Application of Robust Statistics to Asset Allocation Models
Many strategies for asset allocation involve the computation of the expected value and the covariance matrix of the returns of financial instruments. How much of each instrument to own is determined by an attempt to minimize risk — the variance of linear combinations of investments in these financi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Instituto Nacional de Estatística | Statistics Portugal
2007-03-01
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| Series: | Revstat Statistical Journal |
| Subjects: | |
| Online Access: | https://revstat.ine.pt/index.php/REVSTAT/article/view/44 |
| _version_ | 1817997005249576960 |
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| author | Roy E. Welsch Xinfeng Zhou |
| author_facet | Roy E. Welsch Xinfeng Zhou |
| author_sort | Roy E. Welsch |
| collection | DOAJ |
| description |
Many strategies for asset allocation involve the computation of the expected value and the covariance matrix of the returns of financial instruments. How much of each instrument to own is determined by an attempt to minimize risk — the variance of linear combinations of investments in these financial assets — subject to various constraints such as a given level of return, concentration limits, etc. The covariance matrix contains many parameters to estimate and two main problems arise. First, the data will very likely have outliers that will seriously affect the covariance matrix. Second, with so many parameters to estimate, a large number of return observations are required and the nature of markets may change substantially over such a long period. In this paper we discuss using robust covariance procedures, FAST-MCD, Iterated Bivariate Winsorization and Fast 2-D Winsorization, to address the first problem and penalization methods for the second. When back-tested on market data, these methods are shown to be effective in improving portfolio performance. Robust asset allocation methods have great potential to improve risk-adjusted portfolio returns and therefore deserve further exploration in investment management research.
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| first_indexed | 2024-04-14T02:31:50Z |
| format | Article |
| id | doaj.art-23bb73bede09412faf49cd44c6474085 |
| institution | Directory Open Access Journal |
| issn | 1645-6726 2183-0371 |
| language | English |
| last_indexed | 2024-04-14T02:31:50Z |
| publishDate | 2007-03-01 |
| publisher | Instituto Nacional de Estatística | Statistics Portugal |
| record_format | Article |
| series | Revstat Statistical Journal |
| spelling | doaj.art-23bb73bede09412faf49cd44c64740852022-12-22T02:17:38ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712007-03-015110.57805/revstat.v5i1.44Application of Robust Statistics to Asset Allocation ModelsRoy E. Welsch 0Xinfeng Zhou 1Massachusetts Institute of TechnologyBarclays Global Investors Many strategies for asset allocation involve the computation of the expected value and the covariance matrix of the returns of financial instruments. How much of each instrument to own is determined by an attempt to minimize risk — the variance of linear combinations of investments in these financial assets — subject to various constraints such as a given level of return, concentration limits, etc. The covariance matrix contains many parameters to estimate and two main problems arise. First, the data will very likely have outliers that will seriously affect the covariance matrix. Second, with so many parameters to estimate, a large number of return observations are required and the nature of markets may change substantially over such a long period. In this paper we discuss using robust covariance procedures, FAST-MCD, Iterated Bivariate Winsorization and Fast 2-D Winsorization, to address the first problem and penalization methods for the second. When back-tested on market data, these methods are shown to be effective in improving portfolio performance. Robust asset allocation methods have great potential to improve risk-adjusted portfolio returns and therefore deserve further exploration in investment management research. https://revstat.ine.pt/index.php/REVSTAT/article/view/44robust statisticsasset allocationFAST-MCDbivariate Winsorizationpenalization |
| spellingShingle | Roy E. Welsch Xinfeng Zhou Application of Robust Statistics to Asset Allocation Models Revstat Statistical Journal robust statistics asset allocation FAST-MCD bivariate Winsorization penalization |
| title | Application of Robust Statistics to Asset Allocation Models |
| title_full | Application of Robust Statistics to Asset Allocation Models |
| title_fullStr | Application of Robust Statistics to Asset Allocation Models |
| title_full_unstemmed | Application of Robust Statistics to Asset Allocation Models |
| title_short | Application of Robust Statistics to Asset Allocation Models |
| title_sort | application of robust statistics to asset allocation models |
| topic | robust statistics asset allocation FAST-MCD bivariate Winsorization penalization |
| url | https://revstat.ine.pt/index.php/REVSTAT/article/view/44 |
| work_keys_str_mv | AT royewelsch applicationofrobuststatisticstoassetallocationmodels AT xinfengzhou applicationofrobuststatisticstoassetallocationmodels |