AI 中文总结
研究引入双因素随机波动率模型，其均值回复速度是市场注意力场的可观测函数，能生成封闭式波动率预测，嵌套Heston模型，在数据聚合下稳定，分析了粒子系统收敛性，模拟展示了注意力冲击后的相关差异。
AI 中文摘要
我们引入了一个双因素随机波动率模型，其中Cox-Ingersoll-Ross方差过程的均值回复速度是市场注意力场的可观测函数，而非潜在因素。该场有精确的Feynman-Kac表示并能生成封闭式波动率预测。此模型嵌套了Heston波动率模型，在可观测数据聚合下提供测量稳定的动态变化，并在等价鞅测度下保持其结构。均值场分析确定了基础粒子系统的收敛性，模拟展示了注意力冲击后的波动率和期权定价差异。
英文摘要
We introduce a two-factor stochastic volatility model in which the mean-reversion speed of a Cox--Ingersoll--Ross variance process is an observable functional of a market attention field rather than a latent factor. The field admits an exact Feynman--Kac representation and generates closed-form volatility forecasts. The model nests the Heston volatility model, provides measurement-stable dynamics under observable data aggregation, and preserves its structure under an equivalent martingale measure. A mean-field analysis establishes convergence of the underlying particle system, while simulations demonstrate volatility and option-pricing differences following attention shocks.
