Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/210064
Authors: 
Pettenuzzo, Davide
Ravazzolo, Francesco
Year of Publication: 
2014
Series/Report no.: 
Working Paper No. 15/2014
Publisher: 
Norges Bank, Oslo
Abstract: 
We propose a novel Bayesian model combination approach where the combination weights depend on the past forecasting performance of the individual models entering the combination through a utility-based objective function. We use this approach in the context of stock return predictability and optimal portfolio decisions, and investigate its forecasting performance relative to a host of existing combination schemes. We find that our method produces markedly more accurate predictions than the existing model combinations, both in terms of statistical and economic measures of out-of-sample predictability. We also investigate the role of our model combination method in the presence of model instabilities, by considering predictive regressions that feature time-varying regression coe cients and stochastic volatility. We find that the gains from using our model combination method increase significantly when we allow for instabilities in the individual models entering the combination.
Subjects: 
Bayesian econometrics
time-varying parameters
model combinations
portfolio choice
JEL: 
C11
C22
G11
G12
Persistent Identifier of the first edition: 
ISBN: 
978-82-7553-835-0
Creative Commons License: 
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.no
Document Type: 
Working Paper
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