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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2508.18868 |
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| _version_ | 1866908625170071552 |
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| author | Lillo, Fabrizio Mazzarisi, Piero Tsaknaki, Ioanna-Yvonni |
| author_facet | Lillo, Fabrizio Mazzarisi, Piero Tsaknaki, Ioanna-Yvonni |
| contents | The Kelly criterion provides a general framework for optimizing the growth rate of an investment portfolio over time by maximizing the expected logarithmic utility of wealth. However, the optimality condition of the Kelly criterion is highly sensitive to accurate estimates of the probabilities and investment payoffs. Estimation risk can lead to greatly suboptimal portfolios. In a simple binomial model, we show that the introduction of a European option in the Kelly framework can be used to construct a class of growth optimal portfolios that are robust to estimation risk. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tackling estimation risk in Kelly investing using options Lillo, Fabrizio Mazzarisi, Piero Tsaknaki, Ioanna-Yvonni Mathematical Finance The Kelly criterion provides a general framework for optimizing the growth rate of an investment portfolio over time by maximizing the expected logarithmic utility of wealth. However, the optimality condition of the Kelly criterion is highly sensitive to accurate estimates of the probabilities and investment payoffs. Estimation risk can lead to greatly suboptimal portfolios. In a simple binomial model, we show that the introduction of a European option in the Kelly framework can be used to construct a class of growth optimal portfolios that are robust to estimation risk. |
| title | Tackling estimation risk in Kelly investing using options |
| topic | Mathematical Finance |
| url | https://arxiv.org/abs/2508.18868 |