Skewed target range strategy for multiperiod portfolio optimization using a two-stage least squares Monte Carlo method

Rongju Zhang, Nicolas Langrené (), Yu Tian, Zili Zhu, Fima Klebaner and Kais Hamza
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Rongju Zhang: Monash University [Melbourne]
Nicolas Langrené: CSIRO - Commonwealth Scientific and Industrial Research Organisation [Australia]
Yu Tian: Monash University [Melbourne]
Zili Zhu: CSIRO - Commonwealth Scientific and Industrial Research Organisation [Australia]
Fima Klebaner: Monash University [Melbourne]
Kais Hamza: Monash University [Melbourne]

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Abstract: In this paper, we propose a novel investment strategy for portfolio optimization problems. The proposed strategy maximizes the expected portfolio value bounded within a targeted range, composed of a conservative lower target representing a need for capital protection and a desired upper target representing an investment goal. This strategy favorably shapes the entire probability distribution of returns, as it simultaneously seeks a desired expected return, cuts off downside risk and implicitly caps volatility and higher moments. To illustrate the effectiveness of this investment strategy, we study a multiperiod portfolio optimization problem with transaction costs and develop a two-stage regression approach that improves the classical least squares Monte Carlo (LSMC) algorithm when dealing with difficult payoffs, such as highly concave, abruptly changing or discontinuous functions. Our numerical results show substantial improvements over the classical LSMC algorithm for both the constant relative risk-aversion (CRRA) utility approach and the proposed skewed target range strategy (STRS). Our numerical results illustrate the ability of the STRS to contain the portfolio value within the targeted range. When compared with the CRRA utility approach, the STRS achieves a similar mean-variance efficient frontier while delivering a better downside risk-return trade-off.

Keywords: target-based portfolio optimization; alternative performance measure; multiperiod portfolio optimization; least squares Monte Carlo; two-stage regression (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-cmp and nep-upt
Note: View the original document on HAL open archive server: https://hal.science/hal-02909342v1
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Published in The Journal of Computational Finance, 2019, 23 (1), pp.97-127. ⟨10.21314/JCF.2019.368⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02909342

DOI: 10.21314/JCF.2019.368

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