Steven Greiner, the author of Ben Graham Was a Quant, which I reviewed two years ago, is back with a new book, Investment Risk and Uncertainty: Advanced Risk Awareness Techniques for the Intelligent Investor (Wiley, 2013). I suppose Greiner could more accurately be described as editor instead of author; although he has written many of the chapters, either alone or with co-authors, he has also enlisted the expertise of his colleagues at FactSet and FactSet's risk vendors.
The subtitle may describe the intended audience as "the intelligent investor," but this book is really directed at the quantitatively savvy investment professional. Few retail investors, intelligent or not, will either have or require the high-level risk modeling and management skills described in this book.
For those who run large portfolios or who otherwise need to know about a broad spectrum of risk management issues, tools, and practical solutions, Greiner's book is both comprehensive and comprehensible. It is divided into three unequal parts, with the third and shortest highlighting products available from FactSet and fleshing out the many references to FactSet that occur throughout the first two parts.
The first part draws key theoretical distinctions and provides an overview of the field. Its seven chapters include discussions of exposed versus experienced risk, definitions of tractable risk, an introduction to asset class specifics, commodities and currencies, options and interest rate derivatives, measuring asset association and dependence, and risk model construction. The second part zeroes in on fixed income and deals with a range of topics, from interest rate risk and spread risk to portfolio risk measures and portfolio optimization.
Greiner's book does not lend itself to short excerpts, but I'll try nonetheless. First, an abbreviated list of the "many statistics used today to identify, measure, monitor, and mitigate risk": tracking error, portfolio variance, portfolio standard deviation, dollar value at risk (at various confidence intervals and horizons), percent value at risk (at various confidence intervals and horizons), information ratio, Sharpe ratio, systematic risk, and idiosyncratic risk. (p. 68)
And, since we read so much about tracking error (TE), "the most ubiquitous equity risk measure," it might be worth calling attention to one of its obvious shortcomings. TE "calculates the standard deviation of the distribution of portfolio returns versus the benchmark returns. … It says nothing, however, about what the mean of returns is. So it's possible for two portfolios to have two different mean returns and have the same tracking error. In fact, one portfolio could outperform its benchmark and another could underperform its benchmark and yet both portfolios could have the same tracking error." This "yardstick" that was "invented to beat the heads of asset managers with" (p. 63) may thus not be a particularly meaningful stand-alone risk measure.