Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not capture the uncertainty remaining after an implied-volatility (IV) surface has been observed. We develop a simulation-based inference framework for rough Heston (rHeston) calibration that learns the posterior distribution of the model
Reading in Plain English mode — toggle below to switch
Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not capture the uncertainty remaining after an implied-volatility (IV) surface has been observed. We develop a simulation-based inference framework for rough Heston (rHeston) calibration that learns the posterior distribution of the model
This paper introduces a novel framework for quantifying uncertainty in complex financial models using deep learning, moving beyond point estimates to full posterior distributions. This is crucial for robust decision-making in high-stakes financial applications and demonstrates how ML can provide more nuanced insights than traditional methods.