Forecasting the High-Frequency Exchange Rate Volatility with Smooth Transition Exponential Smoothing

Smooth Transition Exponential Smoothing (STES) is a popular exponential smoothing method for volatility forecasting; whereby the success of STES model lies in choice transition variable. In this paper, three realized variance (RV), daily, weekly and monthly RV were used as variables methods to evaluate performance intraday data. While daily squared return noisy series, residual employed proxy actual volatilities study. With five series exchange rates, comparative analysis was conducted Ad Hoc methods, Generalised Autoregressive Conditional Heteroscedastic (GARCH) models, using various combinations. The empirical results showed that when volatility, traditional models with outperformed GARCH under RMSE evaluation criteria. Similar promising also observed MAE evaluation. MCS generally reaffirmed from both