Computational asset allocation using one-sided and two-sided variability measures

  • Authors:
  • Simone Farinelli;Damiano Rossello;Luisa Tibiletti

  • Affiliations:
  • Quantitative and Bond Research, Cantonal Bank of Zurich, Zurich, Switzerland;Department of Economics and Quantitative Methods, University of Catania, Catania, Italy;Department of Statistics and Mathematics “Diego de Castro”, University of Torino, Torino, Italy

  • Venue:
  • ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
  • Year:
  • 2006

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Abstract

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.