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Application Of Robust Regression For Portfolio Optimization

Author

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  • Ezra Putranda Setiawan

    (Universitas Negeri Yogyakarta Fakultas Matematika dan Ilmu Pengetahuan Alam)

  • Dedi Rosadi

    (Universitas Gadjah Mada)

Abstract

The single-index model is a portfolio optimization method that uses each asset’s beta’. In general, the beta is estimated using the return data by the least square method. However, the return data frequently contains several outliers, so the estimation resulting from the least square method is inaccurate. This study examines several beta estimators from robust regression methods, namely the least absolute value estimator, M-estimator, LMS-estimator, LTS-estimator, MM-estimator, and Tau estimator to estimate the beta of each asset and make an optimal portfolio based on this estimated value. We also evaluate the effect of robust beta estimators on the stability and performance of each portfolio. We present the Sharpe ratio and some turnover measures, namely the l-period portfolio turnover, maximum turnover, lower bound single-asset turnover, and lower bound multiple-asset turnover. Among various estimators used here, the Tau estimator is the best estimator to replace the OLS for estimating the beta.

Suggested Citation

  • Ezra Putranda Setiawan & Dedi Rosadi, 2023. "Application Of Robust Regression For Portfolio Optimization," Matrix Science Mathematic (MSMK), Zibeline International Publishing, vol. 7(1), pages 07-15, January.
    • Handle: RePEc:zib:zbmsmk:v:7:y:2023:i:1:p:07-15
    DOI: 10.26480/msmk.01.2023.07.15
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