Journal of Computational Finance

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

  • A novel investment strategy that maximizes the expected portfolio value bounded within a target range.
  • This strategy achieves a similar efficient frontier, a better downside-return trade-off, compared to the CRRA utility.
  • A two-stage regression method that improves the least squares Monte Carlo algorithm.

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.

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