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On Nonlinear, Stochastic Dynamics in Economic and Financial Time Series

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Author Info
Christian Schittenkopf (Austrian Research Institute for Artificial Intelligence)
Georg Dorffner (University of Vienna)
Engelbert Dockner (University of Vienna)
Abstract

The search for deterministic chaos in economic and financial time series has attracted much interest over the past decade. Evidence of chaotic structures is usually blurred, however, by large random components in the time series. In the first part of this paper, a sophisticated algorithm for estimating the largest Lyapunov exponent with confidence intervals is applied to artificially generated and real-world time series. Although the possibility of testing empirically for positivity of the estimated largest Lyapunov exponent is an advantage over other existing methods, the interpretability of the obtained results remains problematic. For instance, it is practically impossible to distinguish chaotic and periodic dynamics in the presence of dynamical noise even for simple dynamical systems. We conclude that the notion of sensitive dependence on initial conditions, as it has been developed for deterministic dynamics, can hardly be transferred into a stochastic context. Therefore, the second part of the paper aims to measure the dependencies of stochastic dynamics on the basis of a distributional characterization of the dynamics. For instance, the dynamics of financial return series are essentially captured by heteroskedastic models. We adopt a sensitivity measure proposed in literature and derive analytical expressions for the most important classes of stochastic dynamics. In practice, the sensitivity measure for the a priori unknown dynamics of a system can be calculated after estimating the conditional density of the system's state variable.

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Publisher Info
Article provided by Berkeley Electronic Press in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 4 (2000)
Issue (Month): 3 ()
Pages: 101-121
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Handle: RePEc:bep:sndecm:4:2000:3:101-121

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Related research
Keywords: chaos Lyapunov exponents stochastic dynamics time series

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Kurt Hornik & Maxwell Stinchcombe & Halbert White, 1990. "Universal Approximation of an Unknown Mapping And Its Derivatives Using Multilayer Feedforward Networks," University of California at San Diego, Economics Working Paper Series 89-36r, Department of Economics, UC San Diego.
  2. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," Journal of Business, University of Chicago Press, vol. 62(3), pages 311-37, July. [Downloadable!] (restricted)
  3. Brock, W. A., 1986. "Distinguishing random and deterministic systems: Abridged version," Journal of Economic Theory, Elsevier, vol. 40(1), pages 168-195, October. [Downloadable!] (restricted)
  4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
  5. repec:fth:guelph:1988-15 is not listed on IDEAS
  6. Frank, Murray & Gencay, Ramazan & Stengos, Thanasis, 1988. "International chaos?," European Economic Review, Elsevier, vol. 32(8), pages 1569-1584, October. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Paul De Grauwe & Marianna Grimaldi, 2003. "Intervention in the Foreign Exchange Market in a Model with Noise Traders," Working Papers 162003, Hong Kong Institute for Monetary Research. [Downloadable!]
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