Model Uncertainty, Thick Modelling and the predictability of Stock Returns
Number: 221
Year: 2002
Author(s): Marco Aiolfi (Bocconi University) and Carlo Ambrogio Favero (Bocconi University and CEPR)
Recent financial research has provided evidence on the predictability of asset returns. In this paper we consider the results contained in Pesaran-Timmerman(1995), which provided evidence on predictability over the sample 1959-1992. We show that the extension of the sample to the ninetie weakens considerably the statistical and economic significance of the predictability of stock returns based on earlier data. We propose an extension of their framework, based on the explicit consideration of model uncertainty under rich parameterizations for the predictive models.
We propose a novel methodology to deal with model uncertainty based on thick modeling, i.e. on considering a multiplicity of predictive models rather than a single predictive model. We show that portfolio allocations based on a thick modelling strategy sistematically overperforms thin modelling.
We propose a novel methodology to deal with model uncertainty based on thick modeling, i.e. on considering a multiplicity of predictive models rather than a single predictive model. We show that portfolio allocations based on a thick modelling strategy sistematically overperforms thin modelling.