Specification Error, Estimation Risk, and Conditional Portfolio Rules

In characterizing the data‐generating process for excess returns, an investor faces both parameter uncertainty (or “estimation risk”) and specification error. We examine the trade‐off between these two effects, in the context of an optimal consumption/portfolio decision problem, by considering a minimal extension of the standard assumption of a linear vector autoregression for excess returns. The key additional assumption in our data‐generating process is a positive linear relationship between market volatility and lagged market dividend yields. This simple specification is consistent with a long sample of U.S. data. We show that volatility adjusted rules are substantially less sensitive to variation in dividend yields, and volatility‐related specification error is economically significant – even when the decisions are based on sample estimates from data sets of a realistic size.