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.
Trust region policy optimization
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
q-fin.CP 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.