In this article, the authors measure and interpret the common 'factors' that describe money market returns. Results are presented for both three- and four-factor models. The authors find that the three-factor model explains, on average, 86 percent of the total variation in most money market returns while the four-factor model explains, on average, 90 percent of this variation. Using mimicking portfolios, they provide an interpretation of the systematic risks represented by these factors.

Continuous-time Markov processes can be characterized conveniently by their infinitesimal generators. For such processes there exist forward and reverse-time generators. We show how to use these generators to construct moment conditions implied by stationary Markov processes. Generalized method of moments estimators and tests can be constructed using these moment conditions. The resulting econometric methods are designed to be applied to discrete-time data obtained by sampling continuous-time Markov processes.