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CVaR-LASSO Enhanced Index Replication (CLEIR): outperforming by minimizing downside risk

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  • Brian Gendreau
  • Yong Jin
  • Mahendrarajah Nimalendran
  • Xiaolong Zhong

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 $$ \sim 1\% $$∼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 $$2.55\% $$2.55%.

Suggested Citation

  • Brian Gendreau & Yong Jin & Mahendrarajah Nimalendran & Xiaolong Zhong, 2019. "CVaR-LASSO Enhanced Index Replication (CLEIR): outperforming by minimizing downside risk," Applied Economics, Taylor & Francis Journals, vol. 51(52), pages 5637-5651, November.
    • Handle: RePEc:taf:applec:v:51:y:2019:i:52:p:5637-5651
    DOI: 10.1080/00036846.2019.1616072
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    Cited by:

    1. Yafen Ye & Renyong Chi & Yuan-Hai Shao & Chun-Na Li & Xiangyu Hua, 2022. "Indicator Selection of Index Construction by Adaptive Lasso with a Generic $$\varepsilon $$ ε -Insensitive Loss," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 971-990, October.

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