  We forecast S&P 500 excess returns using a flexible Bayesian econometric
state space model with non-Gaussian features at several levels. More precisely,
we control for overparameterization via novel global-local shrinkage priors on
the state innovation variances as well as the time-invariant part of the state
space model. The shrinkage priors are complemented by heavy tailed state
innovations that cater for potential large breaks in the latent states.
Moreover, we allow for leptokurtic stochastic volatility in the observation
equation. The empirical findings indicate that several variants of the proposed
approach outperform typical competitors frequently used in the literature, both
in terms of point and density forecasts.

Econometrics;Statistical Finance;Applications