Portfolio Risk Analysis

3/23/2010 - In the investment world, timing is everything. You want to buy assets low before the rest of the world figures out that those assets are cheap, and sell them for a premium before the market realizes they may be overpriced.

It turns out that timing is also key to a less-flashy, often-overlooked subset of investing: the discipline of risk analysis. In normal times, a book-length treatment of risk analysis might not make much of a splash. But with the burst of the housing market bubble, the chaos in the stock markets, and the failures and bailouts of Wall Street entities long considered “safe,” risk analysis has emerged as a hot topic once again.

“Given the facts of the last couple of years, risk management is something people should be thinking about more, rather than less,” says Robert Korajczyk, the Harry G. Guthmann Professor of Finance, director of the Zell Center for Risk Research and co-author of Portfolio Risk Analysis, published recently by Princeton University Press. “Risk models can fail, and certain traditional ways of measuring risk can underestimate what your risks really are.”

In their 300-plus-page text, Korajczyk and his co-authors — former Kellogg professor Gregory Connor, now at the National University of Ireland, and Lisa Goldberg, executive director of analytic initiatives at MSCI Barra — take a multi-disciplinary approach to risk analysis. Their approach, occasionally seen in less-technical treatments but rarely used in an academic work, culls models from finance, statistics, macroeconomics, microeconomics, and even behavioral psychology.

“A lot of risk management books tend to focus on the analytical and math parts,” Korajczyk says. “We try to dig down into the data to show how real numbers can change actual behavior, like how economic shocks change liquidity, or the kind of increase in estimated risk you get in alternative investments if you adjust your risk estimates for biases.”

While heavily driven by data and formulas, Korajczyk and his co-authors have tried to use plain English and real-world examples to explain their risk-modeling approach.

“This is not ‘Risk Management for Dummies,’ but we understand that a lot of potential readers who work in markets are not Ph.D.’s,” Korajczyk says. “We’ve tried to assume a certain level of sophistication, while bearing in mind that you can’t just do equation after equation without setting up a framework for what you’re trying to achieve and establishing why it’s useful.”