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.
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