提出可响应市场状态的波动率预测架构，提升准确性。
Susceptible Reservoir Architectures for Regime-Conditional Volatility Forecasting
- 设计能感知市场状态的储层结构，动态捕捉波动特征
- 在IWM、XLP等资产上显著优于GARCH模型（QLIKE提升）
- 适合关注金融风险建模与多状态预测的研究者
波动率预测受持久性与测量噪声主导，留给非线性模型可利用的残余结构有限。本文提出适用于波动率预测的易感架构（SUSA），包含基于复值开链与周期性储层的两种实现，并引入分状态专家机制，以解析平静、启动、恢复及持续压力状态下的储层特征。同时在Qiskit中实现开系统q-qubit版本，保持统一的AR-Ridge基准与受限残差校正，训练目标为QLIKE。在16个美国股票及交易所交易基金序列上评估，采用三个不重叠的时间划分训练/验证/测试集，输入窗口12个观测值，预测范围5个观测值。所提模型表现媲美GARCH，在特定资产（IWM、XLP）上实现统计显著的QLIKE改进；其预测还与HARQ风格互补：堆叠集成模型使平均QLIKE提升0.0116，且在75%测试场景中胜出。
原文摘要 · Abstract (English)
Volatility forecasting is dominated by persistence and measurement noise, leaving limited residual structure for nonlinear models to exploit. We introduce Susceptible Architectures (SUSA), a reservoir-design principle for volatility forecasting, and its two concrete implementations, based on complex-valued open-chain and periodic reservoirs and regime-conditioned experts to interpret reservoir features across calm, onset, recovery, and persistent-stress states. We also implement open-system $q$-qubit counterparts in Qiskit while retaining a common AR-Ridge anchor and a bounded residual correction trained under QLIKE. We evaluate models on 16 U.S. equity and exchange-traded-fund series using three disjoint chronological training, validation, and test folds, a 12-observation input window, and a five-observation forecast horizon. The proposed models perform competitively with GARCH, achieving statistically significant QLIKE improvements for specific assets (IWM, XLP). Also models' forecasts complement HARQ-style predictions: a stacked ensemble improves mean QLIKE by 0.0116 over its strongest constituent and wins in 75% of test scenarios.
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