Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/244584 
Year of Publication: 
2021
Series/Report no.: 
Working Paper No. 10/2021
Publisher: 
Örebro University School of Business, Örebro
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.
Subjects: 
Bayesian VAR
Dividend Growth Predictability
Predictive Regression
Return Predictability
JEL: 
C11
C58
G12
Document Type: 
Working Paper

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