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

Publikation: Working paper

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.
OriginalspracheEnglisch
Band1805.12217
PublikationsstatusEingereicht - 30 Mai 2018

Publikationsreihe

NamearXiv

Systematik der Wissenschaftszweige 2012

  • 502 Wirtschaftswissenschaften

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