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NeuroCOLT
Technical Report NC-TR-95-031
A Comparative Study For Forecasting Intra-daily
Exchange Rate Data
Sabine
P Toulson
London School of Economics
University of London
Abstract
For the last few years neural nets have been applied to economic
and financial forecasting where they have shown to be increasingly
successful. This paper compares the performance of a two hidden layer
multi-layer perceptron (MLP) with conventional statistical techniques.
The statistical techniques used here consist of a structural model
(SM) and the stochastic volatility model (SV). After reviewing each
of the three models a comparison between the MLP and the SM is made
investigating the predictive power of both models for a one-step ahead
forecast of the Dollar-Deutschmark exchange rate. Reasons are given
for why the MLP is expected to perform better than a conventional
model in this case. A further study gives results on the performance
of an MLP and a SV model in predicting the volatility of the Dollar-
Deutschmark exchange rate and a combination of both models is proposed
to decrease the forecasting error.
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Postscript
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