Large-Scale Portfolio Allocation Under Transaction Costs and Model Uncertainty: Adaptive Mixing of High- and Low-Frequency Information

Nikolaus Hautsch and Stefan Voigt

VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking from Verein für Socialpolitik / German Economic Association

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-codes: C11 C52 C58 G11 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-ecm, nep-mst, nep-ore and nep-upt
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc17:168222

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