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Asset Returns Under Model Uncertainty: Evidence from the Euro Area, the US and the UK

Sousa, João M. ; Sousa, Ricardo M.

Computational economics, 2019-06, Vol.54 (1), p.139-176 [Periódico revisado por pares]

New York: Springer US

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Título:
Asset Returns Under Model Uncertainty: Evidence from the Euro Area, the US and the UK
Autor: Sousa, João M. ; Sousa, Ricardo M.
Assuntos: Autoregressive models ; Bayesian analysis ; Behavioral/Experimental Economics ; Computer Appl. in Social and Behavioral Sciences ; Economic models ; Economic Theory/Quantitative Economics/Mathematical Methods ; Economics ; Economics and Finance ; Empirical analysis ; Euro ; Eurozone ; Forecasting ; Math Applications in Computer Science ; Operations Research/Decision Theory ; Predictive ability ; Risk analysis ; Risk premiums ; Uncertainty
É parte de: Computational economics, 2019-06, Vol.54 (1), p.139-176
Descrição: We analyze predictability of risk premium in the context of model uncertainty. Using data for the euro area, the US and the UK, we show that there is a large amount of model uncertainty and one can improve the forecasts of stock returns with a Bayesian Model Averaging (BMA) approach. The empirical evidence for the euro area suggests that several macroeconomic, financial and macro-financial variables are consistently among the most prominent determinants of risk premium. As for the US, only a few number of predictors play an important role. In the case of the UK, future stock returns are better forecasted by financial variables. These results are corroborated for both the M -open and the M -closed perspectives, different model priors and in the context of “in-sample” and “out-of-sample” forecasting. Finally, we highlight that the predictive ability of the BMA framework is stronger at longer periods, and clearly outperforms the constant expected returns and the autoregressive benchmark models.
Editor: New York: Springer US
Idioma: Inglês

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Asset Returns Under Model Uncertainty: Evidence from the Euro Area, the US and the UK

Sousa, João M. ; Sousa, Ricardo M.

New York: Springer US

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