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Optimal portfolio policy in a bond market.

机译:债券市场中的最优投资组合政策。

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摘要

We have a small investor who wants to optimize his expected utility of terminal wealth over a finite time horizon. We allow him to invest in a money market account or a bond market which consists of a zero coupon bond existing from time zero to time of one half the investment time horizon, and another zero coupon bond existing from time of one half the investment time horizon to the end of the investment time horizon. To solve this problem, a two stage dynamic programming problem in continuous time is formulated and the corresponding Hamilton-Jacobi-Bellman equations of Dynamic Programming are solved. In particular, we determine the optimal value function, optimal portfolio process, and the corresponding wealth process. We state and prove a verification theorem which our candidate optimal value function and portfolio process are shown to satisfy. We analyze the behavior of our optimal portfolio process, optimal value function, and the associated wealth process. Finally, we compare our results where the bond market consists of one zero coupon bond. The resulting optimal value function and wealth process are the same as the two stage case, while the resulting optimal portfolio process agrees with the two stage case on the latter half of the investment time horizon.
机译:我们有一个小投资者,他想在有限的时间范围内优化其终端财富的预期效用。我们允许他投资于货币市场帐户或债券市场,该债券市场由零息债券组成,零息债券存在时间为零至投资时间范围的一半,而另一零息债券则存在于投资时间范围的一半之间。到投资时间范围的尽头。为了解决该问题,提出了连续时间的两阶段动态规划问题,并求解了相应的汉密尔顿-雅各比-贝尔曼动态规划方程。特别是,我们确定了最优价值函数,最优投资组合过程以及相应的财富过程。我们陈述并证明了证明定理,证明了我们的候选最优价值函数和投资组合过程能够满足。我们分析了最优投资组合过程,最优价值函数以及相关财富过程的行为。最后,我们比较了债券市场由一个零息债券组成的结果。最优价值函数和财富过程的结果与两阶段情况相同,而最优投资组合过程在投资时间范围的后半部分与两阶段情况一致。

著录项

  • 作者

    Heddy, Merrill A.;

  • 作者单位

    New York University, Graduate School of Business Administration.;

  • 授予单位 New York University, Graduate School of Business Administration.;
  • 学科 Economics Finance.; Mathematics.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 82 p.
  • 总页数 82
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 财政、金融;数学;
  • 关键词

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