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Stochastic processes adapted by neural networks with application to climate, energy, and finance
Date
2011-10-01
Author
Giebel, Stefan
Rainer, Martin
Metadata
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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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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.