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LQG for portfolio optimization

Author

Listed:
  • M. Abeille
  • E. Serie
  • A. Lazaric
  • X. Brokmann
Abstract
We introduce a generic solver for dynamic portfolio allocation problems when the market exhibits return predictability, price impact and partial observability. We assume that the price modeling can be encoded into a linear state-space and we demonstrate how the problem then falls into the LQG framework. We derive the optimal control policy and introduce analytical tools that preserve the intelligibility of the solution. Furthermore, we link the existence and uniqueness of the optimal controller to a dynamical non-arbitrage criterion. Finally, we illustrate our method using a synthetic portfolio allocation problem.

Suggested Citation

  • M. Abeille & E. Serie & A. Lazaric & X. Brokmann, 2016. "LQG for portfolio optimization," Papers 1611.00997, arXiv.org, revised Nov 2016.
    • Handle: RePEc:arx:papers:1611.00997
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    References listed on IDEAS

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    Cited by:

    1. Ayman Chaouki & Stephen Hardiman & Christian Schmidt & Emmanuel S'eri'e & Joachim de Lataillade, 2020. "Deep Deterministic Portfolio Optimization," Papers 2003.06497, arXiv.org, revised Apr 2020.

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