Time-Varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies

Abstract

A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian strategy combination is introduced in order to deal with a set of strategies. Our approach extends the mixture of the experts analysis by allowing the strategic weights to be dependent between strategies as well as over time and to further allow for strategy incompleteness. Our approach results in a combination of different portfolio strategies: a model-based and a residual momentum strategy. The estimation of this modeling and strategy approach can be done using an extended and modified version of the forecast combination methodology of Casarin, Grassi, Ravazzolo and Van Dijk (2016). Given the complexity of the non-linear and non-Gaussian model used a new and efficient filter is introduced based on the MitISEM approach by Hoogerheide, Opschoor and Van Dijk (2013).

Using US industry portfolios between 1926M7 and 2015M6 as data, our empirical results indicate that time-varying combinations of flexible models in the FAVAR-SV class and two momentum strategies lead to better return and risk features than very simple and very complex models. Combinations of two strategies help, in particular, to reduce risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk.

Keywords: nonlinear, non-gaussian state space, filters, density combinations, bayesian modeling, equity momentum

JEL Classification: C11, C15, G11, G17