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Acknowledgments

The quality of this book would definitely not have been possible without the feedback and suggestions of various practitioners – all of whom I have worked with and highly respect. I appreciate the time that they took from their tight schedules to comment on drafts of this manuscript and the online material. These people include Jacques Boudreau, Simon Chan, Pin Chung, Kirk Evans, Dan Heyer, Stefan Jaschke, and Gudmundur Magnusson. I am also very grateful to Dan, who contributed to a few sections of the materials on the website. Despite the valuable feedback, due to time constraints, I could only selectively incorporate some of the comments. As a consequence, I am totally responsible for any lack of clarity or depth in the material and any typos (or errors) that are still present.

Finally, I would like to thank the staff at Wiley Finance for being professional and patient with me throughout this project, despite unforeseen holdups on my end.

CHAPTER 1. Setting the Stage

Since the seminal Black-Scholes and Merton papers in 1973, the derivatives market has exploded by leaps and bounds. Derivatives are now being traded in esoteric asset classes like weather, mortality, credit, and real estate – just to name a few. While reasons for this development can be attributed to a myriad of factors, including taxes, market inefficiencies, creativity in product development, advances in financial modeling, investor sophistication, and so on, it is undeniable that the single biggest motivation for the existence of the current state of affairs in the derivatives market is the existence of sophisticated market participants. This, fueled by a flurry of publications on risk-quantification techniques, led investment banks in the late 1970s and early 1980s to employ mathematicians, physicists, and engineers with PhDs as their in-house rocket scientists or quants or eggheads. Thus began the migration of academics to the lucrative world of finance (who, at that time, were struggling to find decent university positions in their respective fields of mathematics, physics, and engineering). As a consequence, the field of financial economics grew exponentially in mathematical complexity, with practitioners beginning to question the assumptions underlying the Black-Scholes model in the hope of building a more realistic model that would give them a better competitive advantage.

After the 1987 stock market crash and a series of highly publicized derivatives-based bankruptcies in the 1990s, the use of the derivatives as useful risk-management tools has been constantly questioned and criticized. Even the legendary investor Warren Buffett labeled derivatives as the financial weapons of mass destruction. The irony of this undeserving bad press is that many of these losses could have easily happened even when trading cash instruments, since the primary reason for the bulk of these financial disasters has been the lack of proper controls and corporate governance.[1] Despite many of these unfounded criticisms, the derivatives markets has and will continue to flourish as financial markets become more globalized and bankers are constantly looking at innovative ways to strip and repackage risks to provide more effective, efficient, and customized solutions to the hedging-and-speculating clientele.

Given the above backdrop, it is not surprising to see quantitative tools deployed by derivatives practitioners finding their way (over the years) to the quantification of nonfinancial risks, as risk managers try to better understand the interdependence between financial market risks and nonfinancial market risks. The consequence of trying to better quantify and understand this risk interdependence is the ability to optimize the way resources are manipulated and deployed (or allocated) so as to maximize the value to the firm. This also explains the popularity of the growing discipline of Real Options[2] in which quantitative tools borrowed from the financial engineering world are integrated with those from other disciplines (e.g., engineering, actuarial science, manufacturing, or airline operations management) to create an integrated platform that can be used to better assess the impact of the operations' management on financial market risks, which, in turn, impacts the costs and ways the operations are run.

  • [1] There have been numerous examples of such incidences during the past few decades. One example of such an incident occurred in 2008 when Jerome Kerviel (Societe Generale) hid his losses of USD$7 billion. As a consequence of being found guilty, Jerome was handed down a five-year prison sentence in addition to a permanent ban from working in the financial services industry. Another example is Bruno Iksil (also known as the London Whale) from JP Morgan. In this 2012 incident, JP Morgan lost about USD$7 billion.
  • [2] Myers in 1977 used the methods advocated by Black-Scholes and Merton (in their 1973 papers) to quantify corporate liabilities.
 
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