Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92481
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Accounting and Finance | en_US |
dc.creator | Gendreau, B | en_US |
dc.creator | Jin, Y | en_US |
dc.creator | Nimalendran, M | en_US |
dc.creator | Zhong, X | en_US |
dc.date.accessioned | 2022-04-07T06:32:30Z | - |
dc.date.available | 2022-04-07T06:32:30Z | - |
dc.identifier.issn | 0003-6846 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/92481 | - |
dc.language.iso | en | en_US |
dc.publisher | Routledge, Taylor & Francis Group | en_US |
dc.rights | © 2019 Informa UK Limited, trading as Taylor & Francis Group | en_US |
dc.rights | 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 | en_US |
dc.subject | Conditional value-at-risk | en_US |
dc.subject | Enhanced indexation | en_US |
dc.subject | LASSO | en_US |
dc.subject | Stochastic programming | en_US |
dc.title | CVaR-LASSO Enhanced Index Replication (CLEIR) : outperforming by minimizing downside risk | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 5637 | en_US |
dc.identifier.epage | 5651 | en_US |
dc.identifier.volume | 51 | en_US |
dc.identifier.issue | 52 | en_US |
dc.identifier.doi | 10.1080/00036846.2019.1616072 | en_US |
dcterms.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%. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Applied economics, 2019, v. 51, no. 52, p. 5637-5651 | en_US |
dcterms.isPartOf | Applied economics | en_US |
dcterms.issued | 2019 | - |
dc.identifier.scopus | 2-s2.0-85066076629 | - |
dc.identifier.eissn | 1466-4283 | en_US |
dc.description.validate | 202204 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | RGC-B1-062, AF-0112 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong Polytechnic University [The Learning and Teaching Enhancement Grant (Project No.: 1.21.xx.8ADP)] | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Gendreau_Cvar-Lasso_Enhanced_Index.pdf | Pre-Published version | 326.74 kB | Adobe PDF | View/Open |
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