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Computing the Substantial-Gain-Loss-Ratio

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  • Jan Voelzke
  • Sebastian Mentemeier

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 (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 ß-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 SGLRanalysis on historic returns.

Suggested Citation

  • Jan Voelzke & Sebastian Mentemeier, 2017. "Computing the Substantial-Gain-Loss-Ratio," CQE Working Papers 5917, Center for Quantitative Economics (CQE), University of Muenster.
    • Handle: RePEc:cqe:wpaper:5917
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    File URL: https://www.wiwi.uni-muenster.de/cqe/sites/cqe/files/CQE_Paper/cqe_wp_59_2017.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. Voelzke, Jan, 2015. "Weakening the Gain–Loss-Ratio measure to make it stronger," Finance Research Letters, Elsevier, vol. 12(C), pages 58-66.
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    Cited by:

    1. Shi-jie Jiang & Mujun Lei & Cheng-Huang Chung, 2018. "An Improvement of Gain-Loss Price Bounds on Options Based on Binomial Tree and Market-Implied Risk-Neutral Distribution," Sustainability, MDPI, vol. 10(6), pages 1-17, June.

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