Tangency portfolios in the LP solvable portfolio selection models

Reza Keykhaei; Mohamad Taghi Jahandideh

RAIRO - Operations Research (2012)

  • Volume: 46, Issue: 2, page 149-158
  • ISSN: 0399-0559

Abstract

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A risk measure in a portfolio selection problem is linear programming (LP) solvable, if it has a linear formulation when the asset returns are represented by discrete random variables, i.e., they are defined by their realizations under specified scenarios. The efficient frontier corresponding to an LP solvable model is a piecewise linear curve. In this paper we describe a method which realizes and produces a tangency portfolio as a by-product during the procedure of tracing out of the efficient frontier of risky assets in an LP solvable model, when our portfolio contains some risky assets and a riskless asset, using nonsmooth optimization methods. We show that the test of finding the tangency portfolio can be limited only for two portfolios. Also, we describe that how this method can be employed to trace out the efficient frontier corresponding to a portfolio selection problem in the presence of a riskless asset.

How to cite

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Keykhaei, Reza, and Jahandideh, Mohamad Taghi. "Tangency portfolios in the LP solvable portfolio selection models." RAIRO - Operations Research 46.2 (2012): 149-158. <http://eudml.org/doc/276396>.

@article{Keykhaei2012,
abstract = {A risk measure in a portfolio selection problem is linear programming (LP) solvable, if it has a linear formulation when the asset returns are represented by discrete random variables, i.e., they are defined by their realizations under specified scenarios. The efficient frontier corresponding to an LP solvable model is a piecewise linear curve. In this paper we describe a method which realizes and produces a tangency portfolio as a by-product during the procedure of tracing out of the efficient frontier of risky assets in an LP solvable model, when our portfolio contains some risky assets and a riskless asset, using nonsmooth optimization methods. We show that the test of finding the tangency portfolio can be limited only for two portfolios. Also, we describe that how this method can be employed to trace out the efficient frontier corresponding to a portfolio selection problem in the presence of a riskless asset.},
author = {Keykhaei, Reza, Jahandideh, Mohamad Taghi},
journal = {RAIRO - Operations Research},
keywords = {Linear programming; LP solvable portfolio selection models; subgradient; tangency portfolio; Aneja-Nair method; linear programming},
language = {eng},
month = {7},
number = {2},
pages = {149-158},
publisher = {EDP Sciences},
title = {Tangency portfolios in the LP solvable portfolio selection models},
url = {http://eudml.org/doc/276396},
volume = {46},
year = {2012},
}

TY - JOUR
AU - Keykhaei, Reza
AU - Jahandideh, Mohamad Taghi
TI - Tangency portfolios in the LP solvable portfolio selection models
JO - RAIRO - Operations Research
DA - 2012/7//
PB - EDP Sciences
VL - 46
IS - 2
SP - 149
EP - 158
AB - A risk measure in a portfolio selection problem is linear programming (LP) solvable, if it has a linear formulation when the asset returns are represented by discrete random variables, i.e., they are defined by their realizations under specified scenarios. The efficient frontier corresponding to an LP solvable model is a piecewise linear curve. In this paper we describe a method which realizes and produces a tangency portfolio as a by-product during the procedure of tracing out of the efficient frontier of risky assets in an LP solvable model, when our portfolio contains some risky assets and a riskless asset, using nonsmooth optimization methods. We show that the test of finding the tangency portfolio can be limited only for two portfolios. Also, we describe that how this method can be employed to trace out the efficient frontier corresponding to a portfolio selection problem in the presence of a riskless asset.
LA - eng
KW - Linear programming; LP solvable portfolio selection models; subgradient; tangency portfolio; Aneja-Nair method; linear programming
UR - http://eudml.org/doc/276396
ER -

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