Forecasting exchange rate volatility: An amalgamation approach
研究了金融和宏观经济变量对未来汇率波动的预测能力，采用七种货币对美元汇率，比较线性模型、机器学习等方法，并提出融合预测方法，发现融合方法预测效果更好。
The importance of exchange rate volatility forecasting has both practical and academic merit. Our aim is to provide a comprehensive analysis of the forecasting ability of financial and macroeconomics variables for future exchange rate volatility. We employ seven widely traded currencies against the US dollar and examine linear models and a variety of machine learning, dimensionality reduction and forecast combination approaches, along with creating a grand forecast (amalgamation approach) from these approaches. Our findings highlight the predictive power of the amalgamation approach, as well as the positive contribution of macroeconomic and financial variables in the forecasting experiment. Furthermore, we generate forecasts on the separate frequencies of volatility using wavelet analysis , in order to extract frequency-related information and examine timing effects in the performance of the methods.