在深度序列模型框架与状态空间模型框架交界处：期权定价研究
At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing
AI总结：
本文提出无偏共振平滑器模型，统一深度序列模型和状态空间模型，提升期权定价中的预测准确性和不确定性度量能力。
摘要：
非线性动力系统推理和预测问题在各种背景下出现。一方面，共振计算和深度序列模型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，其固有的确定性特征在一定程度上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不足之处。另一方面，传统状态空间模型框架对噪声具有鲁棒性，并且能够测量不确定性，从而成为共振计算和深度序列模型框架的恰到好处的补充。我们提出了一种无偏共振平滑器模型，该模型统一了深度序列模型和状态空间模型，以实现两个框架的优越性。在噪声数据集上进行期权定价设置的评估中，URS在预测准确性方面表现高度竞争，尤其是长期预测的准确性，以及不确定性度量。进一步讨论了URS的扩展和意义，以推广两个框架的全面整合。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
