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

TL;DR

This paper introduces the unscented reservoir smoother, a unified model combining deep sequential and state-space frameworks, to improve option pricing accuracy and uncertainty measurement in noisy environments.

Contribution

It proposes a novel model that unifies deep sequential models with state-space models, enhancing robustness and uncertainty quantification in nonlinear dynamical systems.

Findings

Achieves high forecasting accuracy in noisy option pricing data.

Provides effective uncertainty measurement.

Performs well on long-term forecasts.

Abstract

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.