Nice quote on Derman's blog here:
It always seemed to me, and recent occur[r]ences seem to confirm it, that most algorithmic trading strategies are long volatility but short volatility of volatility.
A previous post from this blog: On the volatility of volatility
WSJ: In 1985, when I left academia and began putting my physics training to work on Wall Street, I talked eagerly about options theory to anyone who would listen. One lunchtime, I turned to a colleague in the elevator and began to babble about "convexity," a mathematical property of options crucial to the Black-Scholes theory used in derivatives pricing. My friend clearly understood convexity, but he shuffled his feet uncomfortably and quickly changed the subject. "Hey, futures dropped more than a handle today!" he said, imitating a genuine bond trader. It didn't take me long to recognize the source of his discomfort: I had just outed him as a fellow quant. Except back then we practitioners of quantitative finance didn't refer to ourselves as quants. That's what "real businesspeople" -- traders, investment bankers, salespeople -- called us, somewhat pejoratively.
Now the term is proudly embraced, as demonstrated by "How I Became a Quant," which collects 25 mini-memoirs of academics who successfully made the jump to Wall Street. Quantitative finance might have lost a little of its luster in recent weeks with the sub-prime mortgage meltdown and its subsequent deleterious consequences for quantitative trading strategies, but quants know -- as many of them in this book emphasize -- that however science- and math-based investment calculations might be, there is still an art to their use and plenty of room for error.
But definitions first. What is a quant, or, rather, quantitative finance? It is an interdisciplinary mix that combines math, statistics, physics-inspired models and computer science, all aimed at the valuation and management of portfolios of financial securities. In practice, for example, a quant might be presented with a convertible bond being issued by a corporation and, by extending the Black-Scholes model to convertible securities, calculate its probable value. Or he might develop a quantitative algorithm to buy theoretically cheap stocks and short theoretically rich ones.
By my reckoning, several of the 25 memoirists in "How I Became a Quant" are not true quants, and they are honest (or proud) enough to admit it. But many others are renowned in the quant community. To name just a few: Ron Kahn, co-author of the classic "Active Portfolio Management"; Peter Carr, an options expert at Bloomberg; Cliff Asness, one of the founders of AQR Capital; and Peter Muller, who ran statistical arbitrage at Morgan Stanley.
Most of the book's contributors belong to the first wave of a financial revolution that began in the 1970s, when interest rates soared, listed equity options grew popular and options traders began to rely on the mathematically sophisticated Black-Scholes model. Investment banks needed mathematical talent, and, as the academic job market dried up, physicists needed jobs. Many early quants were therefore physicists, amateurs who had happily entered a field that didn't yet have a name.
Today we are in the middle of a second wave. As markets became increasingly electronic-based, asset and hedge-fund managers began to embrace algorithmic trading strategies -- and started competing to hire quants, hoping to emulate the continuing successes of such firms founded in the 1980s as Renaissance Technologies and D.E. Shaw & Co. The establishment of the International Association of Financial Engineers, co-founded in 1992 by another contributor to this book, Jack Marshall, has further legitimized the field. Nowadays you can pay $30,000 a year or more to get a master's degree in the subject. Financial engineering has become a profession, and amateurs are sadly passé.
Most of the early quants -- in addition to physicists, they included computer scientists, mathematicians and economists -- came to the field by force of circumstance. Even if they had been fortunate enough to find a secure academic position, they often became weary of the isolating academic grind and found that they liked working at investment banks and financial institutions. As former SAC Capital Management quant Neil Chriss notes, Wall Street is no more competitive than academia. Life in finance is often more collegial than college life itself -- and more stimulating. It is impressive how many of the contributors here cite with awe their encounters with the late economist Fischer Black (1938-95), himself a Ph.D. in applied mathematics rather than economics, who always insisted that research on Wall Street was better than research in universities.
The memoirs in this book are not quite representative. That there are only two women contributors is proportionately accurate; most quants were male. But most quants were also foreign-born. When I ran an equity quant group in the 1990s, the great majority -- all with doctorates -- were from Europe, India or China. Only two of the memoirists grew up abroad in non-English-speaking countries. Quants in the second wave are still largely foreign-born, but more are women and fewer hold doctorates.
Several contributors to "How I Became a Quant" stress an essential point: Physics and finance are only superficially similar. While theoretical physics captures the essence of the material world to an accuracy of 10 significant figures, theoretical finance is at best an untrustworthy, limited representation of the mysterious way in which financial value is determined. Yet Thomas Wilson, the chief insurance risk officer of the ING Group, wisely remarks: "A model is always wrong, but not useless." Despite the inadequacies of quantitative finance, we have nothing better. And, on the practical side, Andrew Sterge, the chief executive of AJ Sterge Investment Strategies, writes: "The greatest research in the world does no good if it cannot be implemented."
Quants do get more respect these days, because their imperfect models can generate profits when used with a knowledge of their limitations. But quants can also produce awe-inspiring disasters when they begin to idolize their man-made models. Nevertheless, most quants, unless they have their own operations, are still second-class citizens on Wall Street rather than its superstars, and many still aspire to leave behind bookish mathematics and join the ranks of the "real businesspeople" who used to look down on them.