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Dynamic portfolio optimization with risk control for absolute deviation model

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  • Yu, Mei
  • Takahashi, Satoru
  • Inoue, Hiroshi
  • Wang, Shouyang

Abstract

In this paper, we present a new multiperiod portfolio selection with maximum absolute deviation model. The investor is assumed to seek an investment strategy to maximize his/her terminal wealth and minimize the risk. One typical feature is that the absolute deviation is employed as risk measure instead of classical mean variance method. Furthermore, risk control is considered in every period for the new model. An analytical optimal strategy is obtained in a closed form via dynamic programming method. Algorithm with some examples is also presented to illustrate the application of this model.

Suggested Citation

  • Yu, Mei & Takahashi, Satoru & Inoue, Hiroshi & Wang, Shouyang, 2010. "Dynamic portfolio optimization with risk control for absolute deviation model," European Journal of Operational Research, Elsevier, vol. 201(2), pages 349-364, March.
    • Handle: RePEc:eee:ejores:v:201:y:2010:i:2:p:349-364
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    3. Bodnar, Taras & Parolya, Nestor & Schmid, Wolfgang, 2015. "On the exact solution of the multi-period portfolio choice problem for an exponential utility under return predictability," European Journal of Operational Research, Elsevier, vol. 246(2), pages 528-542.
    4. Mei Yu & Shouyang Wang, 2012. "Dynamic optimal portfolio with maximum absolute deviation model," Journal of Global Optimization, Springer, vol. 53(2), pages 363-380, June.
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    6. Antonio Santos, 2016. "Static and dynamic portfolio allocation with nonstandard utility functions," EcoMod2016 9375, EcoMod.
    7. Leung, Pui-Lam & Ng, Hon-Yip & Wong, Wing-Keung, 2012. "An improved estimation to make Markowitz’s portfolio optimization theory users friendly and estimation accurate with application on the US stock market investment," European Journal of Operational Research, Elsevier, vol. 222(1), pages 85-95.
    8. Yu, Jing-Rung & Lee, Wen-Yi, 2011. "Portfolio rebalancing model using multiple criteria," European Journal of Operational Research, Elsevier, vol. 209(2), pages 166-175, March.
    9. Chen, Zhi-ping & Li, Gang & Guo, Ju-e, 2013. "Optimal investment policy in the time consistent mean–variance formulation," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 145-156.
    10. Bai, Zhidong & Li, Hua & Wong, Wing-Keung, 2013. "The best estimation for high-dimensional Markowitz mean-variance optimization," MPRA Paper 43862, University Library of Munich, Germany.
    11. Buckley, Winston S. & Brown, Garfield O. & Marshall, Mario, 2012. "A mispricing model of stocks under asymmetric information," European Journal of Operational Research, Elsevier, vol. 221(3), pages 584-592.
    12. Cooper, W.W. & Kingyens, Angela T. & Paradi, Joseph C., 2014. "Two-stage financial risk tolerance assessment using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 233(1), pages 273-280.
    13. Li, Xiang & Shou, Biying & Qin, Zhongfeng, 2012. "An expected regret minimization portfolio selection model," European Journal of Operational Research, Elsevier, vol. 218(2), pages 484-492.
    14. Buckley, Winston S. & Long, Hongwei, 2015. "A discontinuous mispricing model under asymmetric information," European Journal of Operational Research, Elsevier, vol. 243(3), pages 944-955.
    15. Zhang Peng & Gong Heshan & Lan Weiting, 2017. "Multi-Period Mean-Absolute Deviation Fuzzy Portfolio Selection Model with Entropy Constraints," Journal of Systems Science and Information, De Gruyter, vol. 4(5), pages 428-443, October.

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