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Improved Conditional Value-at-Risk (CVaR) Based Method for Diversified Bond Portfolio Optimization

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Soft Computing in Data Science (SCDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 937))

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Abstract

In this study, an improved CVaR-based Portfolio Optimization Method is presented. The method was used to test the performance of a diversified bond portfolio in providing low expected loss and optimal CVaR. A hypothetical diversified bond portfolio, which is a combination of Islamic bond or Sukuk and conventional bond, was constructed using bonds issued by four banking institutions. The performance of the improved method is determined by comparing the generated returns of the method against the existing CVaR-based Portfolio Optimization Method. The simulation of the optimization process of both methods was carried out by using the Geometric Brownian Motion-based Monte Carlo Simulation method. The results of the improved CVaR portfolio optimization method show that by restricting the upper and lower bounds with certain floor and ceiling bond weights using volatility weighting schemes, the expected loss can be reduced and an optimal CVaR can be achieved. Thus, this study shows that the improved CVaR-based Portfolio Optimization Method is able to provide a better optimization of a diversified bond portfolio in terms of reducing the expected loss, and hence maximizes the returns.

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Acknowledgement

This work was supported by the LESTARI grant [600-IRMI/DANA 5/3/LESTARI (0127/2016)], Universiti Teknologi MARA, Malaysia

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Authors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Shah Alam, Malaysia

    Nor Idayu Mat Rifin, Shahirulliza Shamsul Ambia & Rashidah Ismail

  2. Advanced Analytics Engineering Center (AAEC), Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Nuru’l-‘Izzah Othman

Authors
  1. Nor Idayu Mat Rifin
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  2. Nuru’l-‘Izzah Othman
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  3. Shahirulliza Shamsul Ambia
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  4. Rashidah Ismail
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Corresponding author

Correspondence to Nuru’l-‘Izzah Othman .

Editor information

Editors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Bee Wah Yap

  2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Azlinah Hj Mohamed

  3. Department of Electrical Engineering and Computer Science, University of Tennessee at Knoxville, Knoxville, TN, USA

    Michael W. Berry

APPENDIX A

APPENDIX A

Source Code

A.1: Simulated Price and Return to Run A.2

figure a

A.2: CVaR Portfolio Optimization

figure b

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Mat Rifin, N.I., Othman, N‘., Ambia, S.S., Ismail, R. (2019). Improved Conditional Value-at-Risk (CVaR) Based Method for Diversified Bond Portfolio Optimization. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_12

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  • DOI: https://doi.org/10.1007/978-981-13-3441-2_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3440-5

  • Online ISBN: 978-981-13-3441-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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