Neural calibration of financial models should quantify posterior parameter uncertainty and propagate it through pricing models—point estimates alone can be materially unreliable for exotic derivatives, and information-theoretic explainability reveals which market data regions constrain which pa...
This paper develops a neural framework for calibrating rough Heston volatility models that captures parameter uncertainty rather than just point estimates. It combines simulation-based inference with neural surrogates to produce uncertainty-aware price intervals for exotic options, and introduces an explainability method to identify which market observations drive parameter learning.