Establishes convergence and stability of fully data-driven value iteration for stochastic LQR with unknown dynamics and introduces a robust ADP algorithm requiring no initial admissible policy.
LQG for portfolio optimization
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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.
fields
math.OC 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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A Fully Data-Driven Value Iteration for Stochastic LQR: Convergence, Robustness and Stability
Establishes convergence and stability of fully data-driven value iteration for stochastic LQR with unknown dynamics and introduces a robust ADP algorithm requiring no initial admissible policy.