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A linearized value-at-risk model with transaction costs and short selling

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  • Yu, Jing-Rung
  • Chiou, Wan-Jiun Paul
  • Mu, Da-Ren

Abstract

Though value-at-risk (VaR) has been widely applied by finance industry, a possibility of multiple optimums that is caused by the nonlinearity in modeling can challenge its application. To deal with this technical issue, we propose a linearized VaR model that employs the mixed 0-1 programing in Lin (2009) P4 model. We further advance the previous models with considering various transaction costs and the optimization of short-selling weights. We compare the performance of buy-and-hold (BH) strategy, the mean-variance (MV) model, the original P4 model, and our linearized P4 (LP4) model by rebalancing a wide scale of international and alternative investments during a period between 2001 and 2012. The results of numerical tests show the superior performance of the VaR models to the BH and the MV portfolios. The LP4 model yields the global optimum and outperforms the corresponding P4 model in both return and risk. The stability of portfolio value generated from the LP4 model supports its higher effectiveness in risk management than the P4 model.

Suggested Citation

  • Yu, Jing-Rung & Chiou, Wan-Jiun Paul & Mu, Da-Ren, 2015. "A linearized value-at-risk model with transaction costs and short selling," European Journal of Operational Research, Elsevier, vol. 247(3), pages 872-878.
  • Handle: RePEc:eee:ejores:v:247:y:2015:i:3:p:872-878
    DOI: 10.1016/j.ejor.2015.06.024
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    References listed on IDEAS

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    Cited by:

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    2. Ruchika Sehgal & Aparna Mehra, 2023. "Quantile Regression Based Enhanced Indexing with Portfolio Rebalancing," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(3), pages 721-742, September.
    3. Al Janabi, Mazin A.M. & Arreola Hernandez, Jose & Berger, Theo & Nguyen, Duc Khuong, 2017. "Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1121-1131.
    4. So Yeon Chun & Miguel A. Lejeune, 2020. "Risk-Based Loan Pricing: Portfolio Optimization Approach with Marginal Risk Contribution," Management Science, INFORMS, vol. 66(8), pages 3735-3753, August.
    5. Jing-Rung Yu & Wan-Jiun Paul Chiou & Jian-Hong Yang, 2017. "Diversification benefits of risk portfolio models: a case of Taiwan’s stock market," Review of Quantitative Finance and Accounting, Springer, vol. 48(2), pages 467-502, February.
    6. Mazin A.M. Al Janabi, 2021. "Is optimum always optimal? A revisit of the mean‐variance method under nonlinear measures of dependence and non‐normal liquidity constraints," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 387-415, April.
    7. Adcock, C J & Meade, N, 2017. "Using parametric classification trees for model selection with applications to financial risk management," European Journal of Operational Research, Elsevier, vol. 259(2), pages 746-765.

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