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Found 5 papers, 0 papers with code
Policy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing
We develop policy gradients methods for stochastic control with exit time in a model-free setting.
Optimal Consumption with Reference to Past Spending Maximum
This paper studies the infinite-horizon optimal consumption with a path-dependent reference under exponential utility.
Neural networks-based backward scheme for fully nonlinear PDEs
We propose a numerical method for solving high dimensional fully nonlinear partial differential equations (PDEs).
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications
This paper presents several numerical applications of deep learning-based algorithms that have been introduced in [HPBL18].
Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach
The dynamic setting allows us to consider time varying ambiguity sets, which include the cases where the drift and correlation are estimated on a rolling window of historical data or when the investor takes into account learning on the ambiguity.
