Characteristics of Omega-optimized portfolios at different levels of threshold returns
There is little literature considering effects that the loss-gain threshold used for dividing good and bad outcomes by all downside (upside) risk measures has on portfolio optimization and performance. The purpose of this study is to assess the performance of portfolios optimized with respect to the...
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| Format: | Article |
| Language: | English |
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Vilnius Gediminas Technical University
2014-12-01
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| Series: | Business, Management and Education |
| Subjects: | |
| Online Access: | http://journals.vgtu.lt/index.php/BME/article/view/3517 |
| _version_ | 1798028728226283520 |
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| author | Renaldas Vilkancas |
| author_facet | Renaldas Vilkancas |
| author_sort | Renaldas Vilkancas |
| collection | DOAJ |
| description | There is little literature considering effects that the loss-gain threshold used for dividing good and bad outcomes by all downside (upside) risk measures has on portfolio optimization and performance. The purpose of this study is to assess the performance of portfolios optimized with respect to the Omega function developed by Keating and Shadwick at different levels of the threshold returns. The most common choices of the threshold values used in various Omega studies cover the risk-free rate and the average market return or simply a zero return, even though the inventors of this measure for risk warn that “using the values of the Omega function at particular points can be critically misleading” and that “only the entire Omega function contains information on distribution”. The obtained results demonstrate the importance of the selected values of the threshold return on portfolio performance – higher levels of the threshold lead to an increase in portfolio returns, albeit at the expense of a higher risk. In fact, within a certain threshold interval, Omega-optimized portfolios achieved the highest net return, compared with all other strategies for portfolio optimization using three different test datasets. However, beyond a certain limit, high threshold values will actually start hurting portfolio performance while meta-heuristic optimizers typically are able to produce a solution at any level of the threshold, and the obtained results would most likely be financially meaningless. |
| first_indexed | 2024-04-11T19:13:01Z |
| format | Article |
| id | doaj.art-2a4d52cff092424bbba7aba44c0b4628 |
| institution | Directory Open Access Journal |
| issn | 2029-7491 2029-6169 |
| language | English |
| last_indexed | 2024-04-11T19:13:01Z |
| publishDate | 2014-12-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Business, Management and Education |
| spelling | doaj.art-2a4d52cff092424bbba7aba44c0b46282022-12-22T04:07:32ZengVilnius Gediminas Technical UniversityBusiness, Management and Education2029-74912029-61692014-12-0112210.3846/bme.2014.235Characteristics of Omega-optimized portfolios at different levels of threshold returnsRenaldas Vilkancas0Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, LithuaniaThere is little literature considering effects that the loss-gain threshold used for dividing good and bad outcomes by all downside (upside) risk measures has on portfolio optimization and performance. The purpose of this study is to assess the performance of portfolios optimized with respect to the Omega function developed by Keating and Shadwick at different levels of the threshold returns. The most common choices of the threshold values used in various Omega studies cover the risk-free rate and the average market return or simply a zero return, even though the inventors of this measure for risk warn that “using the values of the Omega function at particular points can be critically misleading” and that “only the entire Omega function contains information on distribution”. The obtained results demonstrate the importance of the selected values of the threshold return on portfolio performance – higher levels of the threshold lead to an increase in portfolio returns, albeit at the expense of a higher risk. In fact, within a certain threshold interval, Omega-optimized portfolios achieved the highest net return, compared with all other strategies for portfolio optimization using three different test datasets. However, beyond a certain limit, high threshold values will actually start hurting portfolio performance while meta-heuristic optimizers typically are able to produce a solution at any level of the threshold, and the obtained results would most likely be financially meaningless.http://journals.vgtu.lt/index.php/BME/article/view/3517downside riskOmega functionportfolio optimizationthreshold returndifferential evolution (DE) |
| spellingShingle | Renaldas Vilkancas Characteristics of Omega-optimized portfolios at different levels of threshold returns Business, Management and Education downside risk Omega function portfolio optimization threshold return differential evolution (DE) |
| title | Characteristics of Omega-optimized portfolios at different levels of threshold returns |
| title_full | Characteristics of Omega-optimized portfolios at different levels of threshold returns |
| title_fullStr | Characteristics of Omega-optimized portfolios at different levels of threshold returns |
| title_full_unstemmed | Characteristics of Omega-optimized portfolios at different levels of threshold returns |
| title_short | Characteristics of Omega-optimized portfolios at different levels of threshold returns |
| title_sort | characteristics of omega optimized portfolios at different levels of threshold returns |
| topic | downside risk Omega function portfolio optimization threshold return differential evolution (DE) |
| url | http://journals.vgtu.lt/index.php/BME/article/view/3517 |
| work_keys_str_mv | AT renaldasvilkancas characteristicsofomegaoptimizedportfoliosatdifferentlevelsofthresholdreturns |