Forecasting exchange rates in Sri Lanka: a comparison of the double seasonal autoregressive integrated moving average models (DSARIMA) and SARIMA models
B. R. P. M. Basnayake,
University of Kelaniya, LK
About B. R. P. M.
B. R. P. M. Basnayake is a graduate student with a First-Class in BSc (Honours) degree in Statistics from the University of Kelaniya, currently working as a Lecturer (Probationary) at the Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Sri Lanka. Further, she is Reading an MPhil degree in Statistics in the Faculty of Graduate Studies, University of Kelaniya, Research interests are data science, time series forecasting and statistical modelling.
N. V. Chandrasekara
University of Kelaniya, LK
About N. V.
Dr. N.V. Chandrasekara is a Senior Lecturer at the Department of Statistics and Computer Science, University of Kelaniya, Sri Lanka. She graduated from the University of Colombo, Sri Lanka with a BSc (Hons.) special degree in Statistics with Computer Science in 2009. She obtained a Masters degree in Financial Economics and PhD in Data Mining from the University of Colombo, Sri Lanka in 2011 and 2018 respectively. Her research interests are data mining, deep learning, time series forecasting, econometrics and statistical modelling.
Exchange rates serve as a medium for currency trading in the financial market. The variations and the uncertainty movements in exchange rates have a potential effect on the performance of a country. The objective of this study is to forecast daily exchange rates in Sri Lanka using Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) with Autoregressive Conditional Heteroscedasticity (ARCH)/ Generalized ARCH (GARCH) models. The study collected a few daily exchange rates from the Yahoo finance website in terms of LKR from 1st January 2008 to 28th February 2022. The DSARIMA and SARIMA models were incorporated with the ARCH/ GARCH specifications of normal, skew-normal, student-t and skew-t due to the accurate specification of the proper error distribution led to an increase in the accuracy of the fitted model. The model comparisons were carried out considering different performance measures. The overall results from the actual and fitted graphs and lower error values of the fitted models suggested a SARIMA model for CHF/LKR, a SARIMA model with ARCH/GARCH for USD, EURO, JPY, GBP and AUD against LKR and a DSARIMA model with ARCH/GARCH for CAD and SGD against LKR were suitable to forecast the respective exchange rate. Overall, the results from this study will support government, investors, corporate, financial and managerial sectors in their future decisions to accomplish their objectives. The originality of this study concerns the application of DSARIMA models in exchange rates due to the availability of double seasonality in data.
How to Cite:
Basnayake, B.R.P.M. and Chandrasekara, N.V., 2022. Forecasting exchange rates in Sri Lanka: a comparison of the double seasonal autoregressive integrated moving average models (DSARIMA) and SARIMA models. Journal of Science of the University of Kelaniya, 15(2), pp.192–209. DOI: http://doi.org/10.4038/josuk.v15i2.8067
23 Dec 2022.