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.
MCMC Estimation of Levy Jump Models Using Stock and Option Prices
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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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.