Predicting returns and dividend growth - the role of non-Gaussian innovations
Stepan Mazur () and
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Stepan Mazur: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden, https://www.oru.se/english/employee/stepan_mazur
No 2021:10, Working Papers from Örebro University, School of Business
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 (search for similar items in EconPapers)
JEL-codes: C11 C58 G12 (search for similar items in EconPapers)
Pages: 14 pages
New Economics Papers: this item is included in nep-cfn, nep-fmk, nep-for and nep-ore
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Journal Article: Predicting returns and dividend growth — The role of non-Gaussian innovations (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:oruesi:2021_010
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