A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting

Abstract

Modeling and forecasting of dynamics nominal exchange rate has long been a focus of financial and economic research. Artificial Intelligence (IA) modeling has recently attracted much attention as a newtechnique in economic and financial forecasting. This paper proposes an alternative approach based on artificial neural network (ANN) to predict the daily exchange rates. Our empirical study is based on a series of daily data in Tunisia. In order to evaluate this approach, we compare it with a generalized autoregressive conditional heteroskedasticity (GARCH) model in terms of their performance. Results indicate that the proposed nonlinear autoregressive (NAR) model is an accurate and a quick prediction method. This finding helps businesses and policymakers to plan more appropriately.

Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

“…In the case of the exchange rate volatility forecasting, Panda and Narasimhan [6] found that Neural Network (NN) has a better exchange rate forecasting performance for not only in-sample but also outof-sample period, compared to the linear regression and random walk models. Other researchers also find a similar result (see, [3], [13], [22]). Although the NN technique has several advantages that distinguish it from the other existing prediction methods, it is a black box learning approach because it cannot interpret the relationship between input and output or deal with uncertainties.…”

Epedimiological Survey on Trypanosomiasis in Dromedary Camels of District Tank Khyber Pakhtunkhwa, Pakistan

“…Results indicated that ANNs model is superior to family of GARCH model when forecasting performance is compared with ANNs model (Dhamija and Bhalla, 2010). Moreover, ANNs model have been compared with available statistical models by (Charef and Ayachi, 2016;Laily et al, 2018). Additionally, Fatima and Uddin (2017a) compared forecasting performance of asymmetric GARCH such as EGARCH and Power Generalized Autoregressive Conditional Heteroscedastic (PGARCH) with ANNs models for KSE-100 and Bombay Stock Exchange Sensex (BSESN).…”

Comparing Forecasts of GARCH (1, 1)-M and GARCH (1, 1)-M-ANNs Model: A Study Based On Stock Market Data

“…Results indicated that ANNs model is superior to family of GARCH model when forecasting performance is compared with ANNs model (Dhamija and Bhalla, 2010). Moreover, ANNs model have been compared with available statistical models by (Charef and Ayachi, 2016;Laily et al, 2018). Additionally, Fatima and Uddin (2017a) compared forecasting performance of asymmetric GARCH such as EGARCH and Power Generalized Autoregressive Conditional Heteroscedastic (PGARCH) with ANNs models for KSE-100 and Bombay Stock Exchange Sensex (BSESN).…”