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Time Varying Risk Premia

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Decision Technologies for Computational Finance

Part of the book series: Advances in Computational Management Science ((AICM,volume 2))

Abstract

Since the moments of asset returns may contain information about the stochastic discount factor that correspond to a present value model with time-variable discount rates and expected returns, this paper uses Hansen-Jagannathan bounds to estimate valid stochastic discount factors under certain conditions. Thus we differentiate stochastic discount factors estimated on individual asset returns and stochastic discount factors using portfolio returns. The bounds were created using data sets on stock and bond returns for the 1965 – 1995 period. We find that different classes of returns impose different restrictions on the bounds and also on the mean-variance frontier. In order to test if the mean-variance frontier is expanded by the portfolio returns, we create a simple trading system by buying portfolios that lift the Hansen-Jagannathan bound upward. By proceeding we find significant excess returns on this trading strategy implying that these portfolio returns do expand the mean-variance frontier. Thus the underlying present value model leaves residuals since discounted returns are forecastable. A multifactor asset pricing model relates the variation of expected returns to a proxy for future business conditions. Thus we suggest that variations in real investment opportunities cause the variation in expected returns. Since we cannot measure real investment opportunities or related variables without error, multifactor models might better approximate consumption influence on asset pricing. For the parallel time-series and crosssectional analysis we exploit the properties of Ivakhnenko’s ‘Group Method of Data Handling’.

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Authors and Affiliations

  1. Department of Finance and Banking, Universitaet Augsburg, Universitaetsstrasse 16, Augsburg, 86159, Germany

    M. Steiner & S. Schneider

Authors
  1. M. Steiner
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  2. S. Schneider
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Editors and Affiliations

  1. London Business School, UK

    Apostolos-Paul N. Refenes

  2. London Business School, UK

    Andrew N. Burgess

  3. Oregon Graduate Institute, Portland, USA

    John E. Moody

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© 1998 Springer Science+Business Media Dordrecht

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Steiner, M., Schneider, S. (1998). Time Varying Risk Premia. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_3

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  • DOI: https://doi.org/10.1007/978-1-4615-5625-1_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-8309-3

  • Online ISBN: 978-1-4615-5625-1

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