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Parametric and Nonparametric Estimation of Conditional Return Expectations

In: Risk Management

Author

Listed:
  • Wolfgang Drobetz

    (University of Basel)

  • Daniel Hoechle

    (University of Basel)

Abstract

This paper explores different specifications of conditional return expectations. We compare the most common specification, linear least squares, with nonparametric techniques. Our results indicate that nonparametric regressions capture some nonlinearities in financial data. In-sample forecasts of international stock market returns are improved with nonparametric techniques. However, there is very little out-of-sample prediction power for both linear and nonparametric specifications of conditional expectations. If an asset manager relies on a simple instrumental variable regression framework to forecast stock returns, our results suggest that linear conditional return expectations are a reasonable approximation.

Suggested Citation

  • Wolfgang Drobetz & Daniel Hoechle, 2005. "Parametric and Nonparametric Estimation of Conditional Return Expectations," Springer Books, in: Michael Frenkel & Markus Rudolf & Ulrich Hommel (ed.), Risk Management, edition 0, pages 169-196, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-26993-9_9
    DOI: 10.1007/3-540-26993-2_9
    as

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