标题： 深度序列模型框架与状态空间模型框架的交叉点：期权定价研究 显示英文标题

标题： At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing

摘要： 非线性动力系统推理和预测问题在各种背景下出现。 储备计算和深度序列模型一方面在建模简单和混沌动力系统方面表现出高效、稳健和优越的性能。 然而，它们固有的确定性特征部分削弱了其对噪声系统的鲁棒性，且无法提供不确定性度量也是该框架的一个不足之处。 另一方面，传统的状态空间模型框架对噪声具有鲁棒性。 它还包含测量的不确定性，形成了对储备计算和深度序列模型框架的恰到好处的补充。 我们提出了无迹储备平滑器，这是一种统一深度序列和状态空间模型的模型，以实现两个框架的优势。 在基于噪声数据集的期权定价设置中进行评估，URS在长期预测和不确定性度量方面表现出高度竞争力。 还讨论了URS的进一步扩展和含义，以推广两个框架的完整集成。 显示英文摘要

摘要： Inference and forecast problems of the nonlinear dynamical system have arisen in a variety of contexts. Reservoir computing and deep sequential models, on the one hand, have demonstrated efficient, robust, and superior performance in modeling simple and chaotic dynamical systems. However, their innate deterministic feature has partially detracted their robustness to noisy system, and their inability to offer uncertainty measurement has also been an insufficiency of the framework. On the other hand, the traditional state-space model framework is robust to noise. It also carries measured uncertainty, forming a just-right complement to the reservoir computing and deep sequential model framework. We propose the unscented reservoir smoother, a model that unifies both deep sequential and state-space models to achieve both frameworks' superiorities. Evaluated in the option pricing setting on top of noisy datasets, URS strikes highly competitive forecasting accuracy, especially those of longer-term, and uncertainty measurement. Further extensions and implications on URS are also discussed to generalize a full integration of both frameworks.