Scandinavian Working Papers in Business Administration

Working Papers,
Örebro University, School of Business

No 2021:10: Predicting returns and dividend growth - the role of non-Gaussian innovations

Tamás Kiss (), Stepan Mazur () and Hoang Nguyen ()
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Tamás Kiss: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Stepan Mazur: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Hoang Nguyen: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden

Abstract: In this paper we assess whether exible modelling of innovations impact the predictive performance of the dividend price ratio for returns and dividend growth. Using Bayesian vector autoregressions we allow for stochastic volatility, heavy tails and skewness in the innovations. Our results suggest that point forecasts are barely affected by these features, suggesting that workhorse models on predictability are sufficient. For density forecasts, however, we finnd that stochastic volatility substantially improves the forecasting performance.

Keywords: Bayesian VAR; Dividend Growth Predictability; Predictive Regression; Return Predictability

JEL-codes: C11; C58; G12

14 pages, May 24, 2021

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