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Hedging Goals

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  • Thomas Krabichler
  • Marcus Wunsch

Abstract

Goal-based investing is concerned with reaching a monetary investment goal by a given finite deadline, which differs from mean-variance optimization in modern portfolio theory. In this article, we expand the close connection between goal-based investing and option hedging that was originally discovered in [Bro99b] by allowing for varying degrees of investor risk aversion using lower partial moments of different orders. Moreover, we show that maximizing the probability of reaching the goal (quantile hedging, cf. [FL99]) and minimizing the expected shortfall (efficient hedging, cf. [FL00]) yield, in fact, the same optimal investment policy. We furthermore present an innovative and model-free approach to goal-based investing using methods of reinforcement learning. To the best of our knowledge, we offer the first algorithmic approach to goal-based investing that can find optimal solutions in the presence of transaction costs.

Suggested Citation

  • Thomas Krabichler & Marcus Wunsch, 2021. "Hedging Goals," Papers 2105.07915, arXiv.org, revised Oct 2021.
    • Handle: RePEc:arx:papers:2105.07915
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    References listed on IDEAS

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    4. Oleg Szehr, 2021. "Hedging of Financial Derivative Contracts via Monte Carlo Tree Search," Papers 2102.06274, arXiv.org, revised Apr 2021.
    5. Hans FÃllmer & Peter Leukert, 2000. "Efficient hedging: Cost versus shortfall risk," Finance and Stochastics, Springer, vol. 4(2), pages 117-146.
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