多元CIR过程的摊销干预预测
Amortized Interventional Forecasting for Multivariate CIR Processes
AI总结：
本文针对多元CIR过程，提出摊销干预预测框架CIR-ACTIVA，可估计因果效应、预测多horizon冲击响应，在CDS利差测试中优于基线，能解答观测型方法无法处理的假设性查询。
AI中文摘要：
均值回复动态在金融领域普遍存在，Cox-Ingersoll-Ross（CIR）过程是生成这类时间序列的标准模型，涵盖短期利率到信用违约互换（CDS）利差等场景。然而CIR模型仅捕捉序列间的相关联动，无法体现序列间的因果影响，因此无法回答某一序列受外部冲击时系统的响应情况，而观测条件分布会将这种响应与历史联动混淆。本文有两项贡献：一是提出一种用于分布因果效应估计的摊销模型，该模型将轨迹视为带时间戳的观测值，无需针对每个场景重新训练即可预测校准后的多 horizon 冲击响应；二是提出一种因果多元CIR数据生成过程，可提供真实市场无法获取的配对观测与干预真实值。我们以CDS利差为测试平台实例化并校准该框架，在合成真实值上验证了CIR-ACTIVA的有效性（与模拟器匹配现实的程度无关），并通过将生成轨迹与真实CDS数据进行回测评估其实用性。与观测型和摊销因果推断基线相比，CIR-ACTIVA在联合分布的因果选择性及分horizon校准方面均表现更优，且当干预规律随horizon变化时仍能保持选择性，增益集中在短期horizon。这为耦合利差系统（包括CDS压力测试）开辟了一类假设性查询，这类查询是观测型预测器无法解答的。
英文摘要：
Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from short rates to credit default swap (CDS) spreads. Yet CIR models capture only \emph{correlated} co-movement, not \emph{causal} influence between series, so they cannot answer the system's response when one series is externally shocked, which observational conditionals confound with historical co-movement. We make two contributions. First, an amortized model for distributional causal effect estimation that frames trajectories as time-stamped observations and predicts the calibrated multi-horizon shock response without retraining per scenario. Second, a causal multivariate CIR data-generating process that supplies the paired observational and interventional ground truth that real markets cannot. We instantiate and calibrate the framework on CDS spreads as a testbed. CIR-ACTIVA's validity is established on synthetic ground truth, independent of how well the simulator matches reality, while practical grounding is assessed by backtesting the generated traces against real CDS data. Against observational and amortized causal-inference baselines, CIR-ACTIVA leads on both causal selectivity in the joint distribution and horizon-resolved calibration, retaining its selectivity once the interventional law varies over the horizon, with gains concentrating at short horizons. This opens up a class of what-if queries on coupled spread systems, CDS stress testing among them, that observational forecasters cannot answer.
