Computational Asset Allocation Using One-Sided and Two-Sided Variability Measures

  • Simone Farinelli
  • Damiano Rossello
  • Luisa Tibiletti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)


Excluding the assumption of normality in return distributions, a general reward-risk ratio suitable to compare portfolio returns with respect to a benchmark must includes asymmetrical information on both “good” volatility (above the benchmark) and “bad” volatility (below the benchmark), with different sensitivities. Including the Farinelli-Tibiletti ratio and few other indexes recently proposed by the literature, the class of one-sided variability measures achieves the goal. We investigate the forecasting ability of eleven alternatives ratios in portfolio optimization problems. We employ data from security markets to quantify the portfolio’s overperformance with respect to a given benchmark.


Portfolio Optimization Portfolio Selection Excess Return Stock Index Exponential Weight Move Average 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Biglova, A., Huber, I., Ortobelli, S., Rachev, S.T.: Optimal Portfolio Selection and Risk Management: A Comparison Between the Stable Paretian Approach and the Gaussian One. In: Rachev, S.T. (ed.) Handbook on Computational and Numerical Methods in Finance, pp. 197–252. Birkhäuser, Basel (2004)Google Scholar
  2. 2.
    Biglova, A., Ortobelli, S., Rachev, S.T., Stoyanov, S.: Different Approaches to Risk Estimation in Portfolio Theory. Journal of Portfolio Management, 103–112 (Fall 2004)Google Scholar
  3. 3.
    Farinelli, S., Tibiletti, L.: Sharpe Thinking in Asset Ranking with One-Sided Measures. European Journal of Operational Research 5 (2005) (forthcoming)Google Scholar
  4. 4.
    Farinelli, S., Tibiletti, L.: Upside and Downside Risk with a Benchmark. Atlantic Economic Journal 31(4), 387 (2003)CrossRefGoogle Scholar
  5. 5.
    Farinelli, S., Tibiletti, L.: Sharpe Thinking with Asymmetrical Preferences. Technical Report, presented at European Bond Commission, Winter Meeting, Frankfurt (2003)Google Scholar
  6. 6.
    Favre, L., Galeano, J.A.: Mean-Modified Value at Risk Optimization with Hedge Funds. The Journal of Alternative Investment 5 (Fall 2002)Google Scholar
  7. 7.
    Huber, I., Ortobelli, S., Rachev, S.T., Schwartz, E.: Portfolio Choice Theory with Non-Gaussian Distributed Returns. In: Rachev, S.T. (ed.) Handbook of Heavy Tailed Distribution in Finance, pp. 547–594. Elsevier, Amsterdam (2003)Google Scholar
  8. 8.
    Konno, H., Yamazaki, H.: Mean-Absolute Deviation Portfolio Optimization Model and its Application to Tokyo Stock Market. Management Science 37, 519–531 (1991)CrossRefGoogle Scholar
  9. 9.
    Martin, D., Rachev, S.T., Siboulet, F.: Phi-Alpha Optimal Portfolios and Extreme Risk Management. Wilmott Magazine of Finance, 70–83 (November 2003)Google Scholar
  10. 10.
    McCulloch, J.H.: Simple Consistent Estimators of Stable Distribution Parameters. Commun. Statist. Simulation 15, 1109–1136 (1986)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Nolan, J.P.: Numerical Approximations of Stable Densities and Distribution Functions. Commun. Statist. Stochastic Models 13, 759–774 (1997)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Pedersen, C.S., Satchell, S.E.: On the Foundation of Performance Measures under Asymmetric Returns. Quantitative Finance (2003)Google Scholar
  13. 13.
    Shalit, H., Yitzhaki, S.: Mean-Gini, Portfolio Theory, and the Pricing of Risky Assets. Journal of Finance 39, 1449–1468 (1984)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Sortino, F.A., van der Meer, R.: Downside Risk. Journal of Portfolio Management 17(4), 27–32 (1991)CrossRefGoogle Scholar
  15. 15.
    Sortino, F.A.: Upside-Potential Ratios Vary by Investment Style. Pensions and Investment 28, 30–35 (2000)Google Scholar
  16. 16.
    Yitzhaki, S.: Stochastic Dominance, Mean Variance and Gini’s Mean Difference. American Economic Review 72, 178–185 (1982)Google Scholar
  17. 17.
    Young, M.R.: A MiniMax Portfolio Selection Rule with Linear Programming Solution. Management Science 44, 673–683 (1998)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Simone Farinelli
    • 1
  • Damiano Rossello
    • 2
  • Luisa Tibiletti
    • 3
  1. 1.Quantitative and Bond ResearchCantonal Bank of ZurichZurichSwitzerland
  2. 2.Department of Economics and Quantitative MethodsUniversity of CataniaCataniaItaly
  3. 3.Department of Statistics and Mathematics “Diego de Castro”University of TorinoTorinoItaly

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