LQG for Portfolio Optimization

24 Pages Posted: 5 Nov 2016

See all articles by Marc Abeille

Marc Abeille

Institut National de Recherche en Informatique et Automatique (INRIA)

Emmanuel Sérié

Capital Fund Management

Alessandro Lazaric

Institut National de Recherche en Informatique et Automatique (INRIA)

Xavier Brokmann

Qube Research & Technologies

Date Written: November 3, 2016

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.

Keywords: Portfolio optimization, Impact, Return predictability, LQR, LQG, Riccati, State-space

JEL Classification: C02, C32, C61, D81, G11

Suggested Citation

Abeille, Marc and Sérié, Emmanuel and Lazaric, Alessandro and Brokmann, Xavier, LQG for Portfolio Optimization (November 3, 2016). Available at SSRN: https://ssrn.com/abstract=2863925 or http://dx.doi.org/10.2139/ssrn.2863925

Marc Abeille

Institut National de Recherche en Informatique et Automatique (INRIA) ( email )

40 avenue Halley
Villeneuve, 59650
France

Emmanuel Sérié (Contact Author)

Capital Fund Management ( email )

23 rue de l'Université
Paris, 75007
France

Alessandro Lazaric

Institut National de Recherche en Informatique et Automatique (INRIA) ( email )

40 avenue Halley
Villeneuve, 59650
France

Xavier Brokmann

Qube Research & Technologies ( email )

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