The Gibbs Posterior and Parametric Portfolio Choice
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914377328754688 |
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| author | Lamoureux, Christopher G. |
| author_facet | Lamoureux, Christopher G. |
| contents | Parametric portfolio policies may experience estimation risk. I develop a generalized Bayesian framework that updates priors, delivering a posterior distribution over characteristic tilts and out-of-sample returns that is the unique belief-updating rule consistent with the investor's utility function, requiring no model for the return generating process. The Gibbs posterior is the closest distribution to the prior in Kullback-Leibler divergence subject to utility maximization. The posterior's scaling parameter $λ$ controls the weight placed on data relative to the prior. I develop a KNEEDLE algorithm to select optimal $λ^*$ in-sample by trading off posterior precision against numerical fragility, eliminating the need for out-of-sample validation. I apply this to U.S. equities (1955-2024), and confirm characteristic-based gains concentrate pre-2000. I find that $λ^*$ varies meaningfully with risk aversion and depends on higher-order moments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02455 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Gibbs Posterior and Parametric Portfolio Choice Lamoureux, Christopher G. Portfolio Management Parametric portfolio policies may experience estimation risk. I develop a generalized Bayesian framework that updates priors, delivering a posterior distribution over characteristic tilts and out-of-sample returns that is the unique belief-updating rule consistent with the investor's utility function, requiring no model for the return generating process. The Gibbs posterior is the closest distribution to the prior in Kullback-Leibler divergence subject to utility maximization. The posterior's scaling parameter $λ$ controls the weight placed on data relative to the prior. I develop a KNEEDLE algorithm to select optimal $λ^*$ in-sample by trading off posterior precision against numerical fragility, eliminating the need for out-of-sample validation. I apply this to U.S. equities (1955-2024), and confirm characteristic-based gains concentrate pre-2000. I find that $λ^*$ varies meaningfully with risk aversion and depends on higher-order moments. |
| title | The Gibbs Posterior and Parametric Portfolio Choice |
| topic | Portfolio Management |
| url | https://arxiv.org/abs/2603.02455 |