Forecasting Exchange Rates Using General Regression Neural Networks
Posted: 1 Oct 2000
In this study, we examine the forecastability of a specific neural network architecture called General Regression Neural Network (GRNN) and compare its performance with a variety of forecasting techniques, including Multi-Layered Feedforward Network (MLFN), multivariate transfer function, and random walk models. The comparison with MLFN provides a measure of GRNN's performance relative to the more conventional type of neural networks while the comparison with transfer function models examines the difference in predictive strength between the non-parametric and parametric techniques. The random walk model is used for benchmark comparison. Our findings show that GRNN not only has a higher degree of forecasting accuracy but also performs statistically better than other evaluated models for different currencies.
Note: This is a description of the paper and is not the actual abstract.
Keywords: general regression neural networks, currency exchange rate, forecasting
JEL Classification: G15, C45, C53
Suggested Citation: Suggested Citation