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Bootstrapping and Jackknifing Neural Networks for Noisy Financial Time Series

Author

Listed:
  • Peter Kim
  • Lingxue Pan
  • Tony Wirjanto

    (Department of Economics, University of Waterloo)

Abstract

In this paper we introduce resampling techniques to a multi-layer feed-forward neural network model for noisy financial time series in order to obtain more reliable interval forecasts of the time series along with a large amount of statistical information associated with the observed data. In particular, we develop two new grouped jackknife learning algorithms from cross-validation back-propagation learning as well as two new bootstrap cross-validation learning algorithms inspired by the parametric and nonparametric modelling strategy to be used on the neural network model selected from pre-tests. Our applicationis in forecasting the spot Canada/US foreign exchange rate, using the daily data from January 2, 1984 to October 1, 1996 and exploiting the existence of a stable transmission link between the spot rate and the short-term interest rate spread.

Suggested Citation

  • Peter Kim & Lingxue Pan & Tony Wirjanto, 1999. "Bootstrapping and Jackknifing Neural Networks for Noisy Financial Time Series," Working Papers 99003, University of Waterloo, Department of Economics, revised Apr 1999.
    • Handle: RePEc:wat:wpaper:99003
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