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Incremental Sharpe and other performance ratios

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  • Eric Benhamou

    (MILES - Machine Intelligence and Learning Systems - LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique, LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Beatrice Guez

Abstract

We present a new methodology of computing incremental contribution for performance ratios for portfolio like Sharpe, Treynor, Calmar or Sterling ratios. Using Euler's homogeneous function theorem, we are able to decompose these performance ratios as a linear combination of individual modified performance ratios. This allows understanding the drivers of these performance ratios as well as deriving a condition for a new asset to provide incremental performance for the portfolio. We provide various numerical examples of this performance ratio decomposition. JEL classification: C12, G11.

Suggested Citation

  • Eric Benhamou & Beatrice Guez, 2018. "Incremental Sharpe and other performance ratios," Post-Print hal-02012443, HAL.
    • Handle: RePEc:hal:journl:hal-02012443
    Note: View the original document on HAL open archive server: https://hal.science/hal-02012443v2
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    References listed on IDEAS

    as
    1. Serge Darolles & Christian Gouriéroux & Emmanuelle Jay, 2012. "Robust Portfolio Allocation with Systematic Risk Contribution Restrictions," Working Papers 2012-35, Center for Research in Economics and Statistics.
    2. repec:dau:papers:123456789/4688 is not listed on IDEAS
    3. Philippe Bertrand, 2009. "Risk-adjusted performance attribution and portfolio optimisations under tracking-error constraints," Journal of Asset Management, Palgrave Macmillan, vol. 10(2), pages 75-88, June.
    4. Nielsen, Lars Tyge & Vassalou, Maria, 2004. "Sharpe Ratios and Alphas in Continuous Time," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(1), pages 103-114, March.
    5. DeMiguel, Victor & Plyakha, Yuliya & Uppal, Raman & Vilkov, Grigory, 2013. "Improving Portfolio Selection Using Option-Implied Volatility and Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(6), pages 1813-1845, December.
    6. Eugene A. Pilotte & Frederic P. Sterbenz, 2006. "Sharpe and Treynor Ratios on Treasury Bonds," The Journal of Business, University of Chicago Press, vol. 79(1), pages 149-180, January.
    7. Philippe Bertrand, 2008. "Risk Attribution and Portfolio Optimizations Under Tracking-Error Constraints," Post-Print hal-01833102, HAL.
    8. Miguel Lobo & Maryam Fazel & Stephen Boyd, 2007. "Portfolio optimization with linear and fixed transaction costs," Annals of Operations Research, Springer, vol. 152(1), pages 341-365, July.
    9. Treynor, Jack L & Black, Fischer, 1973. "How to Use Security Analysis to Improve Portfolio Selection," The Journal of Business, University of Chicago Press, vol. 46(1), pages 66-86, January.
    10. Goto, Shingo & Xu, Yan, 2015. "Improving Mean Variance Optimization through Sparse Hedging Restrictions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 50(6), pages 1415-1441, December.
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    Cited by:

    1. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & François Chareyron, 2021. "Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models," Working Papers hal-03202431, HAL.
    2. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & Franc{c}ois Chareyron, 2021. "Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting," Papers 2104.10483, arXiv.org, revised Apr 2021.

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    More about this item

    Keywords

    Sharpe; Treynor; recovery; incremental Sharpe ratio; portfolio diversification; MILES; LAMSADE;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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