Mathematical Finance for Turbulent Times

Summer 2013

Let’s say you’re thinking of buying a power plant. You consider the cost of natural gas, the cost of converting the gas to electricity, and the price you’ll get for the electricity. You weigh the costs of personnel, infrastructure, and equipment. You throw out an offer, hoping it’s halfway reasonable against the randomly shifting market forces controlling all those numbers.

No, there’s not an app for that, but there is an equation, says Helyette Geman, research professor in the Financial Mathematics program, housed within the Department of Applied Mathematics and Statistics. Geman, who is also a professor of finance at Birkbeck, University of London, is internationally renowned in the world of what she calls mathematical finance—a field that applies mathematical principles like probability theory to tools of the financial market trade, like stocks, derivatives, options, and commodities.

“Mathematical modeling allows us to come up with a valuation for the power plant based on options. That’s different from 20 years ago, [when it was] based on standard finance models,” Geman says. “Since we cannot read in a crystal ball, probability becomes our first tool.” A scientific advisor to major financial institutions and insurance, energy, commodity, and mining companies for the last 21 years, Geman literally wrote the book on the field in 2005: Commodities and Commodity Derivatives: Energy, Metals and Agriculturals.

Traditionally, 15 to 18 percent of Hopkins graduates have gone into financial services. The three-semester Financial Mathematics master’s program, which began in fall 2008, is designed to allow students to explore the connection between applied math and finance beyond the undergraduate level, with the explicit goal of preparing them for careers in global financial institutions, government, and corporations, says the program’s executive director, David Audley, PhD ’72, a 20-year Wall Street practitioner.

The curriculum is divided between courses in practice and applied math—covering areas like quantitative portfolio theory, intro to theoretical derivatives, and risk measurement, as well as stochastic calculus and Monte Carlo analysis. Programming and communication skills are also emphasized, and a seminar series weaves it all together. Following two semesters of course work, each class of 20 to 25 students spends a semester interning in the financial industry before returning for its final semester.

The program’s timing has been fortuitous, Audley says: Students have had front-row seats to the credit crisis and the gradual transformation that has followed. New regulations are forcing investment firms to manage risk differently, creating a market for graduates who understand quantitative techniques. And while it may not be obvious at first, those techniques are not so far removed from more traditional engineering branches like mechanical or civil engineering.

“Those typically rely on basic science, solve problems, make things, and do stuff. This is no different—[it’s just that] the fundamental science is all construed by humans,” Audley says, comparing risk management and hedging to the control theory and feedback control used to help a pilot land a plane in turbulence. “The turbulence is like the markets, and we say, ‘Are there financial instruments like the instruments in a plane to get us safely on the ground?’”