Forecasting Renminbi Exchange Rate Volatility Using CARR‐MIDAS Model

Abstract

In this study, we propose to employ the conditional autoregressive range‐mixed‐data sampling (CARR‐MIDAS) model to model and forecast the renminbi exchange rate volatility. The CARR‐MIDAS model exploits intraday information from the intraday high and low prices, which has the capacity to capture the high persistence of conditional range (volatility). The empirical results show that the range‐based CARR‐MIDAS model provides more accurate out‐of‐sample forecasts of the renminbi exchange rate volatility compared to the return‐based GARCH and GARCH‐MIDAS models and the range‐based CARR model for forecast horizons of 1 day up to 3 months. In addition, the superior predictive ability of the CARR‐MIDAS model is robust to different forecast windows. Hence, our CARR‐MIDAS model provides a promising tool for forecasting the renminbi exchange rate volatility.