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at Rutgers Business School. Before starting his career in finance, he studied economics at the American University of Paris and the University of Oxford.

      Joo-Hyung (David) Park has extensive experience in valuation of financial instruments and derivatives. He provides valuation advisory services to corporate and private equity clients for their holdings in nonstandard derivative products. These products include equity options granted to executives, embedded derivatives in convertible bonds, and many other customized fixed income and equity derivatives. Prior to this, he studied financial engineering at Columbia University, and physics at the University of Toronto.

      Chapter 1

      Overview

      ■ Financial models in light of the great financial crisis.

      ■ The difficulties of option valuation.

      ■ An introduction to the volatility smile.

      ■ Financial science and financial engineering.

      ■ The purpose and use of models.

      Introduction

      Our primary aim in this book is to provide the reader with an accessible, not-too-sophisticated introduction to models of the volatility smile. Prior to the 1987 global stock market crash, the Black-Scholes-Merton (BSM) option valuation model seemed to describe option markets reasonably well. After the crash, and ever since, equity index option markets have displayed a volatility smile, an anomaly in blatant disagreement with the BSM model. Since then, quants around the world have labored to extend the model to accommodate this anomaly. Our main focus in this book will be the theory of option valuation, the study of the BSM model and its limitations, and a detailed introduction to the extensions of the BSM model that attempt to rectify its problems. Most of the book is devoted to these topics.

      A secondary motivation for writing this book originates in the great financial crisis of 2007–2008, which began with the collapse of the mortgage collateralized debt obligation (CDO) market, whose structured credit products were valued using financial engineering techniques. When the crisis began, some pundits blamed the practice of financial engineering for the mortgage market's meltdown. Paul Volcker, whose grandson was a financial engineer, wrote the following paragraph as part of an otherwise sensible speech he gave in 2009:

      A year or so ago, my daughter had seen.. some disparaging remarks I had made about financial engineering. She sent it to my grandson, who normally didn't communicate with me very much. He sent me an email, “Grandpa, don't blame it on us! We were just following the orders we were getting from our bosses.” The only thing I could do was send him back an email, “I will not accept the Nuremberg excuse.”

      Comparing financial modelers to Nazi war criminals seems extreme, and indeed, since then, opinions about modelers' responsibility for the financial meltdown have become more nuanced. Spain and Ireland developed housing market bubbles that, unlike those in the United States, were not inflated by complex financially engineered products. Paul Krugman has suggested that the root cause of the crisis lay in the West's rapid withdrawal of capital from Asia after the currency crisis of 1998, leading Asian countries thereafter to concentrate on exporting, saving, and hoarding, which led them to provide cheap credit that fueled speculation. Other competing explanations abound. As with all complex human events, it's impossible to pinpoint a single cause.

      Nevertheless, models did play a part in the development of the crisis. In the face of very low safe yields, badly engineered financial models were indeed used to tempt investors – at times misleadingly and deceptively – into buying structured CDOs that promised optimistically high yields. Though our expertise lies in models for option valuation rather than mortgage securities, we also wanted to write a book that illustrates how to be sensible about model building.

      The Black-Scholes-Merton Model and Its Discontents

      Stephen Ross of MIT, one of the inventors of the binomial option valuation model and the theory of risk-neutral valuation, once wrote: “When judged by its ability to explain the empirical data, option pricing theory is the most successful theory not only in finance, but in all of economics” (Ross 1987). But even this most successful of models is far from being perfect.

      Finance academics tend to think of option valuation as a solved problem, of little current interest. But readers of this book who end up working as practitioners – on options trading desks in equities, fixed income, currencies, or commodities, as risk managers or controllers or model auditors – will find that the valuation of options isn't really a solved problem at all. Financial markets disrespect the traditional BSM formula even while they employ its flawed language to communicate with each other. Practitioners and traders who are responsible for coming up with the prices at which they are willing to trade derivative securities, especially exotic illiquid derivatives, grapple with appropriate valuation every day. They have to figure out how to amend the BSM model to cope with an actual market that violates its assumptions, and they have to keep finding new ways of doing so as the market modifies its behavior based on its experiences.

      In this book we're going to focus on the BSM model and its discontents. In one sense the BSM model is a miracle: It lets you value, in a totally rational way, securities that before its existence had no plausible or defensible theoretical value at all. In the Platonic world of BSM – a world with normally distributed returns, geometric Brownian motion for stock prices, unlimited liquidity, continuous hedging, and no transaction costs – their model provides a method of dynamically synthesizing an option. It's a masterpiece of engineering in an imaginary world that doesn't quite exist, because markets don't obey all of its assumptions. It's a miracle, but it's only a model, and not reality.

      Some of the BSM assumptions are violated in minor ways, some more dramatically. The assumption that you can hedge continuously, at zero transaction cost, is an approximation we can adjust for, as we will illustrate in later chapters. Skilled traders and quants do this with a mix of estimation and intuition every day. You can, for example, heuristically allow for transaction costs by adding some dollars to your option price, or some volatility points to the BSM formula. In that sense the model is robust – you can perturb it from its Platonic view of the world to approximate the messiness of actual markets.

      Other BSM assumptions are violated in more significant ways. For example, stock prices don't actually follow geometric Brownian motion. They can jump, their distributions have fat tails, and their volatility varies unpredictably. Adjusting for these more significant violations is not always easy. We will tackle many of these difficulties in this book.

      In the end, the BSM model sounds so rational, and has such a strong grip on everyone's imagination, that even people who don't believe in its assumptions nevertheless use it to quote prices at which they are willing to trade.

      A Quick Look at the Implied Volatility Smile

The BSM model assumes that a stock's future return volatility is constant, independent of the strike and time to expiration of any option on that stock. Were the model correct, a plot of the implied BSM volatilities for options with the same expiration over a range of strikes would be a flat line. Figure 1.1 shows what three-month equity index implied volatilities looked like before the Black Monday stock market crash of 1987.

Figure 1.1 Representative S&P 500 Implied Volatilities prior to 1987

Prior to the crash, therefore, the BSM model seemed to describe the option market rather well, at least with respect to variation in strikes. Figure 1.2 shows typical three-month implied volatilities after the crash of 1987. Even though all the options used to generate the smile were written on the same underlier, each option had a different implied volatility. This is inconsistent with the BSM model, which assumes that implied volatility is a forecast of actual volatility, for which there can be only one value. You can think of options as metaphorical photographs of the stock's future volatility, taken from different angles

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