Statistically efficient construction of α-risk-minimizing portfolio

Hiroyuki Taniai*, Takayuki Shiohama

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We propose a semiparametrically efficient estimator for α-risk-minimizing portfolio weights. Based on the work of Bassett et al. (2004), an α-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on an asymmetric Laplace reference density, and asymptotic properties such as consistency and asymptotic normality are obtained. We apply the results of Hallin et al. (2008) to the problem of constructing α-risk-minimizing portfolios using residual signs and ranks and a general reference density. Monte Carlo simulations assess the performance of the proposed method. Empirical applications are also investigated.

Original languageEnglish
Article number980294
JournalAdvances in Decision Sciences
Publication statusPublished - 2012

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Applied Mathematics
  • Computational Mathematics
  • Statistics and Probability


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