Introducing shrinkage in heavy-tailed state space models to predict equity excess returns

TL;DR

This paper introduces a Bayesian state space model with heavy-tailed features and shrinkage priors to improve the prediction of S&P 500 excess returns, addressing overparameterization and large state breaks.

Contribution

It develops a novel Bayesian econometric model with global-local shrinkage priors and heavy-tailed innovations for better financial return forecasts.

Findings

Outperforms traditional models in point forecasts

Provides superior density forecast accuracy

Handles large state breaks effectively

Abstract

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.