Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: The importance of scheduled and unscheduled news announcements

This paper analyzes the dual long memory properties of four major foreign exchange markets of the world oil exporter Saudi Arabia, using the ARFIMA–FIGARCH model under several global events. It discerns the impacts of both scheduled and unscheduled news announcements and structural changes on changing persistence. The results show little evidence of long memory in the conditional mean but provide strong support for long memory in conditional volatility for the four Saudi exchange rates versus major currencies. Moreover, scheduled news announcements have no significant impact on both expectations and volatility, while unscheduled news announcements demonstrate significant effects on the conditional volatility for all exchange rates. Furthermore, we detect at least five structural changes for the exchange rate with the yen and four for the rest of the exchange rates. The structural breaks seem to have greater impacts on changing persistence, and that the ARFIMA–FIGARCH model coupled with the dummy variables of the unscheduled news announcements and the structural changes is the most suitable for examining the long memory processes of these foreign exchange markets in in-sample. Finally, the out-of-sample forecasts provide mixed results and indicate that none of the specifications of the volatility model is appropriate for analyzing the LM dynamics in the Saudi Arabian exchange market. Overall, our results have implications for portfolio managers and policy makers in oil-producing countries.