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

Giebel S., Rainer M.

APPLIED MATHEMATICS AND COMPUTATION, vol.218, no.3, pp.1003-1007, 2011 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 218 Issue: 3
  • Publication Date: 2011
  • Doi Number: 10.1016/j.amc.2011.03.121
  • Page Numbers: pp.1003-1007


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. (C) 2011 Elsevier Inc. All rights reserved.