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

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.

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Year of publication:	2017
Authors:	Hautsch, Nikolaus ; Voigt, Stefan
Publisher:	Kiel, Hamburg : ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft

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Series:	Beiträge zur Jahrestagung des Vereins für Socialpolitik 2017: Alternative Geld- und Finanzarchitekturen - Session: Financial Econometrics I ; F14-V3
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Conference Paper
Language:	English
Other identifiers:	hdl:10419/168222 [Handle] RePEc:zbw:vfsc17:168222 [RePEc]
Classification:	C52 - Model Evaluation and Testing ; C11 - Bayesian Analysis ; c58 ; G11 - Portfolio Choice
Source:	EconStor

Persistent link: https://www.econbiz.de/10011712735