We forecast S&P 500 excess returns using a flexible  econometric state space model with non-Gaussian features at several levels. Estimation and prediction are conducted using fully-fledged Bayesian techniques. 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 swings in the latent states even if the amount of shrinkage introduced is high . 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 . Furthermore, a simple trading exercise shows that our framework also fares well when used for investment decisions .
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 .
A
Bayesian
D
Estimation and prediction are conducted using fully-fledged Bayesian techniques.
R
swings
breaks
D
even if the amount of shrinkage introduced is high
D
. Furthermore, a simple trading exercise shows that our framework also fares well when used for investment decisions