Abstract:
In this paper, we forecast monthly stock returns of eight advanced economies using a time-varying parameter vector autoregressive model (TVP-VAR) with mixture innovations. Compared to standard TVP-VARs, our proposed model automatically detects whether time-variation in the parameters is needed through the introduction of a latent process. This framework is capable of dynamically detecting whether a given regression coefficient is constant or time-varying during distinct time periods. We moreover compare the performance of this model with a wide range of nested alternative time-varying and constant parameter VAR models. Our results indicate that our proposed framework outperforms its competitors in terms of point and density forecasts. A portfolio allocation exercise confirms the superiority of our proposed model. In addition, a copula-based analysis shows that it pays off to adopt a multivariate modeling framework during periods of stress, like the recent financial crisis.
