|Publication type:||Working paper – expertise – study|
|Publisher / Ed. Institution:||arXiv|
|Subject (DDC):||332.6: Investment|
|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.|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Management and Law|
|Organisational Unit:||Institute of Wealth & Asset Management (IWA)|
|Appears in collections:||Publikationen School of Management and Law|
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Krabichler, T., & Wunsch, M. (2021). Hedging goals. arXiv. https://arxiv.org/abs/2105.07915
Krabichler, T. and Wunsch, M. (2021) Hedging goals. arXiv. Available at: https://arxiv.org/abs/2105.07915.
T. Krabichler and M. Wunsch, “Hedging goals,” arXiv, 2021. [Online]. Available: https://arxiv.org/abs/2105.07915
Krabichler, Thomas, and Marcus Wunsch. Hedging Goals. arXiv, 2021, https://arxiv.org/abs/2105.07915.
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