Stochastic processes adapted by neural networks with application to climate, energy, and finance |
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Authors: | Stefan Giebel Martin Rainer |
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Institution: | a University of Luxembourg, Luxembourg b ENAMEC Institute, Würzburg, Germany c Institute of Applied Mathematics, Middle East Technical University, Ankara, Turkey d Faculty of Commerce, Yeditepe University, Istanbul, Turkey |
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Abstract: | 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. |
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Keywords: | Stochastic processes Neural networks Computational finance |
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