Scholars Release Research Paper on Deep Rough Volatility Uncertainty and Interpretability
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Damiano Brigo, Raphaël Huser, and Dan Leonte have released a research paper addressing the calibration of the implied volatility surface for the rough Heston (rHeston) model, proposing a simulation-based inference framework. This framework can learn the posterior distribution of model parameters, combining neural ratio estimation with a heteroscedastic neural surrogate pricing model to generate option price ranges with uncertainty awareness.
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- arXiv q-finUncertainty and Interpretability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
For implied volatility surface calibration of the rough Heston (rHeston) model, a simulation-based inference framework is proposed to learn the posterior distribution of model parameters, combining neural ratio estimation with a heteroscedastic neural surrogate pricer to generate uncertainty-aware option price intervals.
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