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Computing the Substantial-Gain–Loss-Ratio

Author

Listed:
  • Jan Voelzke

    (University of Muenster)

  • Sebastian Mentemeier

    (Universität Kassel)

Abstract

The Substantial-Gain–Loss-Ratio (SGLR) was developed to overcome some drawbacks of the Gain–Loss-Ratio (GLR) as proposed by Bernardo and Ledoit (J Polit Econ 108(1):144–172, 2000). This is achieved by slightly changing the condition for a good-deal, i.e. on the most extreme but at the same time very small part of the state space. As an empirical performance measure the SGLR can naturally handle outliers and is not easily manipulated. Additionally, the robustness of performance is illuminated via so-called $$\beta $$ β -diagrams. In the present paper we propose an algorithm for the computation of the SGLR in empirical applications and discuss its potential usage for theoretical models as well. Finally, we present two exemplary applications of an SGLR-analysis on historic returns.

Suggested Citation

  • Jan Voelzke & Sebastian Mentemeier, 2019. "Computing the Substantial-Gain–Loss-Ratio," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 613-624, August.
    • Handle: RePEc:kap:compec:v:54:y:2019:i:2:d:10.1007_s10614-018-9845-2
    DOI: 10.1007/s10614-018-9845-2
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    References listed on IDEAS

    as
    1. Voelzke, Jan, 2015. "Weakening the Gain–Loss-Ratio measure to make it stronger," Finance Research Letters, Elsevier, vol. 12(C), pages 58-66.
    2. Antonio E. Bernardo & Olivier Ledoit, 2000. "Gain, Loss, and Asset Pricing," Journal of Political Economy, University of Chicago Press, vol. 108(1), pages 144-172, February.
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