An Artificial Neural Network-Based Approach to the Monetary Model of Exchange Rate

This paper aims to investigate the predictive accuracy of the flexible price monetary model of the exchange rate, estimated by an approach based on combining the vector autoregressive model and multilayer feedforward neural networks. The forecasting performance of this nonlinear, nonparametric model is analyzed comparatively with a monetary model estimated in a linear static framework; the monetary model estimated in a linear dynamic vector autoregressive framework; the monetary model estimated in a parametric nonlinear dynamic threshold vector autoregressive framework; and the naïve random walk model applied to six different exchange rates over three forecasting periods. The models are compared in terms of both the magnitude of their forecast errors and the economic value of their forecasts. The proposed model yielded promising outcomes by performing better than the random walk model in 16 out of 18 instances in terms of the root mean square error and 15 out of 18 instances in terms of mean return and Sharpe ratio. The model also performed better than linear models in 17 out of 18 instances for root mean square error and 14 out of 18 instances for mean returns and Sharpe ratio. The distinguishing feature of the proposed model versus the present models in the literature is its robustness to outperform the random walk model, regardless of whether the magnitude of forecast errors or the economic value of the forecasts is chosen as a performance measure.

[1]  Nathan Lael Joseph,et al.  Model Specification and Forecasting Foreign Exchange Rates with Vector Autoregressions , 2001 .

[2]  Mark P. Taylor,et al.  Nonlinear adjustment, long-run equilibrium and exchange rate fundamentals , 2000 .

[3]  Show-lin Chen,et al.  Nominal exchange-rate prediction: evidence from a nonlinear approach , 2001 .

[4]  Florian Huber.  Forecasting exchange rates using multivariate threshold models , 2015 .

[5]  John F. O. Bilson.  The Monetary Approach to the Exchange Rate: Some Empirical Evidence , 1978 .

[6]  Hujun Yin,et al.  Exchange rate prediction using hybrid neural networks and trading indicators , 2009, Neurocomputing.

[7]  Alicia M. Gazely,et al.  Forecasting the UK/US Exchange Rate with Divisia Monetary Models and Neural Networks , 2011 .

[8]  Geoffrey E. Hinton,et al.  How neural networks learn from experience. , 1992, Scientific American.

[9]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[10]  Mehdi Khashei,et al.  Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets , 2013, Int. J. Comput. Intell. Syst..

[11]  D. Hammerstrom,et al.  Neural networks at work , 1993, IEEE Spectrum.

[12]  Giorgio Valente,et al.  Exchange Rates and Fundamentals: Evidence on the Economic Value of Predictability , 2004 .

[13]  Berlin Wu,et al.  Model-free forecasting for nonlinear time series (with application to exchange rates) , 1995 .

[14]  J. Beckmann.  Nonlinear Exchange Rate Adjustment and the Monetary Model , 2013 .

[15]  Mehdi Khashei,et al.  Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) , 2009, Neurocomputing.

[16]  M. Wohar,et al.  Monetary Fundamentals and Exchange Rate Dynamics Under Different Nominal Regimes , 2003 .

[17]  Kenneth S. Rogoff,et al.  Exchange rate models of the seventies. Do they fit out of sample , 1983 .

[18]  J. Frenkel.  A Monetary Approach To The Exchange Rate: Doctrinal Aspects And Empirical Evidence , 1976 .

[19]  Hong-Ghi Min,et al.  Nonlinear dynamics in exchange rate deviations from the monetary fundamentals: An empirical study , 2010 .

[20]  Guoqiang Peter Zhang,et al.  An investigation of neural networks for linear time-series forecasting , 2001, Comput. Oper. Res..

[21]  J. Junttila,et al.  Nonlinearity and time-variation in the monetary model of exchange rates , 2011 .

[22]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[23]  Albert Sesé,et al.  Designing an artificial neural network for forecasting tourism time series , 2006 .

[24]  Francesco Lisi,et al.  A comparison between neural networks and chaotic models for exchange rate prediction , 1999 .

[25]  I. Moosa,et al.  A reappraisal of the Meese–Rogoff puzzle , 2014 .

[26]  Mark P. Taylor,et al.  Why is it so Difficult to Beat the Random Walk Forecast of Exchange Rates? , 2001 .

[27]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[28]  Ashish Sharma,et al.  An application of artificial neural networks for rainfall forecasting , 2001 .

[29]  William Verkooijen,et al.  A neural network approach to long-run exchange rate prediction , 1996 .

[30]  Gordon Leitch,et al.  Economic Forecast Evaluation: Profits versus the Conventional Error Measures , 1991 .