论文标题
在深层顺序模型框架和状态空间模型框架的交集中：对期权定价的研究
At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing
论文作者
论文摘要
非线性动力学系统的推理和预测问题在各种情况下都出现了。一方面，储层计算和深层顺序模型在建模简单而混乱的动力学系统方面表现出了有效，稳健和卓越的性能。但是，他们天生的确定性特征部分损害了它们对嘈杂系统的鲁棒性，并且他们无法提供不确定性测量也是该框架的不足。另一方面，传统的状态空间模型框架对噪音是可靠的。它还具有测量的不确定性，形成了对储层计算和深层顺序模型框架的恰到好处补充。我们提出了无气的储层更顺畅，该模型统一了深层顺序和状态空间模型，以实现这两个框架的优势。在嘈杂的数据集之上，在选项定价设置中进行了评估，URS罢工高度竞争性预测精度，尤其是长期和不确定性测量的准确性。还讨论了对URS的进一步扩展和含义，以概括两个框架的完整整合。
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.
