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

PUBLIC INTEREST STATEMENT

The exchange rate plays an important role in the development of any economy; therefore, it is important to have an eye on the future trends of the exchange rates in order to avoid the exchange rate risk in economic transactions. In the case of Turkey, we have observed drastic fluctuation in exchange rate within a few hours, which itself is an exception therefore in this study, we have forecasted the exchange rate by combining different approaches which include Artificial Neural Network, NARDL, ARIMA, Naïve, and Exponential Smoothing via equal-weights and variance-covariance approaches. The results will be helpful to forecast the Turkish Lira in the future to avoid the unwanted variation, which affects the returns and lose the confidence of the traders. The research is helpful to the FOREX market, investors, traders, exporters, importers, central banks, and individuals to make their policies accordingly.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Rabia Sabri

Ms. Rabia is a PhD Scholar at PAF-KIET, Karachi and working as a Senior Lecturer at Institute of Business Management. Her research interests include panel data analysis, secondary data and forecasting.

Dr. Abdul Aziz Abdul Rahman is an associate professor at the department of finance and accounting, College of Business, Kingdom University (KU), Bahrain. He has published numerous articles in well-known international journals in accounting, business, and finance.

Dr. Abdelrhman Meero, Associate, Professor, has a Ph.D. Degree in Finance, Bordeaux IV university, Bordeaux, France, 2007. He was the Vice-rector of HIBA (The Higher Institute of Business Administration, Damascus) for 2 years, and he was the Dean of Business College/Kingdom University/Bahrain for five years.

Dr. Liaqat Ali Abro is an advocate in High Court of Pakistan.

Muhammad AsadUllah, PhD Scholar, is the Acting Head of the Department of Department of Professional and Commercial Studies at the Institute of Business Management and a Senior Lecturer.