Forecasting Exchange Rates: a Comparative Analysis

Vincenzo Pacelli

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Forecasting Techniques and Applications Financial Risk and Volatility Modeling Stock Market Forecasting Methods

Abstract

Abstract \n \nThis research aims to analyze and to compare the ability of different mathematical models, such as artificial neural networks (ANN) and ARCH and GARCH models, to forecast the daily exchange rates Euro/U.S. dollar (USD), identifying which, among all the models applied, produces more accurate forecasts. By empirically comparing the different mathematical models developed in this research, the traditional indicators for assessing the relevance of the models show that the ARCH and GARCH models, especially in their static formulations, are better than the ANN for analyzing and forecasting the dynamics of the exchange rates.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 1965The Behavior of Stock-Market Prices 1995Multivariate Simultaneous Generalized ARCH 1997The variation of certain speculative prices 1983Empirical exchange rate models of the seventies

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2014Regime-switching models for exchange rates

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