Public articles linked to the same research event.
arXiv Using the complementary Shapley-based explainability methods SHAP and νSHAP, this work analyzes neural calibration mappings for the Heston and rough Heston models across multilayer perceptron, highway, and softmax-parametrized highway architectures, finding that short maturities and smile wings consistently dominate parameter inference and that the dominant attribution structure remains qualitatively stable across architectures, and it uses the input redundancy revealed by νSHAP to substantially reduce input dimensionality for rough Heston while matching calibration accuracy relative to the full implied volatility surface.
Using the complementary Shapley-based explainability methods SHAP and νSHAP, this work analyzes neural calibration mappings for the Heston and rough Heston models across multilayer perceptron, highway, and softmax-parametrized highway architectures, finding that short maturities and smile wings consistently dominate parameter inference and that the dominant attribution structure remains qualitatively stable across architectures, and it uses the input redundancy revealed by νSHAP to substantially reduce input dimensionality for rough Heston while matching calibration accuracy relative to the full implied volatility surface.
Using the complementary Shapley-based explainability methods SHAP and νSHAP, this work analyzes neural calibration mappings for the Heston and rough Heston models across multilayer perceptron, highway, and softmax-parametrized highway architectures, finding that short maturities and smile wings consistently dominate parameter inference and that the dominant attribution structure remains qualitatively stable across architectures, and it uses the input redundancy revealed by νSHAP to substantially reduce input dimensionality for rough Heston while matching calibration accuracy relative to the full implied volatility surface.
Using the complementary Shapley-based explainability methods SHAP and νSHAP, this work analyzes neural calibration mappings for the Heston and rough Heston models across multilayer perceptron, highway, and softmax-parametrized highway architectures, finding that short maturities and smile wings consistently dominate parameter inference and that the dominant attribution structure remains qualitatively stable across architectures, and it uses the input redundancy revealed by νSHAP to substantially reduce input dimensionality for rough Heston while matching calibration accuracy relative to the full implied volatility surface.