Modeling and predicting historical volatility in exchange rate markets

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• 作者：Salim Lahmiri ^{slahmiri@esca.ma}
• 关键词：Historical volatility ; GARCH family ; Exchange rate ; Forecasting ; Neural networks
• 刊名：Physica A: Statistical Mechanics and its Applications
• 出版年：2017
• 出版时间：1 April 2017
• 年：2017
• 卷：471
• 期：Complete
• 页码：387-395
• 全文大小：662 K
• 卷排序：471

文摘

GARCH family processes with different distributional assumptions, and, hybrid GARCH and ANN, and ANN trained with technical analysis indicators are compared in forecasting historical volatility of US exchange rates. ANN with technical analysis indicators performed the best. The proposed approach has the advantage of being assumptions-free, simple to implement, and accurate.