An Application Of Exchange Rate Forecasting In Turkey

Akincilar, Aykan; Temız, İzzettin; Şahin, Erol
October 2011
Gazi University Journal of Science;2011, Vol. 24 Issue 4, p817
Academic Journal
In this study, exchange rate forecasting is studied which plays a key role in free market systems. Official daily data of Central Bank of The Republic of Turkey (CBRT) are used for USD/TL ($/TL), EURO/TL (€/TL) and POUND/TL (£/TL) pars. Moving averages (MA) method, single exponential smoothing method, Holt's method, Winter's method and ARIMA models are applied to the each pars, Performance of the models are assessed with the performance criteria of mean absolute percentage error (MAPE), root mean square errors (RMSE) and mean square error (MAE). As a result of study, successfully application of the methods based on trend analysis is exhibited for exchange rates in Turkey. According to MAPE, RMSE and MAE criteria, the best results are obtained by Winter's method which means that Winter's method is the most appropriate method to forecast exchange rates for the given time interval in Turkey.


Related Articles

  • FORECASTING THE TOTAL FERTILITY RATE IN MALAYSIA. Shitan, Mahendran; Yung Lerd Ng // Pakistan Journal of Statistics;Sep2015, Vol. 31 Issue 5, p547 

    It is vital to understand the demographic development of the country as demographic changes would affect all areas of human activity. Forecasting demographic variables is important as demographic trends which are neglected could be discovered and new policies can be implemented before situation...

  • Test for Symmetric and Asymmetric Nonlinear Mean Reversion in Real Exchange Rates. SOLLIS, ROBERT; LEYBOURNE, STEPHEN; NEWBOLD, PAUL // Journal of Money, Credit & Banking (Ohio State University Press);Aug2002 Part 1, Vol. 34 Issue 3, p686 

    New tests, based on smooth transition autoregressive models, for mean reversion in time series of real exchange rates are proposed. One test forces mean reversion to be symmetric about the integrated process central case, while the other permits asymmetry. The tests are applied to monthly series...

  • Forecasting Foreign Exchange Rates Using Fuzzy Time Series. Boiroju, Naveen Kumar; Rao, M. Venugopala; Reddy, M. Krishna // International Journal of Statistics & Systems; 

    In recent years, many methods have been proposed for forecasting foreign exchange rates. An effort is made in this paper to develop fuzzy time series method and an autoregressive integrated moving average (ARIMA) models for daily exchange rate of the Indian rupees against US Dollar. The...

  • Forecasting Exchange Rates with Mixed Models. BADEA (STROIE), Laura Maria // Journal of Mobile, Embedded & Distributed Systems;2013, Vol. 5 Issue 2, p84 

    Gaining accuracy in exchange rate forecasting applications provides true benefits for financial activities. Supported today by the advancements in computing power, machine learning techniques provide good alternatives to traditional time series estimation methods. Very approached in time series...

  • USING ITERATIVE LINEAR REGRESSION MODEL TO TIME SERIES MODELS. Chikr-el-Mezouar, Zouaoui; Attouch, Mohamed // Electronic Journal of Applied Statistical Analysis;2012, Vol. 5 Issue 2, p137 

    This paper presents an iterated liner regression model and compares its forecasting performance with the traditional liner regression (LR) and Box- Jenkins ARIMA models using two well-known time series datasets: airline data and sunspot data. The difference between iterated LR and traditional LR...

  • ISSUES IN FORECASTING INTERNATIONAL TOURIST TRAVEL. Moss, Steven; Liu, Jun; Moss, Janet // Academy of Information & Management Sciences Journal;2013, Vol. 16 Issue 2, p15 

    In this paper two popular time series methods for modeling seasonality in tourism forecasts are compared. The first uses a decomposition methodology to estimate seasonal variation. In this method seasonal variation is estimated with a ratio-to-centered moving average approach. Three different...

  • Time Series Analysis of Household Electric Consumption with ARIMA and ARMA Models. Pasapitch Chujai; Nittaya Kerdprasop; Kittisak Kerdprasop // Proceedings of the International MultiConference of Engineers & ;2013, p1 

    The purposes of this research are to find a model to forecast the electricity consumption in a household and to find the most suitable forecasting period whether it should be in daily, weekly, monthly, or quarterly. The time series data in our study was individual household electric power...

  • Hybrid Empirical Mode Decomposition-ARIMA for Forecasting Exchange Rates. Abadan, Siti Sarah; Shabri, Ani; Ismail, Shuhaida // AIP Conference Proceedings;2015, Vol. 1643 Issue 1, p256 

    This paper studied the forecasting of monthly Malaysian Ringgit (MYR)/ United State Dollar (USD) exchange rates using the hybrid of two methods which are the empirical model decomposition (EMD) and the autoregressive integrated moving average (ARIMA). MYR is pegged to USD during the Asian...

  • A POST SAMPLE TEST FOR A TIME SERVICES MODEL. Bhattacharyya, M. N.; Andersen, A. P. // Australian Journal of Management (University of New South Wales);Apr76, Vol. 1 Issue 1, p33 

    Abstract: A post-sample diagnostic test for judging the temporal stability of the Box-Jenkins time series models has been developed. The proposed test is based on the stochastic properties of the errors of the forecasts, at different leads, made from the same origin. Its application has been...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics