发表机构
Monodromy（英国Monodromy公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对波动率预测中非线性模型可利用结构有限的问题，提出易感性架构（SUSA）及具体实现，结合复值蓄水池与状态条件专家，在Qiskit中实现q比特对应物，经实验评估，模型在与GARCH竞争及补充HARQ预测方面有良好表现。
AI 中文摘要
波动率预测受持续性和测量噪声主导，留给非线性模型利用的残余结构有限。我们引入易感性架构（SUSA），一种用于波动率预测的蓄水池设计原则及其两种具体实现，基于复值开链和周期性蓄水池以及状态条件专家来解释平静、起始、恢复和持续压力状态下的蓄水池特征。我们还在Qiskit中实现了开放系统q比特对应物，同时保留通用的AR - 岭锚点和在QLIKE下训练的有界残余校正。我们使用三个不相交的按时间顺序排列的训练、验证和测试折、12个观测值的输入窗口和5个观测值的预测范围，对16个美国股票和交易所交易基金序列评估模型。所提出的模型与GARCH竞争，对特定资产（IWM，XLP）实现了统计学上显著的QLIKE改进。模型预测还补充了HARQ风格的预测：堆叠集成比其最强组成部分的平均QLIKE提高了0.0116，并在75%的测试场景中获胜。
英文摘要
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.
Comments15 pages, 6 figures
