Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/168222
Authors: 
Hautsch, Nikolaus
Voigt, Stefan
Year of Publication: 
2017
Series/Report no.: 
Beiträge zur Jahrestagung des Vereins für Socialpolitik 2017: Alternative Geld- und Finanzarchitekturen - Session: Financial Econometrics I F14-V3
Abstract: 
We propose a Bayesian sequential learning framework for high-dimensional asset al-locations under model ambiguity and parameter uncertainty. The model is estimated via MCMC methods and allows for a wide range of data sources as inputs. Employing the proposed framework on a large set of NASDAQ-listed stocks, we observe that time-varying mixtures of high- and low-frequency based return predictions significantly improve the out-of-sample portfolio performance.
JEL: 
C52
C11
C58
G11
Document Type: 
Conference Paper

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