Exchange Rate Forecasting Based on Integration of Gated Recurrent Unit (GRU) and CBOE Volatility Index (VIX)

The foreign exchange market is the most liquid financial market globally, attracting investors looking for lucrative investment opportunities. Despite numerous techniques developed for forecasting foreign exchange trends, accurate and reliable models remain scarce. This article presents a novel approach that combines fundamental and technical analysis to predict exchange rates for the USD-CNY, EUR-USD, and GBP-USD currency pairs. Additionally, we extend the model's architecture by using China CSI300 stock index futures (CIFc1) instead of VIX, LSTM instead of GRU, and adding data pre-processing. The results show that our method is more accurate and stable than other approaches mentioned above, including traditional methods based on fundamental analysis. This study highlights the importance of the idea of combing fundamental information with deep learning, and underscores the effectiveness of integrating technique analysis and fundamental analysis, and lays the groundwork for further extensions and experimentation in foreign exchange forecasting.