A neural-network actor-critic policy gradient algorithm with squashed Gaussian C-vine policies solves high-dimensional robust pricing problems in the uncertain volatility model and outperforms existing Monte Carlo and ML benchmarks in numerical tests.
Deep neural networks algorithms for stochastic control problems on finite horizon: Numerical applications
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Stochastic Policy Gradient Methods in the Uncertain Volatility Model
A neural-network actor-critic policy gradient algorithm with squashed Gaussian C-vine policies solves high-dimensional robust pricing problems in the uncertain volatility model and outperforms existing Monte Carlo and ML benchmarks in numerical tests.