall to 40 below.

As you know, it doesn't matter whether that's centigrade, or Fahrenheit as they cross at minus 40. But living in that area in that time when I was young, it became apparent to me that uncertainty or how things change and how nature affects things was very Important. And I always was interested in uncertainty from a very young person, age of a young person, for example, if you live in that type of environment, you never know what is going to happen.

So you understand that uncertainty is not always drawing from the same distribution. It's what's the distribution you're going to draw from next period of time and how is it going to change, etcetera, over time. And so, I became very interested in uncertainty for a very young age, and then relating to that, my mother would be speculating in silver stocks and gold stocks.

And as a young person, I listened to all the stories they had about how to trade these stocks and the like, that obviously there was not many winners in that game. It was when you got in, when you got out, more so than what the fundamental valuations were, that always perked my interest as a young person to take this ideas of uncertainty and the like.

And to marry them together with valuations and how you value under an uncertain regime, the assets that we were holding or potentially investing in. So over time, in my lifetime, not necessarily all in Timmins, but obviously when I moved away and through university and that I wanted to learn how the world addressed uncertainty.

Philosophers addressed uncertainty in a different way. And then, there was historians, and also those who fought wars obviously had to think about uncertainty and how they addressed uncertain outcomes and how they reacted to them. And there was obviously mathematics and statistics and the like and economics that was really thinking about uncertainty.

When I went to McMaster University, because my mother passed away when I was 15 and I lived in Hamlin with my father. He said that he would like me to go to a local university and not go to University of Toronto, or where other people went to, if you were from our area.

And so, I said, yes, I would go, and I graduated from McMaster taking economics and mathematics. But economics was always taught in those days as a certainty model. The model was, this is demand supply, this is how we think about things. But then if there was any uncertainty at all, an error was put on the certainty model and then the error was integrated out.

Because what you said, let's make a prediction, go back to the certainty world. But there was never really any uncertainty, so I was sort of disappointed in that, and then I decided whether to go to Harvard, or to University of Chicago. And I read the works of Galbraith in university and I read works of Friedman, and then a little bit of George Stigler.

And I was pretty impressed with how they addressed economics and how they thought about uncertainty and how to handle that. So I decided that maybe I should go to the University of Chicago. Now, the issue was, what was I interested in? Was I interested in the economics that I had, which was basically in Marshall's world of things, trying to think about telling a beer maker what the effects of prices were gona be on beer sales, or was I interested in applications, a micro positive view?

And I was more interested in a micropositive view, which is telling a beer maker how to make better beer. And so, I decided and where I wanted to go, because I wanted to learn how to take theory and apply it to practice and help people make better decisions, whether they're investment decisions or pricing decisions, or whatever.

And it seemed to me that going to a business school would be better for me than would be going to an economics department.

>> Jon Hartley: I mean, I'm curious, what was that environment like at the University of Chicago when you were a student? I mean, was it a fairly University of Chicago is famously a very iconoclastic, big personality type world.

And Friedman left in I think the 1976 or so, and came to Hoover at that time. And I think that was in part following Aaron Director, I think Aaron Director was actually the first to come out to Hoover from the University of Chicago. But I'm curious what it was like in those days, I mean, this is very early modern finance days prior to the 1950s and 1960s.

My rough understanding is there just wasn't a real. Real serious framework for thinking about how information influences finance. And it was largely, we just had these sort of rough supply and demand type ideas about finance. But really, my understanding is sort of the watershed moments kind of came when the CAPM kind of was written down Bill Sharp and others, along with Harry Markowitz, also UChicago ties.

I mean, when you arrived as a student at UChicago, what had emerged so far in terms of modern finance? Had Modigliani, Miller sort of arrived as well? And I mean, what was sort of percolating in that environment at the time?

>> Myron Scholes: Well, yeah, I mean, you summarized it well, in a sense that Markowitz was really the big bang of financial economics.

Basically, the idea that Markowitz theory, which actually Milton Friedman said was just statistics and not economics, when he tried to pass his PhD thesis and get it approved at the University of Chicago. Really, Friedman had missed the point that yes, statistics was used, but the major contribution of Markowitz was so different from everyone else's view.

That was the idea of correlations, the idea that correlations among assets really determine the portfolio's risk. And that was a great innovation, so it wasn't individual securities, it was the amalgamation of the securities you held that were risk determinant. And basically, even though all of us or people would think about downside risk as being important, and so did Markowitz and his work.

But he said, look, if the distribution of risk is symmetric, the upside risk and the downside risk are the same. We can use volatility or variance as a measure of risk even because basically, it'll go down 20% or go up 20%, it's symmetrical. And so, with that statistical understanding of how to use covariance and variances and expected returns, he developed the efficient set model.

When I got to Chicago at the time Merton Miller had come to Chicago in 1960, I came there first as a student in 62. And Merton came from Carnegie Mellon, having worked with Franco Modigliani to develop the idea of capital structure equilibrium. Because it was felt, prior to Merton and Franco Modigliani's work, that how you finance your activity was determinant what the cost of capital was on investment.

So if you use more debt, it was cheaper than equity, and therefore there would be a level of debt you would use that would reduce your overall cost of capital. Merton Miller and Franco Modigliani said, no, that's ridiculous because economically, if you think about the pie, it's how you're dividing up the pie is not necessarily what you wanna think about it.

What the pie is itself, how the pie is going to grow, and that means that the risk of the underlying investments of the firm are the risk of the assets, and not how they're financed. And they prove that rigorously by arbitrage models and the like, and showed that basically that was true, which was a great innovation.

Obviously, over time, Mert's work and Frankel's work was criticized simply because people thought about bankruptcy costs and other things that would interfere. And Mert's summary was very good, he said, my theory is a little bit like horse and rabbit stew. There's one horse and one rabbit in the stew, and what my ideas are, obviously the horse and all these conundrums and critic of the horse as the stew is the rabbit.

And so, basically, what's important, and it was really a breakthrough, which he was awarded the Nobel Prize for in 1990 with Bill Sharp and Harry Markowitz for the idea of his capital structure research. He was a great guy to work with and I enjoyed working with him as his research assistant.

And when I came to Chicago, maybe mostly because I figured out how to program, no one else program. And so, it was either, I was adopted by the faculty because of my brilliant insights that I had at the time, or I was a computer programmer and no one else knew how to program.

So identification problem there. And then Gene Fama, after looking at work by the Coles Commission and by Harry Roberts, Robert, had come up with the idea of showing that there is more of a random walk in security prices. They didn't necessarily follow a B reverting process or predictive process that you can make money on.

And so that was the genesis of thinking of the efficient markets model, which later on Lucas adopted for rational expectations model and Samuelson had done work in that area as well. And so it was a genesis of what efficient markets were. And Obviously Fama, in 72, when he had his article, which he was awarded the Nobel Prize for, said at the time that to test for market efficiency you need a model of equilibrium.

And then you can judge whether prices deviate from that model of equilibrium. So therefore it's a little bit other than it's a little bit difficult to understand how to move forward from that definition to what is efficiency. And you mentioned Fisher Black, my colleague earlier, and he's always said the market is efficient, but the price of 100, it really could be selling in the market for 150 or it could be selling for $70.

So efficiency is a very broad range of what the prices could be selling for, even though the underlying animal spirits price was really $100. So we had Markowitz, we had Fama, we had Miller doing fundamental research on corporate finance and Fama doing research and thinking a lot about equilibrium and pricing, in a sense.

And then, Bill Schoch came along in 64 and had this capital asset pricing model. So his invention, if you had homogeneous expectations and there was a market portfolio that investors could hold, then you could price assets off of this market portfolio. You can determine what the risk premium should be on particular assets.

So that was an increase in our valuation thinking to go from the idea of a portfolio in Markowitz's case, so you can have your own idiosyncratic portfolio to an equilibrium portfolio. The important point of this is that. And then also Paul Samuelson and Kutner, you had Lintner and Maassine had similar renditions of the capital asset pricing model.

If you assume the Markowitz portfolio assumptions held, what would be the risk of an asset in the optimal portfolio? How would that relate? And obviously, then it gets to covariance, and it gets to volatility again as a way to anchor this. Now, the interesting part about this theory was that.

We started to develop a way to think about what are the theory. And then how can we use the theory to help investors or investors in corporations making investment decisions make better decisions? And we can help them then be active. Unlike Marshall and the beer maker, we can help the beer maker make better beer.

We can talk how the prices will be in the capital markets, we'll talk about how that beermaker can make better investment decisions. So it was a micro positive approach and I was really excited about that because we're trying to address uncertainty in a systematic way. Initially starting off with statistics and thinking about how the theory then would evolve.

And the most important thing is that we started developing databases to really test the theory and see how to do, you know, relative to the theory. The theory is, takes an abstract view of things. How does it work, you know, and how do you empirically look at it and move from there?

>> Jon Hartley: That's fascinating, and it's amazing just how, Eugene Fama, I think, said that, financial economics is by far the most successful branch of economics. And when you think about all these contributions over the past 60, 70 years or so, it's really amazing. Somebody that's worked as a practitioner in finance, all these things seem just so, either now obvious or so central.

And it's amazing how a lot of these ideas are actually somewhat recent in terms of the broader history of economics and finance. People have been trading stocks for hundreds of years as well as options people have been trading for a long time. So I really wanna get into your central contribution, your main contribution, the Black Scholes options pricing model, which in my mind is I think quite possibly the single most impactful contribution of any single economists in the past 50 to 60 years.

You know, in my opinion, if you were to look at the, look at the contributions of all the Nobel Prize and economics winners, I would imagine that that would be very, very highly ranked in terms of just broader influence. So you published with Fisher Black the world famous Black Scholes option pricing formula in a paper titled the Pricing of Options and Corporate Liabilities in 1973.

It was published in the Journal of Political Economy. I'm curious just to start, when did you first meet Fischer Black? I mean for those who don't know, Fisher Black was and he's incredibly brilliant economist whose life was really sadly cut short from cancer in 1995 at age of 57, he was cited in the 1997 Nobel Prize citation.

And I'm sure had he lived a few more years, I'm sure he would have won it alongside yourself and Robert Merton. I mean, he was an MIT Sloan professor for some time and he spent the past 10 years of his career working at Goldman Sachs. Many of the people he worked with I knew in my time there, which was obviously a couple decades after Fisher Black had passed.

But I just remember when you're saying you're the first person to program. The one thing I heard about Fisher Black was he was really the first person to be doing computer programing or to have a computer really at all like Goldman Sachs in I think the fixed income division.

I'm curious, when did you first meet Fisher Black and how exactly did you sell on this path to start exploring options pricing?

>> Myron Scholes: Well, yeah, Fisher Black was a brilliant person. And I was lucky to meet Fisher and get involved with Fisher. And because of the convolution of what I was interested in, what he was interested in, we were able to develop the theory and the model that became known as the Black Scholes model.

Basically I left Chicago to go to MIT as an assistant professor at mit, being shipped off to MIT by Merton Miller and Fama to get more seasoning. I was offered much more money to go to another university in the south of the United States. And I asked Miller whether going to MIT versus this other university was wise because of the discount that I was taking.

And Miller says, no, you're going to MIT, which was proficient and also profound. And so I did go to mit. And then my friend who is a brilliant economist as well, Mike Jensen, who had written a lot of papers and was very important to me at Chicago, as were other colleagues that I had in the PhD program.

As you know, being in a PhD program, you're lucky to have a cohort group that really educate you. And I was really educated by Dick Rolle, by Marshall Bloom, by Michael Jensen, so we can shoot ideas off each other. Because the field was so young, no one really knew anything.

And so the professors were just scholars that really wanted to learn. And Merton always wanted to learn, Gene did, and Mike and others had great ideas as well. So Mike had written this paper on mutual fund performance. One of the few times in which now taking the data and seeing whether Gene's ideas about efficiency were in the mutual fund data, were mutual funds superior performance, neutral performers, or negative performers relative to the benchmark?

And he was the first to show that it looks pretty clear that mutual funds didn't really outperform a naive strategy or a benchmark at that time. Now so he knew Fisher Black because Fisher was working at Arthur D Little. And Fisher had spoken to Mike or heard Mike about the possibly a paper had this paper, and Fisher was asking Mike about his mutual fund performance paper cuz Fisher was doing consulting at RTH Little on mutual fund performance and the like.

And so I had taken a sidetrack to California, to San Francisco on a project for Wells Fargo bank before I went to MIT. So I left in June, and then I had three or four weeks in San Francisco before I ended up at MIT as an assistant professor.

My job was to evaluate for the management science department at Wells Fargo bank as to whether their implementation of the Markowitz portfolio theory was a direction that was good or bad or how they can change it to make it better. And so I suggested, after looking at what they had done, that they had developed the engine, the efficient market, the efficient set engine, but had no inputs to put in the engine and no clients to use any output from the engine.

So I suggested that maybe they should forget all this stuff because they had no inputs. And knowing Mike's work and then genes thinking about efficient markets, I suggested to them they abandon all that and they start with having. Passive portfolios, going to passive investment strategies, or later became known as index funds or ETFs, or whatever you wanna call them.

And so, I wrote the report, took the plane trip to Boston and got housed at MIT and started doing teaching and research. And Mike Jensen had suggested that I meet Fisher Black for lunch. And I called Fisher Black up, we had lunch together, we hit it off, it was an embryonic field.

And I was enjoying talking to him about his ideas, he enjoying talking to me about my ideas. So we continue to get to know each other through lunches that we had between when I arrived at MIT and that Christmas. And then at Christmas time, the Wells Fargo people, Mac McCrown, called me up and said, we reread your work, your paper on passive investment strategies, we like to move forward and implement those or see if we could implement them.

So I said, well, I'm an assistant professor here at MIT and I have this colleague, Fisher Black, who is thinking about setting up his own firm and leaving Arthur Little, and he could travel to California. I can't, because I'm teaching and have all these responsibilities here. So would that be all right if you met him and me together?

And so we went to Wells Fargo and started a project together, which Fisher and I wrote fundamental research papers looking at risk and return. And with Mike Jensen as a colleague. And so we showed basically that the old form of the capital asset pricing model didn't seem to explain returns very readily.

And Fisher and I wrote some papers together. But then it was the case that part of our project at MIT was that I was responsible for doing master's theses. Some of my students had options data which I looked at, they were doing research on, and came out with silly answers because they could get the expected terminal value of the option.

It was very hard to figure out how to discount that back to present value. Fisher and I talked about that in addition to our Wells Fargo project, given we're gonna invest in index funds, which didn't seem very skillful. A need for skills that basically, we thought about maybe boosting it up by allowing for options to be part of the strategy, just a side project from the main project.

And so Fisher and I started talking about options and where I had come in my thinking about having the dynamic portfolio or the replicating portfolio. And he had thought about similar things. And we started working together, and we very quickly came to a theory of how to solve the option by setting up the replicating portfolio.

But we tried to think about how to do it for myriad state variables. And even though the theory was correct and could be done, it was basically the state variables would be multiple, and then figure out how to integrate. Once you had a differential equations with all these state variables made it impossible to come to a conclusion or solution quickly.

So then Fisher and I said, well, let's make an assumption, which is false, that the interest rate is constant, and that the volatility is constant. And we got and the option was European, and therefore, we can get a closed form solution. So that we got a closed form solution and that became known as the Black Scholes option pricing model.

The underlying theory was published in the journal Political Economy with the model or given its assumptions. Now we know that every model has an assumption, every model has an error, every model is an incomplete description of reality. How well does the model do in making predictions? And that's the key.

Basically the model has done very well over time. There's a lot of people who say the model doesn't do this, the model doesn't do that, but it does pretty darn great. The model does great, the theory does great, but the theory is based on arbitrage, it's based on an equilibrium, which is far different from anyone else's statement.

They had the theory, they had this, they had this, they got the equation. No, they might have had whatever. They assumed they didn't prove analytically that the riskless rate should be the discount rate, because you can get a replicating portfolio which has zero risk in the limit. And if that has the case that portfolio is arbitrage related.

And that's an equilibrium. So it's a theory, an underlying arbitrage theory which can't be disputed if you believe in the assumptions of the model, and the hedging portfolio assumptions. And so fine, that's how it went. So that then was turned down several times by academic and journals, including the Journal of Political Economy, as being too arcane and not necessarily general enough.

So Fisher and I rewrote the paper and try to show that basically everything in our lives was options and option related. And if you really thought about uncertainty, that options were primary and think about the right to do something, but not the obligation and what the value of that right would be.

>> Jon Hartley: Amazing. And I mean, how difficult of a road was it to get the paper published? How many rejections did you have to go through? I mean, I think this is a pretty famous story of an Extremely brilliant paper that I guess just wasn't apparent to some at that time.

And I guess it sort of speaks to how the diffusion of knowledge kind of works. And I'm curious, like, I know, I think Robert Merton sort of had this rival paper out as well that he, I think, independently produced. I know it's easier.

>> Jon Hartley: Okay.

>> Myron Scholes: No, the idea was that Fisher and I, we're working on a paper and husband their ideas too. I mean, basically you want to make. See how far you can get and where you go. And so you work on your own. And, you know, you don't. You try to keep your work to yourself and not spread it out. When you got. When you're at a stage where you think you got something or you want to get information from others, then you divulge it.

And we had a conference at MIT in 1970, and Fisher Black and I were gonna give our paper for the first time to a consortium of academics who were Franco Modigliani, Jean Merton Miller. And we invited Bob Merton to come to hear the paper. And we disclosed for the first time our technology and the solution, and the Black Scholes model, okay, and what we had found.

There are two parts of it. One is the technology or the theory, and the second is the model, which is the application of the theory to make it usable and to make it so people can understand it easily. And then Bob Merton, who is going to attend, he overslept, and he didn't come to the session at all.

Now, Fisher Black and I knew that he was working on his dynamic multi-period consumption model at the time, which is very important in economics and finance. And when Rob would come into my office and say he was working on that I say, great, Bob, keep working, finish that up, get it published, that's terrific.

And then at same time I was worried, as was Fisher, that since Samuelson and Merton had had this option pricing paper where they used as a discount rate called a utility probability to figure out how to get the expected terminal value back to present value by assuming a pseudo discount rate.

Obviously if you know the final condition, you know today's value, but you don't know today's value. But how do you get it back to present? So they assumed a constant discount rate. When Fisher and I knew the discount rate was always changing depending on the level of the price relative to the exercise price.

So you couldn't go backwards, you had to go forwards. And our model was an inductive model which figured out how to cut a replicating portfolio each period to get rid of the risk of this changing risk. By setting up the right proportion of equities and debt so you could hedge this risk over time perfectly.

And as a result of that you would not have. You can set any discount rate you want. And so it was perfectly hedged, you could set the riskless rate, you could have any risk you want, as long as it was constant risk, then you can figure out a discount rate.

So basically we presented a paper and obviously lots of people don't believe it when you present something. The great thing about science is that people love the middle of the distribution, they love replicating. Replication is very important in our field and people like to dig deeper in there.

But the question is, that's why we call it research. Researching again what other people have done and going more deeply into things, that's research. Sometimes we have to reward search as well, searching in a completely new area. I think what Fisher and I did, we're searching in a new area.

And so even when Bob came into my office a few weeks later and said, I heard you gave this paper and claimed you had a solution to the option pricing model or framework and you had a price option. And he said what was it about? So I explained it to him and then he said no, that he thought it was not correct, incorrect.

And I said well, it is correct, and explained because of the perfect correlation assumption. And a couple of days later as being a great colleague phoned me up and said he has a different proof of it, you know, and using continuous time mathematics, which was terrific, you know, and basically his he, in his view and in my view, having an independent approach to get the solution was crucial and rightly deserving the Nobel Prize in addition to Fisher, Black and myself.

But, you know, the order is to say was not an independent paper, but one that was built on ideas that Fisher and I had, which were not continuous time mathematic. Because the issue was how does the replicating portfolio result? Do you let time get shorter and shorter and move to continuous time or do you start immediately with continuous time?

Fisher and I like the idea of the hedging portfolio because we thought in a real world it was easier for individuals to take a position in options offset by a position in equities and bonds and move that over time. As opposed to assume that anyone can act in continuous time.

So if we went to approximation of continuous time, then we could use portfolio theory or diversification to say if you had any residual risk, it was all diversifiable away. Again, arbitrage would result if you could arbitrage it away.

>> Jon Hartley: I know, I mean the whole, I think discrete time versus continuous time differences in styles, I think still persist.

And some people that say that they're continuous time people and very fond of that and think it's better than in discrete time. And I know that some of that still persists today. And I know the proofs of, I think, Black-Scholes have also gotten. There have been successive attempts to prove it using I think Ito's lemma and otherwise.

>> Myron Scholes: Yeah, Merton used Ito's lemma. But once you know how to go through the woods, okay, then there is a path to get out of the woods. You're gonna find many paths that get you out of the woods. But the brilliance was initially to think about it, who knew how to get out of the bamboo forest?

>> Jon Hartley: Right, and ultimately to use tools like yourself and Professor Black did that are very similar to solving a heat equation. Solving a partial differential equation that's very much like a heat equation in dynamics.

>> Myron Scholes: Heat transfer equation with constant parameters, yeah. I was asked, go ahead.

>> Jon Hartley: I wanna talk a little bit about the impact of just Black-Scholes, because you've really seen it since its origin and see it take off.

I'm curious, some people out there, I mean, there are some critics, I don't know, Nassim Taleb and others have maybe said things that Black-Scholes isn't such a big deal. Really sort of cast models are already existing. I mean, some people like Thorpe claim that they had come up with it earlier in the 1960s and some now I think say they use tree models instead.

I mean, I think what people don't really realize is that, you know, people who are trading options prior to Black Scholes really had no idea how to price options and they were sort of this new thing and you know, people just didn't really have a sense of how to really systematically price them.

And if you had the Black-Scholes model say in the 1960s, you could have done really, really well just using it to exploit sort of mispricing that was going on in the 1960s. I'm curious, when sort of Black-Scholes was released into the world, what was your sort of experience in terms of seeing its popularity just astronomically rise in finance?

>> Myron Scholes: Well, there was two dimensions of this or many dimensions, but I mentioned two. One is at the time the Black-Scholes model was published was coincident with the birth of the first listed options trading in the Chicago Board Options Exchange in Chicago. So there was 16 options were traded on calls, call options at that time on 16 securities.

That was in 1973. Then it was the case that there was the old grizzly traders who thought they had the experience from the over the counter market and the new young turks who were going to be market makers and trade on the floor of the Chicago Board Options Exchange.

So here's an idea with experience only and intuition versus a model. And the young guys had the model and then they would either have it. Fisher Black made sheets of paper which talked about the Delta and the pricing at different levels of the stock price relative to the exercise price.

And they could look at the sheets. And there was a war between the grizzly intuition people and the model people, the young turks who had no intuition, but they had the model. And in a matter of about six months or so, the young turks had wiped out the grizzlies, okay, the intuition people.

Cuz intuition is a model, but it's an incomplete description of reality. All our intuition is a model, cuz you can do it, but it has a big error to it. So if you could use a model like the The Black Scholes model, it reduces the error and set up an ability to hedge at that time.

And I work with some of the banks who would finance the dealers and built that whole risk management system for them, which would be able to monitor when their traders who were there financing were getting out of kilter. And they were supposed to make money by being an intermediary, being an agent, and book these things, but not necessarily make money by taking huge positions themselves.

So are you making money turning over the inventory? Are you making money holding the inventory? Many market makers think they can make money by doing both. But it's much easier to make money by turning over inventory if you have buyers and sellers than it is to make money holding inventory.

And so we at the time, the options model was taken into the fold by so many traders on exchanges. But then by 1980, investment banks realized that's the Solomon Brothers and Goldman Sachs and other banks realized that they were just an agency business. if they had bonds, insurance companies would wanna buy their bonds issued by companies and other pension funds.

And so, most of the desks that were at Goldman Sachs or at JP Morgan or other of the investment banks, such as Solomon, would be people with phones that the issuer desk would say, okay, we're issuing this bond. And they would pick up the phone and phone insurance company and essentially sell these bond.

So that was an agency model. The same way you go to a car dealer or a real estate agent, they're not going to buy your home from you and then resell it. They're going to act as an agent to do so. So what changed was the advent of how the business, the whole investment banking business went from an agency business to a principal business.

And what I mean by that is with the technology, the Black Scholes technology, they realize this is a mathematical model. We can hire people to come and work with us. And what we can do if a client wants something, we can say, okay, what solution can we provide that client?

How can we hedge it? Or how can we sell off pieces of it that someone else wants so we don't have to sell it as a whole package? We can sell the parts of the car, presumably to someone who wants A and B, and we construct things in a different way.

So, it changed the whole investment banking business and the whole banking business from that of being an agency business to a principal business and moved it as economics has three important components. Can you do something faster, can you do something more individualized? Or you can do something more flexibly and a principal business obviously can do something fast because if a corporation like IBM wanted something, then the bank can say, okay, it's yours, okay, we'll give it to you.

And then basically they now have what they want and now they have to repackage it and sell it off to other pieces people and keep pieces themselves and hedge that in the market. And so that does it faster. They can create a more individualized solution for IBM. And if IBM wants to change their mind later on, they don't have to worry about calling in the big bond.

They can decide how to repackage it to make it IBM suitable. So, efficiency and they can do it at less cost. And that set the whole business in a different direction, which made the power of the option pricing technology even richer, okay? And how you address investment decisions, financing decisions and it made the world alive.

As opposed to a static theory. In Markowitz's world you had a portfolio. How'd you do it? Sharp's world is a measurement. My world, the same thing was outside looking in, not being inside looking out. So basically in everything we do is in life is there's architecture. I was involved in architecture specifications, which is proving things specification.

Then there's the implementation side. How do you implement things to do things better and more efficiently? Because you learn from the implementation. So, the theory imports and supports innovation. And so the implementation is very important part of the innovation and the architecture that one builds.

>> Jon Hartley: I mean, it's amazing.

What impresses me so much about your career is that your academic work, I mean you've been at Stanford since the early 1980s. Really just how much it translates directly into practice in financial markets. And you've had an amazing private sector career as well at Solomon Brothers Long Term Capital Management, Platinum Grove Asset Management and now Janice Henderson.

I want to talk just a little bit about financial regulation. You know, I think some people wrongly claim that, you know, Black Scholes was, you know, responsible for the growth of derivatives that were central to the global financial crisis of 2008. Exotic mortgage-backed securities and CDO squared are I think pretty different than European call options.

I'm just curious. And a lot of people have point a lot of things. There's one genius failed this whole book about long term capital management. I mean you're really just, I think an academic advisor there. And so, I think, and those were, there's a whole illiquidity crisis that was born out of taking too much risk in on the runoff the run trades and with too much leverage.

I'm just curious, do you have any thoughts on financial regulation and where we're at now? Roughly over 15 years after, after Dodd Frank, I mean, there are still a lot of questions out there, I think, about counterparty risk. Some mandated central clearing of derivative securities has resulted from Dodd Frank.

Obviously there is, on the part of banking regulation. There's been a lot of changes in terms of capital standards, capital requirements. I'm curious on the security side of things, there's so many proposals to centrally clear treasuries is something that Daryl Duffy is often talked about. Curious, what are your thoughts on financial regulation in general and how has your own thinking been informed by working as a practitioner while also having a strong foothold in academia?

>> Myron Scholes: Yeah, I mean it's involved. I always think that innovation under uncertainty, again, under certainty. Don't forget you have a lot of, a lot of noise in the system and that the more uncertainty you have, you have AI and AI has a lot of uncertainty and the more uncertainty comes in is that there's a model underlying uncertainty.

And then cheaters figure out how to come into the system to game the system and create things that are to their advantage and not necessarily to those who are the client of those systems. So, innovation means that you're gonna have uncertainty because why do you have an innovation always has to lead infrastructure.

That's what I've learned over time, that you can't put the ordinance troops up in the front line and have the warriors come second. We did that maybe in leaving Afghanistan where we pulled the warriors out first and then tried to get the women and children out afterwards. But even as a young man in.

In Canada, when I watch western movies, they always send the wagon train out first with the old guy and then left the soldiers to fight. So basically, what happens is you have innovation always has to lead infrastructure, because we don't know what innovations are gonna be successful, and that's life.

Every innovation has failures, every innovation has successes. It was known for certain it would already be done. So therefore, only things that are valuable for society are things that are uncertain and unknowable, okay? And then that means that there's events that people didn't take account of or didn't know about.

And so wait in every corporation or investment bank or everyone else waits to see whether the innovation success or puts all types of regulations on top of new things to see if it can a band aid to see if it works. Over time, as it builds more success, you get more regulation come into play.

So the theory of regulation is following infrastructure follows innovation, okay, and basically innovate. But innovators wanna do things, as I said, faster, more individualized and more flexible. That's make money that way. Governance structures are slow, regularized, and inflexible. So there's conflict. If you have a young child who's two years old, you have many constraints on that child.

You don't let them do very much. But I have children who are now 50 years old and basically I can't constrain them at all. So I got to trust them now. But the friction at the margin comes in when the innovator is trying to do things faster, more individualized and flexible, and the regulator is trying to do things slow, regularized, and inflexible.

So at the margin, everyone's fighting each other, and that's how innovation proceeds over time. As more trust is built, the model is expanded, the cheaters are figured out how to get rid of, then it becomes the norm and goes on from there as though it were something we did and knew all the time, and then the new innovation occurs.

But everything in life that we have to think under uncertainty is not the middle of the distribution, it's the exceptions. The actual tails of the distribution are really crucial. And so the innovator is always thinking about the middle of the distribution. Just as AI is looking at all the history and the previous data and then saying we can explain the future based on that history.

But the history is not how creativity works. Creativity works by using the data mining of the past and seeing when the exceptions are, and the exceptions will create innovation under uncertainty. If you're a researcher and just wanna do what everyone else has done, you're not gonna get anywhere.

So a great researcher knows when there's exceptions and tries to figure out how to use the exception to enhance and build a better field, a better science. Therefore, it's the exceptions, which define everything. Therefore, it's the exceptions, which frustrate regulators, because the regulator can't think of exceptions. The regulator can only think about what the past gives them.

They're just an AI engine that reads the past and then figures out what the middle of the distribution is. And then it's always worried about the tails. All the scenarios that you can have in regulation are only based on the past. They're not based on the new scenarios that are unseen and unknown.

And that's the crazy part, the wonderful part of our lives is the distributions are always changing. That's what I realized over the years. And you have to think about what's the new distribution, what's the new risks that are gonna be out there and how we're gonna handle that.

But most regulators think that the world is an evolutionary process that has no tail events that can occur and has no new risks that are out there. And that creates a stupidity which leads to encumberments, which actually impede growth. And then you get from that that new innovators or new people who see the exceptions of the stupid regulations, the rules, that they'll figure out how to get around the regulations and innovate accordingly.

And over time, the regulations will be destroyed by the new Turks that come in under the new uncertainty, build new models and new ways of doing things, that really change things. Right now, the banks are regulated, they need all this capital. Their business is initiating loans. But unfortunately, the regulations are all based on holding the inventory of the loans they actually innovate, but that's not their business.

They can't make money holding the inventory. They make money turning over the inventory. When you were at Goldman Sachs, basically, you didn't tell you we're gonna make money by holding what you're doing and making money by selling and buying things in the market. That was their business. So basically, that's their business.

That's where their expertise in, they'll figure out what client needs are, figure out solutions for them, and innovate accordingly. So what happened was the government regulators start putting from 0708 on, Dodd-Frank, etc., all these rules that they have to have all this capital in place to handle all these shocks that they don't know are gonna occur or would occur.

And that basically, so then over time, what we see is a whole new business arises. Hedge funds, or pension funds, or others who hold the inventory, that's the most efficient holder of this inventory. The banks are not the efficient holder of the inventory. So now, you're getting it offloaded to all these other entities and some of the regulators regulating the wrong thing, essentially.

And so now, the question for society is what about the shadow Coke banking system? Is that a systemic risk or not? So you define things arbitrarily. First of all, it's not a zero-order process. You don't change something and nothing happens. We change something, it's a multi-order process. And over time, you get a whole new dynamic and whole new evolution.

That's the brilliance of finance and financial innovation is basically how do you do things that satisfy customer needs individually and faster and be more flexible on how you do things. The regulator is taking exactly the opposite pole and basically the regulation is too static. It's not dynamic. It doesn't have any life to it, and that's the problem.

So the only way life occurs for innovation is if you have new innovators come in that do different things or the bank calls itself something else. It doesn't call itself a bank, calls itself a hedge fund, or it calls itself a asset manager, or whatever you wanna call it.

>> Jon Hartley: Absolutely, well, I know you're absolutely right in that regulation is very reactionary and just thinking. But risk being so fundamental to entrepreneurship and technology, there's a set of words that I think has been used recently by Luigi Zingales, and I think Greg Raymer. John says that we're kind of in this era of riskless capitalism where regulators almost don't want there to be risk in any risk at all in the system.

And that there's always this Fed or policymaker put, which I think, interesting to think about how that warps. And well, speaking of innovators, I think there's no one in financial economics who I could say is an innovator like yourself, Myron. And it's a real honor to have you on and to hear about your career and ideas and the history of the Black-Scholes options pricing model and your whole journey.

Thank you so much for joining us today. It has been a real honor.

>> Myron Scholes: You're welcome. Good job, and thank you.

>> Jon Hartley: This is the Capitalism and Freedom in the 21st Century podcast, an official podcast of the Hoover Economic Policy Working Group where we talk about economics, markets, and public policy.

I'm Jon Hartley, your host. Thanks so much for joining us.