发表机构
Imperial College; King Abdullah University of Science and Technology（帝国理工学院; 阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于模拟推理的粗糙Heston模型校准框架，利用神经比率估计学习参数后验分布，并引入Hellinger-SHAP可解释性方法，以提供不确定性感知的价格区间和参数信息增益分析。
AI 中文摘要
深度学习已大幅加速了复杂随机波动率模型的校准，但仅靠神经点校准并不能捕捉到在观察到隐含波动率（IV）曲面后仍存在的不确定性。我们开发了一个基于模拟的推理框架，用于粗糙Heston（rHeston）模型校准，该框架学习以IV曲面为条件的模型参数的后验分布。利用神经比率估计，我们获得校准的后验样本，这些样本可通过异方差神经替代定价器传播，用于路径依赖的奇异期权。由此产生的后验预测分布将残差参数不确定性与条件替代不确定性相结合，并产生具有不确定性意识的价格区间。我们进一步引入了Hellinger-SHAP，一种用于后验推理的信息论可解释性方法。它不是归因于单个参数点估计，而是将局部背景核SHAP应用于一个后验信息泛函，该泛函衡量从先验到后验的收缩。这识别了与各个rHeston参数的后验信息增益相关的成熟度-货币性区域。在一项模拟研究中，后验预测区间在前瞻起始、障碍和已实现方差索赔中提供了校准或保守的覆盖率，而点插入价格在选定的合约制度下可能严重不可靠。总之，UQ和XAI分析为在指定先验预测模型下进行具有不确定性意识的神经校准和下游奇异定价提供了一个透明的框架。
英文摘要
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 parameters conditional on an IV surface. Using neural ratio estimation, we obtain calibrated posterior samples that can be propagated through heteroscedastic neural surrogate pricers for path-dependent exotic options. The resulting posterior-predictive distributions combine residual parameter uncertainty with conditional surrogate uncertainty and yield uncertainty-aware price intervals. We further introduce Hellinger-SHAP, an information-theoretic explainability method for posterior inference. Rather than attributing a single parameter point estimate, it applies local-background Kernel SHAP to a posterior-information functional measuring contraction from the prior to the posterior. This identifies maturity--moneyness regions associated with posterior information gain for individual rHeston parameters. In a simulation study, posterior-predictive intervals provide calibrated or conservative coverage across forward-start, barrier, and realized-variance claims, while point plug-in prices can be materially unreliable for selected contract regimes. Together, the UQ and XAI analyses provide a transparent framework for uncertainty-aware neural calibration and downstream exotic pricing under the specified prior-predictive model.
