Bootstrapping and Jackknifing Neural Networks for Noisy Financial Time Series

Peter Kim, Lingxue Pan and Tony Wirjanto ()

No 99003, Working Papers from University of Waterloo, Department of Economics

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.

Date: 1999-04, Revised 1999-04
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Persistent link: https://EconPapers.repec.org/RePEc:wat:wpaper:99003

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