Predicting International Equity Returns: Evidence from Time-Varying Parameter Vector Autoregressive Models

In this paper, we forecast monthly stock returns of eight advanced economies using a time varying parameter vector autoregressive model (TVP-VAR). Compared to standard TVP-VARs, our proposed model automatically detects whether time-variation in the parameters is needed through the introduction of a latent threshold process that is driven by the absolute size of parameter changes. The advantage of this framework is that it can dynamically detect 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 the threshold TVP-VAR outperforms its competitors in terms of point and density forecasts. A portfolio allocation exercise confirms the superiority of our proposed framework. In addition, a copula-based analysis also indicates that it pays off to adopt a multivariate modeling framework, especially during periods of stress, like the recent financial crisis.