At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing
This work is significant for financial modelers and quantitative analysts who need robust and accurate option pricing models that can handle noisy data and provide uncertainty estimates.
This paper addresses the challenge of modeling nonlinear dynamical systems, particularly in the presence of noise and the need for uncertainty quantification. The authors propose the Unscented Reservoir Smoother (URS), which combines deep sequential models with state-space models to achieve competitive forecasting accuracy and uncertainty measurement in option pricing, especially for longer-term predictions.
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.