A simulation approach to statistical estimation of multiperiod optimal portfolios

研究成果: Article査読

抄録

This paper discusses a simulation-based method for solving discrete-time multiperiod portfolio choice problems under AR(1) process. The method is applicable even if the distributions of return processes are unknown. We first generate simulation sample paths of the random returns by using AR bootstrap. Then, for each sample path and each investment time, we obtain an optimal portfolio estimator, which optimizes a constant relative risk aversion (CRRA) utility function. When an investor considers an optimal investment strategy with portfolio rebalancing, it is convenient to introduce a value function. The most important difference between single-period portfolio choice problems and multiperiod ones is that the value function is time dependent. Our method takes care of the time dependency by using bootstrapped sample paths. Numerical studies are provided to examine the validity of our method. The result shows the necessity to take care of the time dependency of the value function.

本文言語English
論文番号341476
ジャーナルAdvances in Decision Sciences
2012
DOI
出版ステータスPublished - 2012

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Statistics and Probability
  • Computational Mathematics
  • Applied Mathematics

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