Component‐Driven FX Volatility Prediction: Evidence From USDCNH via GARCH‐MIDAS Models Exploiting Leading Indicators

This study adopts a component‐driven approach to improve FX volatility and value‐at‐risk (VaR) forecasts, with a focus on two types of leading indicators: currency indexes and sovereign spreads. Specifically, we explore the significance of the US dollar index, RMB index, and China–US 10‐year sovereign bond yield spread, as long‐term volatility components in GARCH‐MIDAS models for the USDCNH exchange rate. The investigation reveals that these explanatory variables have a substantial influence on the market's volatility. In terms of the enhanced prediction of volatility and VaR, our analysis presents empirical evidence for the forecasting superiority of the GARCH‐MIDAS models that fully exploit the aforementioned variables and their combinations. Improving upon the traditional method, the optimal GARCH‐MIDAS specification is comparable to or even outperforms the intraday high‐frequency realized volatility model. Our research contributes to a deeper understanding of the influential factors behind USDCNH fluctuations and advances an effective method to accurately forecast volatility and VaR from component‐driven perspectives.