Stochastic processes adapted by neural networks with application to climate, energy, and finance

Date

2011-10-01

Author

Giebel, Stefan
Rainer, Martin

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Local climate parameters may naturally effect the price of many commodities and their derivatives. Therefore we propose a joint framework for stochastic modeling of climate and commodity prices. In our setting, a stable Levy process is drift augmented to a generalized SDE. The related nonlinear function on the state space typically exhibits deterministic chaos. Additionally, a neural network adapts the parameters of the stable process such that the latter produces increasingly optimal differences between simulated output and observed data. Thus we propose a novel method of "intelligent'' calibration of the stochastic process, using learning neural networks in order to dynamically adapt the parameters of the stochastic model.

Subject Keywords

Applied Mathematics, Computational Mathematics

URI

https://hdl.handle.net/11511/65182

Journal

APPLIED MATHEMATICS AND COMPUTATION

DOI

https://doi.org/10.1016/j.amc.2011.03.121

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

S. Giebel and M. Rainer, “Stochastic processes adapted by neural networks with application to climate, energy, and finance,” APPLIED MATHEMATICS AND COMPUTATION, pp. 1003–1007, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65182.