Forecasting Exchange Rates Comparison In The Presence Of Instabilities

Abstract

Exchange rate forecasting is an inherent approach in financial risk management, yet previous forecasting models were criticized for their poor predictive ability, mainly during periods of exceptional macroeconomic weaknesses. This is attributed to their failure to identify the importance and strength of key transmission and amplification channels, especially those linked to financial markets and uncertainty. Though there is no model that can be precise, especially during periods of crises, it is important to find a model that can yield near-accurate results. The present study therefore evaluates the different forecasting models, considering how each handles instabilities. The Rossi Sekhposyan forecast rationality test results reveal that the EGARCH model under general error distribution and APARCH under normal error distribution show the strongest evidence against rationality around the year 2009, identifying the concentration of instabilities during that time. This vindicates the need to control for instabilities in forecasting. This implies that, in the presence of instabilities, the fluctuation tests are more powerful than traditional tests.