Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

Abstract

The motivation of this study is built from the previous research to find a way to enhance the forecast of advanced and emerging market currency volatilities. Given the exchange rate's nonlinear and time-varying characteristics, we introduce the neural networks (NN) approach to enhance the Markov Switching Beta-Exponential Generalized Autoregressive Conditional Heteroscedasticity (MS-Beta-t-EGARCH) model. Our hybrid model synthesizes these two approaches' advantages to predict exchange rate volatility. We validate the performance of our proposed model by comparing it with various traditional volatility forecasting models. In-sample and out-of-sample volatility forecasts are considered to achieve our comparison. The empirical results suggest that our hybrid NN-MS Beta-t-EGARCH outperforms the other models for both emerging and advanced market currencies.