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http://hdl.handle.net/10397/92481
Title: | CVaR-LASSO Enhanced Index Replication (CLEIR) : outperforming by minimizing downside risk | Authors: | Gendreau, B Jin, Y Nimalendran, M Zhong, X |
Issue Date: | 2019 | Source: | Applied economics, 2019, v. 51, no. 52, p. 5637-5651 | Abstract: | Index-funds are one of the most popular investment vehicles among investors, with total assets indexed to the S&P500 exceeding $8.7 trillion at-the-end of 2016. Recently, enhanced-index-funds, which seek to outperform an index while maintaining a similar risk-profile, have grown in popularity. We propose an enhanced-index-tracking method that uses the linear absolute shrinkage selection operator (LASSO) method to minimize the Conditional Value-at-Risk (CVaR) of the tracking error. This minimizes the large downside tracking-error while keeping the upside. Using historical and simulated data, our CLEIR method outperformed the benchmark with a tracking error of 1%. The effect is more pronounced when the number of the constituents is large. Using 50–80 large stocks in the S&P 500 index, our method closely tracked the benchmark with an alpha 2.55%. | Keywords: | Conditional value-at-risk Enhanced indexation LASSO Stochastic programming |
Publisher: | Routledge, Taylor & Francis Group | Journal: | Applied economics | ISSN: | 0003-6846 | EISSN: | 1466-4283 | DOI: | 10.1080/00036846.2019.1616072 | Rights: | © 2019 Informa UK Limited, trading as Taylor & Francis Group This is an Accepted Manuscript of an article published by Taylor & Francis in Applied Economics on 20 May 2019 (Published online), available at: http://www.tandfonline.com/10.1080/00036846.2019.1616072 |
Appears in Collections: | Journal/Magazine Article |
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Gendreau_Cvar-Lasso_Enhanced_Index.pdf | Pre-Published version | 326.74 kB | Adobe PDF | View/Open |
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