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

• URL: http://arxiv.org/abs/2012.07784v1
• Date: Mon, 14 Dec 2020 18:21:41 GMT
• Title: At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing
• Authors: Ziyang Ding and Sayan Mukherjee
• Abstract summary: Inference and forecast problems of the nonlinear dynamical system have arisen in a variety of contexts. We propose a model that unifies both deep sequential and state-space models to achieve both frameworks' superiorities.
• Score: 2.3224617218247126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model [23.40376181606577]
  We present TrackDiffuser, a generative framework addressing both challenges by reformulating Bayesian filtering as a conditional diffusion model. Our approach implicitly learns system dynamics from data to mitigate the effects of inaccurate SSM. TrackDiffuser exhibits remarkable robustness to SSM inaccuracies, offering a practical solution for real-world state estimation problems.
  arXiv  Detail & Related papers  (2025-02-08T16:21:18Z)
• Stability-Certified Learning of Control Systems with Quadratic Nonlinearities [9.599029891108229]
  This work primarily focuses on an operator inference methodology aimed at constructing low-dimensional dynamical models. Our main objective is to develop a method that facilitates the inference of quadratic control dynamical systems with inherent stability guarantees.
  arXiv  Detail & Related papers  (2024-03-01T16:26:47Z)
• Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractions [44.99833362998488]
  We develop an abstraction framework that can be used to solve this problem under various modeling assumptions. We use state-of-the-art verification techniques to compute an optimal policy on the iMDP with guarantees for satisfying the given specification. We then show that, by construction, we can refine this policy into a feedback controller for which these guarantees carry over to the dynamical model.
  arXiv  Detail & Related papers  (2023-11-16T11:03:54Z)
• Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness [7.158144011836533]
  This work explore the dynamic attributes in model level through dynamic ensemble selection technology. In training phase the Dirichlet distribution is apply as prior of sub-models' predictive distribution, and the diversity constraint in parameter space is introduced. In test phase, the certain sub-models are dynamically selected based on their rank of uncertainty value for the final prediction.
  arXiv  Detail & Related papers  (2023-08-01T07:41:41Z)
• Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
  The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree. We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
  arXiv  Detail & Related papers  (2023-07-19T12:11:15Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS [20.348825818435767]
  We present a framework for high-resolution predictive uncertainty quantification of semantic segmentation models. We use a multi-moment functional definition of uncertainty associated with the model's feature space in the kernel reproducing the Hilbert space (RKHS) This leads to a significantly more accurate view of model uncertainty than conventional Bayesian methods.
  arXiv  Detail & Related papers  (2022-11-03T17:10:49Z)
• Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty [68.00748155945047]
  Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
  arXiv  Detail & Related papers  (2022-10-12T07:57:03Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization [60.73540999409032]
  We show that expressive autoregressive dynamics models generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We also show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer.
  arXiv  Detail & Related papers  (2021-04-28T16:48:44Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.