Forecasting exchange rates in Sri Lanka: a comparison of the double seasonal autoregressive integrated moving average models (DSARIMA) and SARIMA models

Abstract

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 1 st January 2008 to 28 th 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.