Non-spanning expiries identify the no-event volatility surface while event-spanning quotes calibrate deterministic-time jumps, yielding better held-out pricing for SPX options around macro events than surface-absorbing or amortized alternatives.
A Generative Adversarial Network Approach to Calibration of Local Stochastic V olatility Models
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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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Non-Spanning Identification of Scheduled Event Risk in Option Pricing
Non-spanning expiries identify the no-event volatility surface while event-spanning quotes calibrate deterministic-time jumps, yielding better held-out pricing for SPX options around macro events than surface-absorbing or amortized alternatives.
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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.