Arbeitspapier
Predicting returns and dividend growth - the role of non-Gaussian innovations
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.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 10/2021
- Classification
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Wirtschaft
Bayesian Analysis: General
Financial Econometrics
Asset Pricing; Trading Volume; Bond Interest Rates
- Subject
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Bayesian VAR
Dividend Growth Predictability
Predictive Regression
Return Predictability
- Event
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Geistige Schöpfung
- (who)
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Kiss, Tamás
Mazur, Stepan
Nguyen, Hoang
- Event
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Veröffentlichung
- (who)
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Örebro University School of Business
- (where)
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Örebro
- (when)
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2021
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Kiss, Tamás
- Mazur, Stepan
- Nguyen, Hoang
- Örebro University School of Business
Time of origin
- 2021
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