Portfolio optimization under lower partial risk measures

Hiroshi Konno, Hayato Waki, Atsushi Yuuki

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

Portfolio management using lower partial risk (downside risk) measures is attracting more attention of practitioners in recent years. The purpose of this paper is to review important characteristics of these risk measures and conduct simulation using four alternative measures, lower semi-variance, lower semi-absolute deviation, first order below target risk and conditional value-at-risk. We will show that these risk measures are useful to control downside risk when the distribution of assets is non-symmetric. Further, we will propose a computational scheme to resolve the difficulty associated with solving a large dense linear programming problems resulting from these models. We will demonstrate that this method can in fact solve problems consisting of 104 assets and 105 scenarios within a practical amount of CPU time.

Original languageEnglish
Pages (from-to)127-140
Number of pages14
JournalAsia-Pacific Financial Markets
Volume9
Issue number2
DOIs
Publication statusPublished - Jan 1 2002
Externally publishedYes

Fingerprint

Assets
Portfolio optimization
Risk measures
Downside risk
Simulation
Semivariance
Portfolio management
Measure of risk
Deviation
Linear programming
Scenarios
Conditional value at risk

All Science Journal Classification (ASJC) codes

  • Finance

Cite this

Portfolio optimization under lower partial risk measures. / Konno, Hiroshi; Waki, Hayato; Yuuki, Atsushi.

In: Asia-Pacific Financial Markets, Vol. 9, No. 2, 01.01.2002, p. 127-140.

Research output: Contribution to journalArticle

Konno, Hiroshi ; Waki, Hayato ; Yuuki, Atsushi. / Portfolio optimization under lower partial risk measures. In: Asia-Pacific Financial Markets. 2002 ; Vol. 9, No. 2. pp. 127-140.
@article{deecc1f44e744fb58d3f24765196010a,
title = "Portfolio optimization under lower partial risk measures",
abstract = "Portfolio management using lower partial risk (downside risk) measures is attracting more attention of practitioners in recent years. The purpose of this paper is to review important characteristics of these risk measures and conduct simulation using four alternative measures, lower semi-variance, lower semi-absolute deviation, first order below target risk and conditional value-at-risk. We will show that these risk measures are useful to control downside risk when the distribution of assets is non-symmetric. Further, we will propose a computational scheme to resolve the difficulty associated with solving a large dense linear programming problems resulting from these models. We will demonstrate that this method can in fact solve problems consisting of 104 assets and 105 scenarios within a practical amount of CPU time.",
author = "Hiroshi Konno and Hayato Waki and Atsushi Yuuki",
year = "2002",
month = "1",
day = "1",
doi = "10.1023/A:1022238119491",
language = "English",
volume = "9",
pages = "127--140",
journal = "Asia-Pacific Financial Markets",
issn = "1387-2834",
publisher = "Springer New York",
number = "2",

}

TY - JOUR

T1 - Portfolio optimization under lower partial risk measures

AU - Konno, Hiroshi

AU - Waki, Hayato

AU - Yuuki, Atsushi

PY - 2002/1/1

Y1 - 2002/1/1

N2 - Portfolio management using lower partial risk (downside risk) measures is attracting more attention of practitioners in recent years. The purpose of this paper is to review important characteristics of these risk measures and conduct simulation using four alternative measures, lower semi-variance, lower semi-absolute deviation, first order below target risk and conditional value-at-risk. We will show that these risk measures are useful to control downside risk when the distribution of assets is non-symmetric. Further, we will propose a computational scheme to resolve the difficulty associated with solving a large dense linear programming problems resulting from these models. We will demonstrate that this method can in fact solve problems consisting of 104 assets and 105 scenarios within a practical amount of CPU time.

AB - Portfolio management using lower partial risk (downside risk) measures is attracting more attention of practitioners in recent years. The purpose of this paper is to review important characteristics of these risk measures and conduct simulation using four alternative measures, lower semi-variance, lower semi-absolute deviation, first order below target risk and conditional value-at-risk. We will show that these risk measures are useful to control downside risk when the distribution of assets is non-symmetric. Further, we will propose a computational scheme to resolve the difficulty associated with solving a large dense linear programming problems resulting from these models. We will demonstrate that this method can in fact solve problems consisting of 104 assets and 105 scenarios within a practical amount of CPU time.

UR - http://www.scopus.com/inward/record.url?scp=24944501358&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=24944501358&partnerID=8YFLogxK

U2 - 10.1023/A:1022238119491

DO - 10.1023/A:1022238119491

M3 - Article

VL - 9

SP - 127

EP - 140

JO - Asia-Pacific Financial Markets

JF - Asia-Pacific Financial Markets

SN - 1387-2834

IS - 2

ER -