- London School of Economics
Wednesday, November 16, 2016
Polo Santa Marta, Via Cantarane 24, Room 1.59
We show that low-order autoregression models for short-term expected returns imply long-term dynamics that have a (too) fast vanishing persistence when compared with the evidence from long-horizon predictive regressions. We then propose a novel modeling framework that exploits the low-frequency information in the predictors as a prior to update the high-frequency distribution of expected returns.
Our framework shows that, in order to restore consistency with the empirical evidence from predictive regressions, the short-term dynamics of expected returns need to have long-range dependence. In turn, these long-memory type of dynamics generate first-order effects on forecasting and investment decisions, especially in the long-run. We quantify these effects along several dimensions.
- Programme Director
- Publication date
March 14, 2016