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Estimating Beta-Coefficients of German Stock Data: A Non-Parametric Approach

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  • Maik Eisenbeiss
  • Goran Kauermann
  • Willi Semmler

Abstract

Although the consumption based asset pricing theory appears to be theoretically superior and more elegant than the beta pricing model, in practice the beta pricing model is more widely applied. Indeed, beta pricing models are one of the most widely adopted tools in financial analysis. They readily allow handling systematic risk as priced in financial assets. However, accurately estimating beta-coefficients is not as straightforward as implicitly suggested by Sharpe's standard market model, i.e. simply using the ordinary least-squares (OLS) regression. This is primarily because beta-coefficients cannot generally be assumed to be stable over time. In order to overcome this deficiency, we present and apply a non-parametric estimation technique that allows capturing this time effect and promises both more reliable estimates than obtained with an OLS regression as well as better manageability compared with the existing time-series approaches dealing with time-varying beta-coefficients. Estimation results for constant and time-varying betas are presented for portfolios of German industries.

Suggested Citation

  • Maik Eisenbeiss & Goran Kauermann & Willi Semmler, 2007. "Estimating Beta-Coefficients of German Stock Data: A Non-Parametric Approach," The European Journal of Finance, Taylor & Francis Journals, vol. 13(6), pages 503-522.
    • Handle: RePEc:taf:eurjfi:v:13:y:2007:i:6:p:503-522
    DOI: 10.1080/13518470701201405
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    Cited by:

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    3. Willi Semmler, 2011. "Asset Prices, Booms and Recessions," Springer Books, Springer, number 978-3-642-20680-1, September.
    4. Esteban González, María Victoria & Tusell Palmer, Fernando Jorge, 2009. "Predicting Betas: Two new methods," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    5. Alan T. K. Wan & Jinhong You & Riquan Zhang, 2016. "A Seemingly Unrelated Nonparametric Additive Model with Autoregressive Errors," Econometric Reviews, Taylor & Francis Journals, vol. 35(5), pages 894-928, May.
    6. Асатуров К.Г., 2015. "Динамические Модели Систематического Риска: Сравнение На Примере Индийского Фондового Рынка," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 51(4), pages 59-75, октябрь.
    7. Magdalena Mikolajek-Gocejna, 2022. "Systematic Risk of ESG Companies Listed on the Polish Capital Market in 2019-2022," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 597-615.
    8. Christoph Wegener & Tobias Basse, 2019. "The Stability of Factor Sensitivities of German Stock Market Sector Indices: Empirical Evidence and Some Thoughts about Practical Implications," JRFM, MDPI, vol. 12(3), pages 1-10, August.
    9. M. V. Esteban & E. Ferreira & S. Orbe-Mandaluniz, 2015. "Nonparametric methods for estimating and testing for constant betas in asset pricing models," Applied Economics, Taylor & Francis Journals, vol. 47(25), pages 2577-2607, May.

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