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Autores principales: Lillo, Fabrizio, Mazzarisi, Piero, Tsaknaki, Ioanna-Yvonni
Formato: Preprint
Publicado: 2025
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Acceso en línea:https://arxiv.org/abs/2508.18868
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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.
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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