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首页> 外文期刊>International review of economics & finance >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
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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

机译：石油出口商的外汇市场波动的建模和预测中的结构性断裂和长期记忆：计划内和计划外新闻公告的重要性

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This paper analyzes the dual long memory properties of four major foreign exchange markets of the world oil exporter Saudi Arabia, using the ARF1MA-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.
机译：本文使用ARF1MA-FIGARCH模型在一些全球性事件下分析了世界石油出口国沙特阿拉伯的四个主要外汇市场的双重长期记忆特性。它可以识别计划内和计划外新闻公告以及结构更改对持久性变化的影响。结果表明，几乎没有证据表明条件均值会长期记忆，但对于四种沙特汇率与主要货币的条件波动率，长期记忆会提供强有力的支持。此外，预定的新闻公告对预期和波动率均无重大影响，而非预定的新闻公告则对所有汇率的有条件波动率均具有重大影响。此外，我们发现日元与日元的汇率至少有五个结构性变化，其余汇率则为四个。结构性中断似乎对持久性的变化有更大的影响，并且ARFIMA-FIGARCH模型与计划外新闻公告的虚拟变量以及结构性变化相结合，最适合于检查这些外汇市场在美国的长期记忆过程。 -样品。最后，样本外预测提供的结果参差不齐，并且表明波动率模型的任何规范都不适合分析沙特阿拉伯交易所市场中的LM动态。总体而言，我们的结果对产油国的投资组合经理和政策制定者有影响。

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