Large-scale portfolio allocation under transaction costs and model uncertainty

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Standard

Large-scale portfolio allocation under transaction costs and model uncertainty. / Hautsch, Nikolaus; Voigt, Stefan.

I: Journal of Econometrics, Bind 212, Nr. 1, 09.2019, s. 221-240.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Hautsch, N & Voigt, S 2019, 'Large-scale portfolio allocation under transaction costs and model uncertainty', Journal of Econometrics, bind 212, nr. 1, s. 221-240. https://doi.org/10.1016/j.jeconom.2019.04.028

APA

Hautsch, N., & Voigt, S. (2019). Large-scale portfolio allocation under transaction costs and model uncertainty. Journal of Econometrics, 212(1), 221-240. https://doi.org/10.1016/j.jeconom.2019.04.028

Vancouver

Hautsch N, Voigt S. Large-scale portfolio allocation under transaction costs and model uncertainty. Journal of Econometrics. 2019 sep.;212(1):221-240. https://doi.org/10.1016/j.jeconom.2019.04.028

Author

Hautsch, Nikolaus ; Voigt, Stefan. / Large-scale portfolio allocation under transaction costs and model uncertainty. I: Journal of Econometrics. 2019 ; Bind 212, Nr. 1. s. 221-240.

Bibtex

@article{ad86924a4d7b4e94a99e3605edb13916,
title = "Large-scale portfolio allocation under transaction costs and model uncertainty",
abstract = "We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized versions of efficient allocations. The regulatory effect of transaction costs is studied in an econometric setting incorporating parameter uncertainty and optimally combining predictive distributions resulting from high-frequency and low-frequency data. In an extensive empirical study, we illustrate that turnover penalization is more effective than commonly employed shrinkage methods and is crucial in order to construct empirically well-performing portfolios.",
keywords = "High frequency data, Model uncertainty, Portfolio choice, Regularization, Transaction costs",
author = "Nikolaus Hautsch and Stefan Voigt",
year = "2019",
month = sep,
doi = "10.1016/j.jeconom.2019.04.028",
language = "English",
volume = "212",
pages = "221--240",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Large-scale portfolio allocation under transaction costs and model uncertainty

AU - Hautsch, Nikolaus

AU - Voigt, Stefan

PY - 2019/9

Y1 - 2019/9

N2 - We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized versions of efficient allocations. The regulatory effect of transaction costs is studied in an econometric setting incorporating parameter uncertainty and optimally combining predictive distributions resulting from high-frequency and low-frequency data. In an extensive empirical study, we illustrate that turnover penalization is more effective than commonly employed shrinkage methods and is crucial in order to construct empirically well-performing portfolios.

AB - We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized versions of efficient allocations. The regulatory effect of transaction costs is studied in an econometric setting incorporating parameter uncertainty and optimally combining predictive distributions resulting from high-frequency and low-frequency data. In an extensive empirical study, we illustrate that turnover penalization is more effective than commonly employed shrinkage methods and is crucial in order to construct empirically well-performing portfolios.

KW - High frequency data

KW - Model uncertainty

KW - Portfolio choice

KW - Regularization

KW - Transaction costs

U2 - 10.1016/j.jeconom.2019.04.028

DO - 10.1016/j.jeconom.2019.04.028

M3 - Journal article

AN - SCOPUS:85063612486

VL - 212

SP - 221

EP - 240

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -

ID: 248550772