CARLOS employs an aggregate deep neural network trained on progressively finer time grids with adaptive sampling to learn continuous-time exercise boundaries for optimal stopping, delivering higher values than discrete Bermudan methods.
MCMC Estimation of Levy Jump Models Using Stock and Option Prices
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Defines resilience evaluation D^ρ π as the L1-limit of scaled dynamic risk measure applied to process increments, and derives its dual representation as worst-case conditional expectation of an effective drift when ρ arises from BSDEs with Lipschitz or quadratic drivers.
Bayesian neural SDE calibration produces posterior mixtures that deliver robust bounds on implied volatility by jointly using historical and option data, learning the historical-to-risk-neutral measure change, and sampling via Langevin dynamics.
Develops a singular stochastic control model for optimal execution with stochastic resilience dynamics and regime-switching liquidity, proving the value function is the unique viscosity solution to a system of variational HJB inequalities.
citing papers explorer
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Continuous-time Optimal Stopping through Deep Reinforcement Learning
CARLOS employs an aggregate deep neural network trained on progressively finer time grids with adaptive sampling to learn continuous-time exercise boundaries for optimal stopping, delivering higher values than discrete Bermudan methods.
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Financial Resilience Evaluation: From Conditional Expectations to Dynamic Convex Risk Measures
Defines resilience evaluation D^ρ π as the L1-limit of scaled dynamic risk measure applied to process increments, and derives its dual representation as worst-case conditional expectation of an effective drift when ρ arises from BSDEs with Lipschitz or quadratic drivers.
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Robust financial calibration: a Bayesian approach for neural SDEs
Bayesian neural SDE calibration produces posterior mixtures that deliver robust bounds on implied volatility by jointly using historical and option data, learning the historical-to-risk-neutral measure change, and sampling via Langevin dynamics.
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Optimal Execution under Liquidity Uncertainty
Develops a singular stochastic control model for optimal execution with stochastic resilience dynamics and regime-switching liquidity, proving the value function is the unique viscosity solution to a system of variational HJB inequalities.