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When Bayesian learning meets stochastic optimal control : Optimal portfolio choice under drift uncertainty| old_uid | 13538 |
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| title | When Bayesian learning meets stochastic optimal control : Optimal portfolio choice under drift uncertainty |
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| start_date | 2017/03/24 |
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| schedule | 11h30 |
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| online | no |
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| details | Keywords : Bayesian learning, Hamilton-Jacobi-Bellman, duality, PDE, portfolio optimization |
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| summary | We shall present several models addressing optimal portfolio choice and optimal portfolio transition issues, in which the expected returns of risky assets are unknown. Our approach is based on a coupling between Bayesian learning and dynamic programming techniques. It permits to recover the well-known results of Karatzas and Zhao in the case of conjugate (Gaussian) priors for the drift distribution, but also to go beyond the no-friction case, when martingale methods are no longer available. In particular, we address optimal portfolio choice in a framework à la Almgren-Chriss and we build therefore a model in which the agent takes into account in his/her allocation decision process both the liquidity of assets and the uncertainty with respect to their expected returns. We also address optimal portfolio liquidation and optimal portfolio transition problems. |
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| responsibles | Randon-Furling |
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