Forecasting Turkish lira against the US Dollars via forecasting approaches

Abstract

The aim of this study is to predict the Turkish Lira’s exchange rate against the US Dollar by combining models . As a result, the authors include three univariate forecasting models: ARIMA, Naive, and Exponential smoothing, and one multivariate model: NARDL for the first time with Artificial Neural Network model. To the best of our knowledge, it is a unique study to integrate univariate models, ANN with NARDL. The researchers utilize two combination criteria to forecast the Turkish Lira, namely, equal weightage and var-cor. The findings conclude that the combination of NARDL and Naive outperforms all standalone and combined time series techniques. The results indicate that the Turkish Lira’s currency rate against the USD is strongly reliant on recent time-series observations with symmetric and asymmetric behavior of macro-economic fundamentals.

Keywords

• Projection
• exchange rate changes
• auto-regressive
• naïve
• exponential smoothing
• ANN