M.Sc. Victoria Voigts

M.Sc. Victoria Voigts
Address
Königsworther Platz 1
30167 Hannover
Building
Room
044
M.Sc. Victoria Voigts
Address
Königsworther Platz 1
30167 Hannover
Building
Room
044

Focal Points in Teaching and Research

  • Asset Pricing
  • Factor Models
  • Machine Learning
  • Climate Finance

Curriculum Vitae

  • Professional background

    since 04/19
    Ph.D. student, Institute of Finance and Commodity Markets (Prof. Dr. Marcel Prokopczuk), Leibniz University Hannover

    seit 04/19
    Managing Director of the Hannover Center of Finance and Insurance e.V.

    11/16 - 02/19
    Student assistant at the institutes of Statistics, Macroeconomics and Microeconomics

  • Education

    10/2017 - 02/2019
    Master of Science - Economics and Management, Leibniz University Hannover, Major: Finance

    03/2018 - 10/2018
    Studies abroad, Macquarie University Sydney, Australia,  Grant by the DAAD

    10/2013 - 09/2017
    Bachelor of Science - Economics and Management, Leibniz University Hannover, Specialization subjects: Econometrics and Statistics, Money and International Financial Industry, Econometric Theory

    09/2015 - 01/2016
    Studies abroad, Universiteit Antwerpen, Grant by the Erasmus-Program

    08/2012 - 05/2013
    Language study abroad in North Devon, UK

    07/2011
    Abitur, Gymnasium Lehrte

Publications and Working Papers

How Robust are Empirical Factor Models to the Choice of Breakpoints?  (with F. Hollstein and M. Prokopczuk; Quarterly Journal of Finance, forthcoming)

SSRN Version

Poster

Abstract:

We comprehensively investigate the robustness of well-known factor models to altered factor formation breakpoints. Deviating from the standard 30th and 70th percentile selection, we use an extensive set of anomaly test portfolios to uncover two main findings: First, there is a trade-off between specification and diversification. More centered breakpoints tend to result in less (idiosyncratic) risk. More extreme sorts lead to greater exposure to the underlying anomalies and thus to higher average returns. Second, the models are robust to varying degrees. The Hou, Xue, and Zhang (2015) model is much more sensitive to changes in breakpoints than the Fama–French models.

Presentations:

  • 2022 Financial Econometrics Summer School of the Society for Financial Econometrics (SoFiE), Brussels
  • 2022 European Meeting of the Financial Management Association (FMA), Lyon;
  • 2022 Meeting of the International Finance and Banking Society (IFBAS), Naples;
  • 2023 Ph.D. Poster Session of the American Finance Association (AFA), New Orleans, LA